diff --git a/src/FlotationAnalytics/README.md b/src/FlotationAnalytics/README.md
new file mode 100644
index 0000000..a6bea15
--- /dev/null
+++ b/src/FlotationAnalytics/README.md
@@ -0,0 +1,92 @@
+Данная работа представляет автоматизацию анализа флотации. Вы можете загрузить видеозапись, выбрать интересующий трекер и на выходе будет детальный анализ трекинга пузырей.
+=====================
+
+Выполните следующий код в терминале, чтобы создать и перейти в виртуальное окружение:
+
+`python3 -m venv .venv`
+
+`source ./.venv/bin/activate`
+
+Установите все необходимые зависимости:
+
+`pip install -r requirements.txt`
+
+В моем коде используются вызовы следующих моделей: **DeepSORT** и **CounTR**. Их сначала необходимо склонировать себе в проект:
+
+DeepSORT: `git clone https://github.com/nwojke/deep_sort.git`
+
+CounTR: `git clone https://github.com/Verg-Avesta/CounTR.git`
+
+Также в репозитории CounTR нужно внести некоторые изменения, чтобы корректно запустить проект:
+- в файле **CounTR/util/misc.py**:
+
+ закомментировать строчку`from torch._six import inf` и прописать `inf = float('inf')`
+
+- в файле **CounTR/util/pos_embed.py**:
+
+ `omega = np.arange(embed_dim // 2, dtype=np.float)` на `omega = np.arange(embed_dim // 2, dtype=float)`
+
+В файле **CounTR/models_mae_cross.py** нужно написать правильный путь, а именно добавить название папки
+
+`from CounTR.models_crossvit import CrossAttentionBlock`
+
+`from CounTR.util.pos_embed import get_2d_sincos_pos_embed`
+
+
+Скачайте веса и добавьте в папку **model**:
+
+https://drive.google.com/file/d/1CzYyiYqLshMdqJ9ZPFJyIzXBa7uFUIYZ/view?usp=sharing
+
+В данной работе используются следующие существующие методы трекинга:
+- SORT
+- DeepSORT
+- Bot-SORT
+- ByteTrack
+
+Для детекции объектов ограничивающими рамками:
+- YOLOv11
+
+Для детекции центров объектов:
+- CounTR
+- PseCO
+
+### Запуск основного сервиса:
+
+`PYTHONPATH=абсолютный_путь_до_проекта HYDRA_FULL_ERROR=1 python app/main.py tracker=botsort output_dir="resuts_end"`,
+
+где tracker - выбор трекера, output_dir - папка для сохранения результатов
+
+### Запуск тестов:
+
+`PYTHONPATH=абсолютный_путь_до_проекта pytest tests/test_metrics.py -v`
+
+Также представлены скрипты, которые не связаны с сервисом, но позволяют провести анализ.
+-----------------------------------
+
+**Конвертация из YOLO формата в CVAT аннотацию:** `fromYOLOtoCVAT.py`
+
+**Подсчет контролируемых метрик: MOTP и MOTA.** А также визуализация сопоставлений истинных ограничивающих рамок с предсказанными лежит в `metrics.py`
+
+В коде используются `cvat_annotations.xml` и `output_botsort`.
+
+`cvat_annotations.xml` - разметка в формате CVAT
+
+`output_botsort` - разметка в расширенном формате YOLO
+
+Код, который по сегментации делает разметку, представлен в `fromMaskToYolo.py`.
+
+Запустить код можно с помощью команды:
+
+`!python fromMaskToYolo.py --image_folder '/path/to/images' --mask_folder '/path/to/masks' --output_folder '/path/to/output' --class_id 0`, где
+
+- **image_folder** - путь к папке с изображениями,
+
+- **mask_folder** - путь к папке с масками,
+
+- **output_folder** - путь для сохранения YOLO разметки
+
+### Проведенное исследование
+
+Для оценки качества работы разных методов использовался Google Colab. Код из него был экспортирован в формате `.py` и находится в папке **research**.
+
+Он включает в себя обучение детектора, запуск моделей, замер FPS, подсчет среднего перемещения и других метрик. Также в нем реализована визуализация трекинга.
diff --git a/src/FlotationAnalytics/app/classes/__init__.py b/src/FlotationAnalytics/app/classes/__init__.py
new file mode 100644
index 0000000..87b5c42
--- /dev/null
+++ b/src/FlotationAnalytics/app/classes/__init__.py
@@ -0,0 +1,4 @@
+from .deepSortTracker import DeepSortTracker
+from .trackingQualityAnalyzer import TrackingQualityAnalyzer
+from .objectTracker import ObjectTracker
+from .videoTracker import VideoTracker
\ No newline at end of file
diff --git a/src/FlotationAnalytics/app/classes/deepSortTracker.py b/src/FlotationAnalytics/app/classes/deepSortTracker.py
new file mode 100644
index 0000000..aa22995
--- /dev/null
+++ b/src/FlotationAnalytics/app/classes/deepSortTracker.py
@@ -0,0 +1,54 @@
+from deep_sort.application_util import preprocessing
+from deep_sort.deep_sort import nn_matching
+from deep_sort.deep_sort.detection import Detection
+from deep_sort.deep_sort.tracker import Tracker
+import numpy as np
+
+
+class DeepSortTracker:
+ def __init__(
+ self,
+ img_size,
+ nms_max_overlap=0.6,
+ max_cosine_distance=0.5,
+ nn_budget=None,
+ max_age=30,
+ min_hits=3,
+ iou_threshold=0.3,
+ ):
+ self.img_size = img_size
+ self.nms_max_overlap = nms_max_overlap
+ self.iou_threshold = iou_threshold
+ metric = nn_matching.NearestNeighborDistanceMetric(
+ "cosine", max_cosine_distance, nn_budget
+ )
+ self.tracker = Tracker(metric, max_age=max_age, n_init=min_hits)
+
+ def prepare_detections(self, yolo_detections):
+ detections = []
+ for det in yolo_detections:
+ x1, y1, x2, y2, conf, _ = det
+ bbox = (x1, y1, x2 - x1, y2 - y1) # Конвертация в формат (x,y,w,h)
+ feature = []
+ detections.append(Detection(bbox, conf, feature))
+ return detections
+
+ def update(self, yolo_detections):
+ detections = self.prepare_detections(yolo_detections)
+ boxes = np.array([d.tlwh for d in detections])
+ scores = np.array([d.confidence for d in detections])
+ indices = preprocessing.non_max_suppression(boxes, self.nms_max_overlap, scores)
+ detections = [detections[i] for i in indices]
+
+ self.tracker.predict()
+ self.tracker.update(detections)
+
+ results = []
+ for track in self.tracker.tracks:
+ print("track", track, track.is_confirmed(), track.time_since_update)
+ if not track.is_confirmed() or track.time_since_update > 1:
+ continue
+ bbox = track.to_tlwh()
+ results.append([track.track_id, bbox[0], bbox[1], bbox[2], bbox[3]])
+
+ return results
diff --git a/src/FlotationAnalytics/app/classes/objectTracker.py b/src/FlotationAnalytics/app/classes/objectTracker.py
new file mode 100644
index 0000000..d70b7c9
--- /dev/null
+++ b/src/FlotationAnalytics/app/classes/objectTracker.py
@@ -0,0 +1,157 @@
+import numpy as np
+from .trackingQualityAnalyzer import TrackingQualityAnalyzer
+from .utils import get_center
+
+MAX_DISAPPEARED = 3
+MAX_POSITIONS_HISTORY = 5
+IOU_DIST_WEIGHTS = (0.4, 0.6)
+MIN_OBJECT_SIZE = 5
+MIN_DISTANCE = 15
+MAX_COST_THRESHOLD = 0.6
+BORDER_DISAPPEAR_MULTIPLIER = 2
+
+
+class ObjectTracker:
+ def __init__(self, max_disappeared=1, frame_size=(1920, 1080)):
+ self.next_id = 1
+ self.objects = {}
+ self.max_disappeared = max_disappeared
+ self.frame_width, self.frame_height = frame_size
+ self.metrics = TrackingQualityAnalyzer()
+
+ def update(self, detections, current_frame=None):
+ for obj_id in self.objects:
+ if len(self.objects[obj_id]["positions"]) > 1:
+ last_pos = self.objects[obj_id]["positions"][-1]
+ prev_pos = self.objects[obj_id]["positions"][-2]
+ velocity = last_pos - prev_pos
+ predicted_pos = last_pos + velocity
+ self.objects[obj_id]["predicted_pos"] = predicted_pos
+ else:
+ self.objects[obj_id]["predicted_pos"] = get_center(
+ self.objects[obj_id]["bbox"]
+ )
+
+ for obj_id in list(self.objects.keys()):
+ bbox = self.objects[obj_id]["bbox"]
+ if (
+ bbox[0] <= 5
+ or bbox[1] <= 5
+ or bbox[2] >= self.frame_width - 5
+ or bbox[3] >= self.frame_height - 5
+ ):
+ self.objects[obj_id]["disappeared"] += BORDER_DISAPPEAR_MULTIPLIER
+ else:
+ self.objects[obj_id]["disappeared"] += 1
+
+ if self.objects[obj_id]["disappeared"] > self.max_disappeared:
+ del self.objects[obj_id]
+
+ if len(detections) == 0:
+ return self._prepare_output(current_frame)
+
+ if len(self.objects) == 0:
+ for det in detections:
+ det_center = get_center(det)
+ self.objects[self.next_id] = {
+ "bbox": det,
+ "disappeared": 0,
+ "positions": [det_center],
+ "predicted_pos": det_center,
+ }
+ self.next_id += 1
+ return self._prepare_output(current_frame)
+
+ obj_ids = list(self.objects.keys())
+ obj_bboxes = [self.objects[obj_id]["bbox"] for obj_id in obj_ids]
+
+ cost_matrix = np.zeros((len(obj_bboxes), len(detections)))
+ for i, obj_bbox in enumerate(obj_bboxes):
+ obj_predicted = self.objects[obj_ids[i]]["predicted_pos"]
+ for j, det_bbox in enumerate(detections):
+ det_center = get_center(det_bbox)
+ iou_score = 1 - self.calculate_iou(obj_bbox, det_bbox)
+ dist_score = np.linalg.norm(obj_predicted - det_center) / 100
+ cost_matrix[i, j] = (
+ IOU_DIST_WEIGHTS[0] * iou_score + IOU_DIST_WEIGHTS[1] * dist_score
+ )
+
+ matched_obj_indices = set()
+ matched_det_indices = set()
+
+ while True:
+ min_cost = np.min(cost_matrix)
+ if min_cost > MAX_COST_THRESHOLD:
+ break
+
+ i, j = np.unravel_index(np.argmin(cost_matrix), cost_matrix.shape)
+ obj_id = obj_ids[i]
+
+ self.objects[obj_id]["bbox"] = detections[j]
+ self.objects[obj_id]["disappeared"] = 0
+ det_center = get_center(detections[j])
+ self.objects[obj_id]["positions"].append(det_center)
+ if len(self.objects[obj_id]["positions"]) > MAX_POSITIONS_HISTORY:
+ self.objects[obj_id]["positions"].pop(0)
+
+ matched_obj_indices.add(i)
+ matched_det_indices.add(j)
+
+ cost_matrix[i, :] = float("inf")
+ cost_matrix[:, j] = float("inf")
+
+ for j in set(range(len(detections))) - matched_det_indices:
+ det = detections[j]
+ det_center = get_center(det)
+
+ width = det[2] - det[0]
+ height = det[3] - det[1]
+ if width < MIN_OBJECT_SIZE or height < MIN_OBJECT_SIZE:
+ continue
+
+ too_close = False
+ for obj_id in self.objects:
+ obj_center = get_center(self.objects[obj_id]["bbox"])
+ distance = np.linalg.norm(obj_center - det_center)
+ if distance < MIN_DISTANCE:
+ too_close = True
+ break
+
+ if not too_close:
+ self.objects[self.next_id] = {
+ "bbox": det,
+ "disappeared": 0,
+ "positions": [det_center],
+ "predicted_pos": det_center,
+ }
+ self.next_id += 1
+
+ return self._prepare_output(current_frame)
+
+ @staticmethod
+ def calculate_iou(bbox1, bbox2):
+ x1 = max(bbox1[0], bbox2[0])
+ y1 = max(bbox1[1], bbox2[1])
+ x2 = min(bbox1[2], bbox2[2])
+ y2 = min(bbox1[3], bbox2[3])
+
+ inter_area = max(0, x2 - x1) * max(0, y2 - y1)
+ if inter_area == 0:
+ return 0.0
+
+ bbox1_area = (bbox1[2] - bbox1[0]) * (bbox1[3] - bbox1[1])
+ bbox2_area = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1])
+
+ return inter_area / float(bbox1_area + bbox2_area - inter_area)
+
+ def _prepare_output(self, current_frame):
+ current_tracks = [
+ [obj_id, obj["bbox"][0], obj["bbox"][1], obj["bbox"][2], obj["bbox"][3]]
+ for obj_id, obj in self.objects.items()
+ ]
+
+ if current_frame is not None:
+ self.metrics.update_metrics(
+ len(self.metrics.metrics["frame"]), current_tracks, current_frame
+ )
+ return {obj_id: obj["bbox"] for obj_id, obj in self.objects.items()}
diff --git a/src/FlotationAnalytics/app/classes/trackingQualityAnalyzer.py b/src/FlotationAnalytics/app/classes/trackingQualityAnalyzer.py
new file mode 100644
index 0000000..15b74ae
--- /dev/null
+++ b/src/FlotationAnalytics/app/classes/trackingQualityAnalyzer.py
@@ -0,0 +1,238 @@
+import cv2
+import numpy as np
+from scipy.special import expit
+import matplotlib.pyplot as plt
+import streamlit as st
+
+
+class TrackingQualityAnalyzer:
+ def __init__(self):
+ self.metrics = {
+ "frame": [],
+ "displacement": [],
+ "coverage": [],
+ "optical_flow": [],
+ "temporal_consistency": [],
+ "track_lengths": {},
+ "active_tracks": [],
+ }
+ self.prev_tracks = {}
+ self.prev_frame = None
+
+ def update_metrics(self, frame_num, current_tracks, current_frame):
+ current_dict = {}
+ for track in current_tracks:
+ if len(track) >= 5:
+ track_id = track[0]
+ bbox = track[1:5]
+ current_dict[track_id] = bbox
+
+ for track_id in current_dict:
+ self.metrics["track_lengths"][track_id] = (
+ self.metrics["track_lengths"].get(track_id, 0) + 1
+ )
+
+ if not self.prev_tracks:
+ self.prev_tracks = current_dict
+ self.prev_frame = current_frame.copy()
+ return
+
+ matched = 0
+ total_displacement = 0
+ total_iou = 0
+ total_flow = 0
+
+ if self.prev_frame is not None:
+ prev_gray = cv2.cvtColor(self.prev_frame, cv2.COLOR_BGR2GRAY)
+ curr_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
+ flow = cv2.calcOpticalFlowFarneback(
+ prev_gray, curr_gray, None, 0.5, 3, 15, 3, 5, 1.2, 0
+ )
+
+ for track_id, current_bbox in current_dict.items():
+ if track_id in self.prev_tracks:
+ matched += 1
+ prev_bbox = self.prev_tracks[track_id]
+
+ # Среднее смещение
+ dx = current_bbox[0] - prev_bbox[0]
+ dy = current_bbox[1] - prev_bbox[1]
+ displacement = np.sqrt(dx**2 + dy**2)
+ total_displacement += displacement
+
+ # IoU для темпоральной согласованности
+ iou = self.calculate_iou(prev_bbox, current_bbox)
+ total_iou += iou
+
+ # Анализ оптического потока в области объекта
+ if self.prev_frame is not None:
+ x, y, w, h = map(int, prev_bbox)
+ x, y = max(0, x), max(0, y)
+ w, h = min(w, current_frame.shape[1] - x), min(
+ h, current_frame.shape[0] - y
+ )
+ if w > 0 and h > 0:
+ obj_flow = flow[y : y + h, x : x + w]
+ if obj_flow.size > 0:
+ magnitude = np.sqrt(
+ obj_flow[..., 0] ** 2 + obj_flow[..., 1] ** 2
+ )
+ total_flow += np.mean(magnitude)
+
+ # Полнота обнаружения
+ coverage = matched / len(self.prev_tracks) if len(self.prev_tracks) > 0 else 0
+
+ self.metrics["frame"].append(frame_num)
+ self.metrics["displacement"].append(
+ total_displacement / matched if matched > 0 else 0
+ )
+ self.metrics["coverage"].append(coverage)
+ self.metrics["temporal_consistency"].append(
+ total_iou / matched if matched > 0 else 0
+ )
+ self.metrics["optical_flow"].append(total_flow / matched if matched > 0 else 0)
+ self.metrics["active_tracks"].append(len(current_dict))
+
+ self.prev_tracks = current_dict
+ self.prev_frame = current_frame.copy()
+
+ @staticmethod
+ def calculate_iou(box1, box2):
+ x1, y1, w1, h1 = box1
+ x2, y2, w2, h2 = box2
+
+ xi1 = max(x1, x2)
+ yi1 = max(y1, y2)
+ xi2 = min(x1 + w1, x2 + w2)
+ yi2 = min(y1 + h1, y2 + h2)
+
+ inter_area = max(xi2 - xi1, 0) * max(yi2 - yi1, 0)
+ box1_area = w1 * h1
+ box2_area = w2 * h2
+ union_area = box1_area + box2_area - inter_area
+
+ return inter_area / union_area if union_area > 0 else 0
+
+ def get_final_metrics(self):
+ if not self.metrics["frame"]:
+ return {}
+
+ avg_metrics = {
+ "avg_displacement": np.mean(self.metrics["displacement"]),
+ "avg_coverage": np.mean(self.metrics["coverage"]),
+ "avg_temporal_consistency": np.mean(self.metrics["temporal_consistency"]),
+ "avg_optical_flow": np.mean(self.metrics["optical_flow"]),
+ "track_length_mean": (
+ np.mean(list(self.metrics["track_lengths"].values()))
+ if self.metrics["track_lengths"]
+ else 0
+ ),
+ "track_length_median": (
+ np.median(list(self.metrics["track_lengths"].values()))
+ if self.metrics["track_lengths"]
+ else 0
+ ),
+ "max_active_tracks": (
+ max(self.metrics["active_tracks"])
+ if self.metrics["active_tracks"]
+ else 0
+ ),
+ }
+ return avg_metrics
+
+ def get_tracking_score(self, weights=None, normalize=True, reference_score=22.5190):
+ final_metrics = self.get_final_metrics()
+ print("New metrics keys:", final_metrics.keys())
+ if not final_metrics:
+ print("Предупреждение: нет метрик для расчета score!")
+ return 0.0
+ print(final_metrics)
+ default_weights = {
+ "avg_displacement": -0.2,
+ "avg_coverage": 0.35,
+ "avg_temporal_consistency": 0.25,
+ "avg_optical_flow": -0.1,
+ "track_length_mean": 0.2,
+ "max_active_tracks": 0.1,
+ }
+
+ weights = weights if weights is not None else default_weights
+
+ missing_metrics = [k for k in weights if k not in final_metrics]
+
+ if missing_metrics:
+ print(
+ f"Предупреждение: отсутствуют метрики {missing_metrics}, они не учитываются в score"
+ )
+
+ raw_score = 0.0
+ for key, weight in weights.items():
+ if key in final_metrics:
+ raw_score += float(final_metrics[key]) * weight
+
+ if normalize:
+ if reference_score <= 0:
+ print("Ошибка: reference_score должен быть положительным!")
+ return 0.0
+
+ normalized_score = (raw_score / reference_score) - 1
+
+ return (expit(normalized_score)) * 2
+ else:
+ return raw_score
+
+ def generate_metrics_plots(self, save_path=None):
+ if not self.metrics["frame"]:
+ return None
+
+ plt.figure(figsize=(16, 12))
+
+ plt.subplot(3, 2, 1)
+ plt.plot(self.metrics["frame"], self.metrics["displacement"], color="blue")
+ plt.title("Динамика смещения объектов")
+ plt.grid(True)
+
+ plt.subplot(3, 2, 2)
+ plt.plot(self.metrics["frame"], self.metrics["coverage"], color="green")
+ plt.title("Полнота трекинга")
+ plt.grid(True)
+
+ plt.subplot(3, 2, 3)
+ plt.plot(self.metrics["frame"], self.metrics["displacement"])
+ plt.plot(self.metrics["frame"], self.metrics["coverage"])
+ plt.title("Качество трекинга")
+ plt.grid(True)
+
+ plt.subplot(3, 2, 4)
+ plt.plot(
+ self.metrics["frame"], self.metrics["temporal_consistency"], color="red"
+ )
+ plt.title("Темпоральная согласованность")
+ plt.grid(True)
+
+ plt.subplot(3, 2, 5)
+ plt.plot(self.metrics["frame"], self.metrics["optical_flow"], color="purple")
+ plt.title("Оптический поток объектов")
+ plt.grid(True)
+
+ plt.subplot(3, 2, 6)
+ plt.plot(self.metrics["frame"], self.metrics["active_tracks"], color="green")
+ plt.title("Активные треки")
+ plt.grid(True)
+
+ plt.tight_layout()
+
+ if save_path:
+ plt.savefig(save_path)
+
+ return plt.gcf()
+
+ def plot_metrics(self, save_path=None):
+ fig = self.generate_metrics_plots(save_path)
+ if fig is None:
+ st.warning("Нет данных для построения графиков!")
+ return
+
+ st.subheader("Графики метрик")
+ st.pyplot(fig)
+ plt.close(fig)
diff --git a/src/FlotationAnalytics/app/classes/utils.py b/src/FlotationAnalytics/app/classes/utils.py
new file mode 100644
index 0000000..62b663a
--- /dev/null
+++ b/src/FlotationAnalytics/app/classes/utils.py
@@ -0,0 +1,5 @@
+import numpy as np
+
+
+def get_center(bbox):
+ return np.array([(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2])
diff --git a/src/FlotationAnalytics/app/classes/videoTracker.py b/src/FlotationAnalytics/app/classes/videoTracker.py
new file mode 100644
index 0000000..6279f26
--- /dev/null
+++ b/src/FlotationAnalytics/app/classes/videoTracker.py
@@ -0,0 +1,270 @@
+import streamlit as st
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import time
+import torch
+from CounTR import models_mae_cross
+from PIL import Image
+from torchvision import transforms
+from .deepSortTracker import DeepSortTracker
+from .trackingQualityAnalyzer import TrackingQualityAnalyzer
+from .objectTracker import ObjectTracker
+
+MAX_DISAPPEARED = 3
+NMS_RADIUS = 12
+DENSITY_THRESHOLD = 0.05
+SHOT_NUM = 0
+MODEL_SIZE = 384
+BBOX_SIZE = 10
+
+
+class VideoTracker:
+ def __init__(self, model_path, tracker_type):
+ self.tracker_type = tracker_type
+
+ if tracker_type == "Мой трекер + CounTR":
+ self.model = models_mae_cross.__dict__["mae_vit_base_patch16"](
+ norm_pix_loss="store_true"
+ )
+ checkpoint = torch.load(model_path, map_location="cpu", weights_only=False)
+ self.model.load_state_dict(checkpoint["model"], strict=False)
+ self.custom_tracker = ObjectTracker(max_disappeared=MAX_DISAPPEARED)
+ self.is_yolo_model = False
+ else:
+ self.model = YOLO(model_path)
+ self.is_yolo_model = True
+
+ self.quality_analyzer = TrackingQualityAnalyzer()
+ self.processed_frames = []
+
+ if tracker_type == "DeepSORT + YOLOv11s":
+ self.deep_sort_tracker = DeepSortTracker(img_size=(640, 480))
+
+ def _parse_tracks(self, results):
+ tracks = []
+ if self.tracker_type == "DeepSORT + YOLOv11s":
+ if results[0].boxes.data is not None:
+ detections = results[0].boxes.data.cpu().numpy()
+ track_results = self.deep_sort_tracker.update(detections)
+ tracks = [[int(t[0]), t[1], t[2], t[3], t[4]] for t in track_results]
+ elif self.tracker_type == "Мой трекер + CounTR":
+ if isinstance(results, dict):
+ tracks = []
+ for obj_id, obj_data in results.items():
+ bbox = obj_data["bbox"]
+ if isinstance(bbox, (list, tuple, np.ndarray)) and len(bbox) >= 4:
+ tracks.append([obj_id, bbox[0], bbox[1], bbox[2], bbox[3]])
+ return tracks
+ else:
+ if hasattr(results[0], "boxes") and results[0].boxes.id is not None:
+ boxes = results[0].boxes.xyxy.cpu().numpy()
+ ids = results[0].boxes.id.cpu().numpy().astype(int)
+ tracks = [
+ [
+ ids[i],
+ boxes[i][0],
+ boxes[i][1],
+ boxes[i][2] - boxes[i][0],
+ boxes[i][3] - boxes[i][1],
+ ]
+ for i in range(len(boxes))
+ ]
+ return tracks
+
+ def _draw_detections(self, frame, tracks):
+ for track in tracks:
+ track_id, x, y, w, h = track
+ x1, y1, x2, y2 = int(x), int(y), int(x + w), int(y + h)
+ cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+ cv2.putText(
+ frame,
+ str(track_id),
+ (x1, y1 - 5),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 0.5,
+ (0, 255, 255),
+ 1,
+ )
+ return frame
+
+ def non_max_suppression(self, points, scores, radius=NMS_RADIUS):
+ sorted_indices = np.argsort(scores)[::-1]
+ keep = []
+
+ while sorted_indices.size > 0:
+ i = sorted_indices[0]
+ keep.append(i)
+
+ dists = np.sqrt(
+ (points[i, 0] - points[sorted_indices[1:], 0]) ** 2
+ + (points[i, 1] - points[sorted_indices[1:], 1]) ** 2
+ )
+
+ to_remove = np.where(dists < radius)[0] + 1
+ sorted_indices = np.delete(sorted_indices, [0] + list(to_remove))
+
+ return points[keep]
+
+ def process_frame(self, frame, model, old_w, old_h, tracker=None):
+ if tracker is None:
+ tracker = ObjectTracker(max_disappeared=MAX_DISAPPEARED)
+
+ frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+ pil_img = Image.fromarray(frame_rgb)
+
+ w, h = pil_img.size
+ ratio = min(MODEL_SIZE / w, MODEL_SIZE / h)
+ new_w, new_h = int(w * ratio), int(h * ratio)
+ pil_img = pil_img.resize((new_w, new_h), Image.BILINEAR)
+
+ padded_img = Image.new("RGB", (MODEL_SIZE, MODEL_SIZE), (0, 0, 0))
+ pad_x = (MODEL_SIZE - new_w) // 2
+ pad_y = (MODEL_SIZE - new_h) // 2
+ padded_img.paste(pil_img, (pad_x, pad_y))
+
+ transform = transforms.Compose([transforms.ToTensor()])
+ img_tensor = transform(padded_img).unsqueeze(0)
+ boxes = torch.zeros(1, 0, 4)
+
+ with torch.no_grad():
+ density_map = model(img_tensor, boxes, SHOT_NUM)[0].squeeze(0).cpu().numpy()
+
+ y, x = np.where(density_map > DENSITY_THRESHOLD * density_map.max())
+ scores = density_map[y, x]
+ points = np.column_stack((x, y))
+
+ filtered_points = (
+ self.non_max_suppression(points, scores) if len(points) > 0 else []
+ )
+
+ current_detections = []
+ for x, y in filtered_points:
+ orig_x = int((x - pad_x) * old_w / new_w)
+ orig_y = int((y - pad_y) * old_h / new_h)
+
+ x1 = max(0, orig_x - BBOX_SIZE)
+ y1 = max(0, orig_y - BBOX_SIZE)
+ x2 = min(old_w - 1, orig_x + BBOX_SIZE)
+ y2 = min(old_h - 1, orig_y + BBOX_SIZE)
+ current_detections.append((x1, y1, x2, y2))
+
+ tracked_objects = tracker.update(current_detections, frame)
+
+ for obj_id, bbox in tracked_objects.items():
+ x1, y1, x2, y2 = bbox
+ cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+ cv2.putText(
+ frame,
+ str(obj_id),
+ (x1, y1 - 5),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 0.5,
+ (0, 255, 255),
+ 1,
+ )
+
+ cv2.putText(
+ frame,
+ f"Total: {len(tracked_objects)}",
+ (10, 30),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 0.7,
+ (0, 0, 255),
+ 2,
+ )
+
+ return frame, len(tracked_objects), tracker
+
+ def process_video(self, video_path):
+ cap = cv2.VideoCapture(video_path)
+ self.processed_frames = []
+
+ if self.tracker_type == "DeepSORT + YOLOv11s":
+ frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+ frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+ self.deep_sort_tracker = DeepSortTracker(
+ img_size=(frame_width, frame_height)
+ )
+
+ progress_bar = st.progress(0)
+ total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+ frame_num = 0
+ start_time = time.time()
+
+ while cap.isOpened():
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ if self.tracker_type == "Мой трекер + CounTR":
+ processed_frame, count, tracker = self.process_frame(
+ frame,
+ self.model,
+ frame.shape[1],
+ frame.shape[0],
+ self.custom_tracker,
+ )
+ current_tracks = self._parse_tracks(tracker.objects)
+ frame_with_detections = processed_frame
+
+ self.quality_analyzer.update_metrics(frame_num, current_tracks, frame)
+ else:
+ if self.tracker_type == "DeepSORT + YOLOv11s":
+ results = self.model(frame, conf=0.3, iou=0.5, max_det=400)
+ current_tracks = self._parse_tracks(results)
+ else:
+ results = self.model.track(
+ frame,
+ tracker=self.tracker_type.lower().replace(
+ " + yolov11s", ".yaml"
+ ),
+ persist=True,
+ conf=0.3,
+ iou=0.5,
+ max_det=400,
+ )
+ current_tracks = self._parse_tracks(results)
+
+ frame_with_detections = self._draw_detections(
+ frame.copy(), current_tracks
+ )
+ self.quality_analyzer.update_metrics(frame_num, current_tracks, frame)
+
+ self.processed_frames.append(
+ {
+ "frame": frame_with_detections,
+ "tracks": current_tracks,
+ "bubbles_count": len(current_tracks),
+ }
+ )
+
+ progress_bar.progress(cap.get(cv2.CAP_PROP_POS_FRAMES) / total_frames)
+ frame_num += 1
+
+ processing_time = time.time() - start_time
+ cap.release()
+
+ metrics = self.quality_analyzer.get_final_metrics()
+ metrics["final_score"] = self.quality_analyzer.get_tracking_score()
+
+ metrics.update(
+ {
+ "bubbles_per_frame": [
+ f["bubbles_count"] for f in self.processed_frames
+ ],
+ "max_active_tracks_history": self.quality_analyzer.metrics[
+ "active_tracks"
+ ],
+ "track_lengths": self.quality_analyzer.metrics["track_lengths"],
+ "displacement": self.quality_analyzer.metrics["displacement"],
+ "coverage": self.quality_analyzer.metrics["coverage"],
+ "temporal_consistency": self.quality_analyzer.metrics[
+ "temporal_consistency"
+ ],
+ "optical_flow": self.quality_analyzer.metrics["optical_flow"],
+ "processing_time": processing_time,
+ }
+ )
+
+ return metrics
diff --git a/src/FlotationAnalytics/app/conf/config.yaml b/src/FlotationAnalytics/app/conf/config.yaml
new file mode 100644
index 0000000..0db1aed
--- /dev/null
+++ b/src/FlotationAnalytics/app/conf/config.yaml
@@ -0,0 +1,8 @@
+defaults:
+ - _self_
+ - tracker: bytetrack
+
+video_path: "video/video1.mp4"
+output_dir: "output_results"
+save_frames: true
+num_sample_frames: 5
\ No newline at end of file
diff --git a/src/FlotationAnalytics/app/conf/tracker/botsort.yaml b/src/FlotationAnalytics/app/conf/tracker/botsort.yaml
new file mode 100644
index 0000000..d76148e
--- /dev/null
+++ b/src/FlotationAnalytics/app/conf/tracker/botsort.yaml
@@ -0,0 +1,2 @@
+name: "BoT-SORT + YOLOv11s"
+config_file: "bot-sort.yaml"
diff --git a/src/FlotationAnalytics/app/conf/tracker/bytetrack.yaml b/src/FlotationAnalytics/app/conf/tracker/bytetrack.yaml
new file mode 100644
index 0000000..00b279b
--- /dev/null
+++ b/src/FlotationAnalytics/app/conf/tracker/bytetrack.yaml
@@ -0,0 +1,2 @@
+name: "ByteTrack + YOLOv11s"
+config_file: "bytetrack.yaml"
diff --git a/src/FlotationAnalytics/app/conf/tracker/deepsort.yaml b/src/FlotationAnalytics/app/conf/tracker/deepsort.yaml
new file mode 100644
index 0000000..71a6480
--- /dev/null
+++ b/src/FlotationAnalytics/app/conf/tracker/deepsort.yaml
@@ -0,0 +1,2 @@
+name: "DeepSORT + YOLOv11s"
+config_file: none
diff --git a/src/FlotationAnalytics/app/conf/tracker/my_tracker_and_CounTR.yaml b/src/FlotationAnalytics/app/conf/tracker/my_tracker_and_CounTR.yaml
new file mode 100644
index 0000000..28ff73a
--- /dev/null
+++ b/src/FlotationAnalytics/app/conf/tracker/my_tracker_and_CounTR.yaml
@@ -0,0 +1,2 @@
+name: "Мой трекер + CounTR"
+config_file: none
\ No newline at end of file
diff --git a/src/FlotationAnalytics/app/main.py b/src/FlotationAnalytics/app/main.py
new file mode 100644
index 0000000..805261a
--- /dev/null
+++ b/src/FlotationAnalytics/app/main.py
@@ -0,0 +1,177 @@
+from dataclasses import dataclass
+from classes import VideoTracker
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from omegaconf import DictConfig
+import hydra
+from hydra.core.config_store import ConfigStore
+import torch
+from CounTR import models_mae_cross
+
+
+@dataclass
+class TrackerConfig:
+ name: str
+ config_file: str
+
+
+@dataclass
+class FlotationAnalysisConfig:
+ video_path: str
+ tracker: TrackerConfig
+ output_dir: str = "output"
+ save_frames: bool = True
+ num_sample_frames: int = 5
+
+
+cs = ConfigStore.instance()
+cs.store(name="flotation_config", node=FlotationAnalysisConfig)
+
+
+@hydra.main(config_path="conf", config_name="config", version_base=None)
+def main(cfg: DictConfig) -> None:
+ os.makedirs(cfg.output_dir, exist_ok=True)
+
+ tracker = initialize_tracker(cfg.tracker)
+
+ print(f"Starting video processing: {cfg.video_path}")
+ metrics = process_video_with_tracker(cfg.video_path, tracker)
+ save_results(metrics, cfg.output_dir, cfg.save_frames, cfg.num_sample_frames)
+ visualize_results(metrics, cfg.output_dir)
+
+ print(f"Analysis completed. Results saved to: {os.path.abspath(cfg.output_dir)}")
+
+
+def initialize_tracker(tracker_cfg: DictConfig):
+ if tracker_cfg.name == "Мой трекер + CounTR":
+ model_path = "model/FSC147.pth"
+ model = models_mae_cross.__dict__["mae_vit_base_patch16"](
+ norm_pix_loss="store_true"
+ )
+ checkpoint = torch.load(model_path, map_location="cpu", weights_only=False)
+ model.load_state_dict(checkpoint["model"], strict=False)
+ tracker = VideoTracker(model_path, tracker_cfg.name)
+ tracker.model = model
+ else:
+ tracker = VideoTracker("model/YOLOv11s.pt", tracker_cfg.name)
+ return tracker
+
+
+def process_video_with_tracker(video_path: str, tracker) -> dict:
+ metrics = tracker.process_video(video_path)
+ return metrics
+
+
+def plot_metrics(metrics, output_dir):
+ if "max_active_tracks_history" in metrics and metrics["max_active_tracks_history"]:
+ fig, ax = plt.subplots(figsize=(12, 4))
+ ax.plot(metrics["max_active_tracks_history"], "c-", label="Активные треки")
+ ax.axhline(
+ y=metrics["max_active_tracks"],
+ color="r",
+ linestyle="--",
+ label=f'Максимум: {metrics["max_active_tracks"]}',
+ )
+ ax.set_xlabel("Номер кадра")
+ ax.set_ylabel("Количество треков")
+ ax.grid(True)
+ ax.legend()
+ plt.savefig(os.path.join(output_dir, "active_tracks.png"))
+ plt.close()
+
+ if "optical_flow" in metrics and metrics["optical_flow"]:
+ fig, ax = plt.subplots(figsize=(12, 4))
+ ax.plot(metrics["optical_flow"], "m-", label="Оптический поток")
+ ax.set_xlabel("Номер кадра")
+ ax.set_ylabel("Величина потока (пиксели)")
+ ax.grid(True)
+ ax.legend()
+ plt.savefig(os.path.join(output_dir, "optical_flow.png"))
+ plt.close()
+
+ if "track_lengths" in metrics and metrics["track_lengths"]:
+ fig, ax = plt.subplots(figsize=(12, 4))
+
+ lengths = metrics["track_lengths"]
+ if isinstance(lengths, dict):
+ lengths = list(lengths.values())
+ elif not isinstance(lengths, (list, np.ndarray)):
+ lengths = []
+
+ if lengths:
+ ax.hist(lengths, bins=20, color="orange", edgecolor="black", alpha=0.7)
+ mean_length = np.mean(lengths)
+ ax.axvline(
+ mean_length,
+ color="r",
+ linestyle="--",
+ label=f"Среднее: {mean_length:.1f} кадров",
+ )
+ ax.set_xlabel("Длина трека (кадры)")
+ ax.set_ylabel("Количество треков")
+ ax.grid(True)
+ ax.legend()
+ plt.savefig(os.path.join(output_dir, "track_lengths.png"))
+ plt.close()
+
+ if "displacement" in metrics and len(metrics["displacement"]) > 0:
+ fig, ax = plt.subplots(figsize=(12, 4))
+ ax.plot(metrics["displacement"], "r-", label="Смещение (пиксели)")
+ ax.set_xlabel("Номер кадра")
+ ax.set_ylabel("Смещение")
+ ax.grid(True)
+ ax.legend()
+ plt.savefig(os.path.join(output_dir, "displacement.png"))
+ plt.close()
+
+ if "coverage" in metrics and len(metrics["coverage"]) > 0:
+ fig, ax = plt.subplots(figsize=(12, 4))
+ ax.plot(metrics["coverage"], "g-", label="Процент совпадений")
+ ax.set_xlabel("Номер кадра")
+ ax.set_ylabel("Процент")
+ ax.grid(True)
+ ax.legend()
+ plt.savefig(os.path.join(output_dir, "coverage.png"))
+ plt.close()
+
+ if "temporal_consistency" in metrics and len(metrics["temporal_consistency"]) > 0:
+ fig, ax = plt.subplots(figsize=(12, 4))
+ ax.plot(metrics["temporal_consistency"], "m-", label="Средний IoU")
+ ax.set_xlabel("Номер кадра")
+ ax.set_ylabel("IoU")
+ ax.grid(True)
+ ax.legend()
+ plt.savefig(os.path.join(output_dir, "temporal_consistency.png"))
+ plt.close()
+
+ if "bubbles_per_frame" in metrics and metrics["bubbles_per_frame"]:
+ fig, ax = plt.subplots(figsize=(12, 4))
+ ax.plot(metrics["bubbles_per_frame"], "b-", label="Количество пузырей")
+ ax.set_xlabel("Номер кадра")
+ ax.set_ylabel("Количество пузырей")
+ ax.grid(True)
+ ax.legend()
+ plt.savefig(os.path.join(output_dir, "bubbles_per_frame.png"))
+ plt.close()
+
+
+def save_results(
+ metrics: dict, output_dir: str, save_frames: bool = True, num_sample_frames: int = 5
+) -> None:
+ plot_metrics(metrics, output_dir)
+
+
+def visualize_results(metrics: dict, output_dir: str) -> None:
+ print("\n=== Summary Metrics ===")
+ for key, value in metrics.items():
+ if isinstance(value, (list, np.ndarray)):
+ print(f"{key}: {len(value)} elements")
+ elif isinstance(value, dict):
+ print(f"{key}: {len(value)} items")
+ else:
+ print(f"{key}: {value}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/FlotationAnalytics/bot-sort.yaml b/src/FlotationAnalytics/bot-sort.yaml
new file mode 100644
index 0000000..83f670a
--- /dev/null
+++ b/src/FlotationAnalytics/bot-sort.yaml
@@ -0,0 +1,22 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Default Ultralytics settings for BoT-SORT tracker when using mode="track"
+# For documentation and examples see https://docs.ultralytics.com/modes/track/
+# For BoT-SORT source code see https://github.com/NirAharon/BoT-SORT
+
+tracker_type: botsort # tracker type, ['botsort', 'bytetrack']
+track_high_thresh: 0.3 # threshold for the first association
+track_low_thresh: 0.1 # threshold for the second association
+new_track_thresh: 0.25 # threshold for init new track if the detection does not match any tracks
+track_buffer: 200 # buffer to calculate the time when to remove tracks
+match_thresh: 1.0 # threshold for matching tracks
+fuse_score: True # Whether to fuse confidence scores with the iou distances before matching
+# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
+
+# BoT-SORT settings
+gmc_method: sparseOptFlow # method of global motion compensation
+# ReID model related thresh
+proximity_thresh: 0.5 # minimum IoU for valid match with ReID
+appearance_thresh: 0.8 # minimum appearance similarity for ReID
+with_reid: False
+model: auto # uses native features if detector is YOLO else yolo11n-cls.pt
diff --git a/src/FlotationAnalytics/bytetrack.yaml b/src/FlotationAnalytics/bytetrack.yaml
new file mode 100644
index 0000000..ea45eea
--- /dev/null
+++ b/src/FlotationAnalytics/bytetrack.yaml
@@ -0,0 +1,14 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Default Ultralytics settings for ByteTrack tracker when using mode="track"
+# For documentation and examples see https://docs.ultralytics.com/modes/track/
+# For ByteTrack source code see https://github.com/ifzhang/ByteTrack
+
+tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack']
+track_high_thresh: 0.3 # threshold for the first association
+track_low_thresh: 0.1 # threshold for the second association
+new_track_thresh: 0.25 # threshold for init new track if the detection does not match any tracks
+track_buffer: 200 # buffer to calculate the time when to remove tracks
+match_thresh: 1.0 # threshold for matching tracks
+fuse_score: True # Whether to fuse confidence scores with the iou distances before matching
+# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
\ No newline at end of file
diff --git a/src/FlotationAnalytics/cvat_annotations.xml b/src/FlotationAnalytics/cvat_annotations.xml
new file mode 100644
index 0000000..877d868
--- /dev/null
+++ b/src/FlotationAnalytics/cvat_annotations.xml
@@ -0,0 +1,25230 @@
+
+
+ 1.1
+
+
+ 2207020
+ 100
+ interpolation
+ 5
+
+ 2025-03-14 13:31:29.587588+00:00
+ 2025-03-26 23:19:03.889501+00:00
+ default
+ 0
+ 99
+
+
+
+ 2209437
+ 0
+ 99
+ https://app.cvat.ai/api/jobs/1
+
+
+
+ m
+ m@gmail.com
+
+
+
+
+
+
+ 2025-04-24 19:53:36.982180+00:00
+
+ 800
+ 600
+
+
+
+
+
+
+
+
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diff --git a/src/FlotationAnalytics/fromMaskToYolo.py b/src/FlotationAnalytics/fromMaskToYolo.py
new file mode 100644
index 0000000..6b41a23
--- /dev/null
+++ b/src/FlotationAnalytics/fromMaskToYolo.py
@@ -0,0 +1,87 @@
+import cv2
+import os
+import numpy as np
+import argparse
+
+# Извлечение bbox из маски
+def extract_bounding_boxes(mask):
+ contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+ boxes = []
+ for cnt in contours:
+ x, y, w, h = cv2.boundingRect(cnt)
+ boxes.append([x, y, x+w, y+h])
+ return boxes
+
+# Конвертация bbox в YOLO формат
+def convert_bbox_to_yolo(box, image_width, image_height):
+ x1, y1, x2, y2 = box
+ x_center = (x1 + x2) / 2 / image_width
+ y_center = (y1 + y2) / 2 / image_height
+ width = (x2 - x1) / image_width
+ height = (y2 - y1) / image_height
+ return x_center, y_center, width, height
+
+def process_images_to_yolo_labels(image_folder, mask_folder, output_folder, class_id=0):
+ os.makedirs(output_folder, exist_ok=True)
+
+ for image_name in os.listdir(image_folder):
+ if not image_name.lower().endswith('.jpg'):
+ continue
+
+ base_name = os.path.splitext(image_name)[0]
+ image_path = os.path.join(image_folder, image_name)
+ mask_path = os.path.join(mask_folder, f"{base_name}.png")
+ txt_path = os.path.join(output_folder, f"{base_name}.txt")
+
+ if not os.path.exists(mask_path):
+ print(f"Маска для {image_name} не найдена")
+ continue
+
+ image = cv2.imread(image_path)
+ mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+
+ if image is None:
+ print(f"Ошибка при загрузке изображения: {image_path}")
+ continue
+ if mask is None:
+ print(f"Ошибка при загрузке маски: {mask_path}")
+ continue
+
+ boxes = extract_bounding_boxes(mask)
+ if not boxes:
+ print(f"Не найдено объектов на изображении {image_name}")
+ continue
+
+ height, width = image.shape[:2]
+ with open(txt_path, 'w') as f:
+ for box in boxes:
+ yolo_coords = convert_bbox_to_yolo(box, width, height)
+ f.write(f"{class_id} {' '.join(f'{x:.6f}' for x in yolo_coords)}\n")
+
+ print(f"Разметка для {image_name} сохранена в {txt_path}")
+
+def main():
+ parser = argparse.ArgumentParser(
+ description='Конвертация масок в YOLO формат разметки'
+ )
+
+ parser.add_argument('--image_folder', required=True,
+ help='Путь к папке с изображениями')
+ parser.add_argument('--mask_folder', required=True,
+ help='Путь к папке с масками')
+ parser.add_argument('--output_folder', required=True,
+ help='Путь для сохранения YOLO разметки')
+ parser.add_argument('--class_id', type=int, default=0,
+ help='ID класса для разметки (по умолчанию: 0)')
+
+ args = parser.parse_args()
+
+ process_images_to_yolo_labels(
+ image_folder=args.image_folder,
+ mask_folder=args.mask_folder,
+ output_folder=args.output_folder,
+ class_id=args.class_id
+ )
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/src/FlotationAnalytics/fromYOLOtoCVAT.py b/src/FlotationAnalytics/fromYOLOtoCVAT.py
new file mode 100644
index 0000000..82ba9d4
--- /dev/null
+++ b/src/FlotationAnalytics/fromYOLOtoCVAT.py
@@ -0,0 +1,154 @@
+import os
+import csv
+import xml.etree.ElementTree as ET
+from xml.dom import minidom
+
+class DeepSortToCvatConverter:
+ def __init__(self, input_folder, output_xml_file, img_width=None, img_height=None):
+ self.input_folder = input_folder
+ self.output_xml_file = output_xml_file
+ self.img_width = img_width
+ self.img_height = img_height
+
+ def _create_metadata(self, annotations, num_frames):
+ ET.SubElement(annotations, "version").text = "1.1"
+
+ meta = ET.SubElement(annotations, "meta")
+ job = ET.SubElement(meta, "job")
+
+ ET.SubElement(job, "id").text = ""
+ ET.SubElement(job, "size").text = str(num_frames)
+ ET.SubElement(job, "mode").text = "annotation"
+ ET.SubElement(job, "overlap").text = "0"
+ ET.SubElement(job, "bugtracker")
+ ET.SubElement(job, "created").text = "2025-04-18 13:40:56.502816+00:00"
+ ET.SubElement(job, "updated").text = "2025-04-18 14:18:23.946959+00:00"
+ ET.SubElement(job, "subset").text = "default"
+ ET.SubElement(job, "start_frame").text = "0"
+ ET.SubElement(job, "stop_frame").text = str(num_frames - 1)
+ ET.SubElement(job, "frame_filter")
+
+ segments = ET.SubElement(job, "segments")
+ segment = ET.SubElement(segments, "segment")
+ ET.SubElement(segment, "id").text = ""
+ ET.SubElement(segment, "start").text = "0"
+ ET.SubElement(segment, "stop").text = str(num_frames - 1)
+ ET.SubElement(segment, "url").text = ""
+
+ owner = ET.SubElement(job, "owner")
+ ET.SubElement(owner, "username").text = ""
+ ET.SubElement(owner, "email").text = ""
+
+ ET.SubElement(job, "assignee")
+
+ labels = ET.SubElement(job, "labels")
+ label = ET.SubElement(labels, "label")
+ ET.SubElement(label, "name").text = "bubble"
+ ET.SubElement(label, "color").text = "#66ff66"
+ ET.SubElement(label, "type").text = "any"
+ ET.SubElement(label, "attributes")
+
+ ET.SubElement(meta, "dumped").text = "2025-04-18 14:18:39.991110+00:00"
+
+ def _process_tracks(self, annotations, txt_files):
+ tracks = {}
+ track_frames = {}
+ active_tracks = set()
+
+ for frame_num, filename in enumerate(txt_files):
+ filepath = os.path.join(self.input_folder, filename)
+ current_frame_tracks = set()
+
+ with open(filepath, 'r') as f:
+ reader = csv.reader(f)
+ for row in reader:
+ if len(row) < 5:
+ continue
+
+ track_id = row[0]
+ current_frame_tracks.add(track_id)
+
+ x_center = float(row[1])
+ y_center = float(row[2])
+ width = float(row[3])
+ height = float(row[4])
+
+ if self.img_width and self.img_height and max(x_center, y_center, width, height) <= 1.0:
+ x_center *= self.img_width
+ y_center *= self.img_height
+ width *= self.img_width
+ height *= self.img_height
+
+ xtl = x_center
+ ytl = y_center
+ xbr = x_center + width
+ ybr = y_center + height
+
+ if self.img_width:
+ xtl = max(0, min(xtl, self.img_width))
+ xbr = max(0, min(xbr, self.img_width))
+ if self.img_height:
+ ytl = max(0, min(ytl, self.img_height))
+ ybr = max(0, min(ybr, self.img_height))
+
+ xtl, ytl, xbr, ybr = round(xtl, 2), round(ytl, 2), round(xbr, 2), round(ybr, 2)
+
+ if track_id not in tracks:
+ track_elem = ET.SubElement(annotations, "track", {
+ "id": track_id,
+ "label": "bubble",
+ "source": "manual"
+ })
+ tracks[track_id] = track_elem
+ track_frames[track_id] = set()
+
+ if frame_num not in track_frames[track_id]:
+ ET.SubElement(tracks[track_id], "box", {
+ "frame": str(frame_num),
+ "keyframe": "1",
+ "outside": "0",
+ "occluded": "0",
+ "xtl": str(xtl),
+ "ytl": str(ytl),
+ "xbr": str(xbr),
+ "ybr": str(ybr),
+ "z_order": "0"
+ })
+ track_frames[track_id].add(frame_num)
+
+ disappeared_tracks = active_tracks - current_frame_tracks
+ for track_id in disappeared_tracks:
+ if track_id in tracks:
+ ET.SubElement(tracks[track_id], "box", {
+ "frame": str(frame_num),
+ "keyframe": "1",
+ "outside": "1",
+ "occluded": "0",
+ "xtl": "0",
+ "ytl": "0",
+ "xbr": "0",
+ "ybr": "0",
+ "z_order": "0"
+ })
+ track_frames[track_id].add(frame_num)
+
+ active_tracks = current_frame_tracks
+
+ def convert(self):
+ txt_files = sorted(f for f in os.listdir(self.input_folder) if f.endswith('.txt'))
+ if not txt_files:
+ raise ValueError(f"No .txt files found in {self.input_folder}")
+
+ annotations = ET.Element("annotations")
+
+ self._create_metadata(annotations, len(txt_files))
+
+ self._process_tracks(annotations, txt_files)
+
+ xml_str = ET.tostring(annotations, encoding='utf-8')
+ pretty_xml = minidom.parseString(xml_str).toprettyxml(indent=" ")
+
+ with open(self.output_xml_file, 'w', encoding='utf-8') as f:
+ f.write(pretty_xml)
+
+ print(f"Successfully converted {len(txt_files)} frames to CVAT XML at {self.output_xml_file}")
diff --git a/src/FlotationAnalytics/metrics.py b/src/FlotationAnalytics/metrics.py
new file mode 100644
index 0000000..38231f8
--- /dev/null
+++ b/src/FlotationAnalytics/metrics.py
@@ -0,0 +1,308 @@
+# Подсчет контролируемых метрик: MOTP и MOTA
+import numpy as np
+import cv2
+from scipy.optimize import linear_sum_assignment
+import xml.etree.ElementTree as ET
+from pathlib import Path
+import re
+import shutil
+
+
+# Переименовываем файлы с image0.txt в image{i}.txt
+def rename_yolo_annotations(root_dir):
+ root_path = Path(root_dir)
+ for labels_dir in root_path.glob("track*/labels"):
+ track_num = labels_dir.parent.name.replace("track", "")
+ if track_num == "":
+ track_num = 0
+ else:
+ track_num = int(track_num) - 1
+
+ for txt_file in labels_dir.glob("image0.txt"):
+ new_name = f"image{track_num}.txt"
+ new_path = txt_file.with_name(new_name)
+ shutil.move(str(txt_file), str(new_path))
+ print(f"Renamed: {txt_file} -> {new_path}")
+
+
+# Парсим разметки из текстовых файлов и возвращаем словарь с разметкой
+# Для CVAT аннотации
+def parse_cvat_xml(xml_path):
+ tree = ET.parse(xml_path)
+ root = tree.getroot()
+
+ width = int(root.find("meta/original_size/width").text)
+ height = int(root.find("meta/original_size/height").text)
+
+ annotations = {}
+
+ for track in root.findall("track"):
+ track_id = int(track.get("id"))
+
+ for box in track.findall("box"):
+ frame = int(box.get("frame"))
+ xtl = float(box.get("xtl"))
+ ytl = float(box.get("ytl"))
+ xbr = float(box.get("xbr"))
+ ybr = float(box.get("ybr"))
+ box_width = xbr - xtl
+ box_height = ybr - ytl
+ x_center = xtl + box_width / 2
+ y_center = ytl + box_height / 2
+
+ if width and height:
+ x_center /= width
+ y_center /= height
+ box_width /= width
+ box_height /= height
+
+ x_center = max(0, min(x_center, 1.0))
+ y_center = max(0, min(y_center, 1.0))
+ box_width = max(0, min(box_width, 1.0))
+ box_height = max(0, min(box_height, 1.0))
+
+ if frame not in annotations:
+ annotations[frame] = []
+
+ annotations[frame].append(
+ {
+ "track_id": int(track_id) + 1,
+ "bbox": [x_center, y_center, box_width, box_height],
+ }
+ )
+
+ return annotations
+
+
+# Для расширенной YOLO аннотации
+def parse_yolo_txt(yolo_dir):
+ annotations = {}
+
+ yolo_dir = Path(yolo_dir)
+ labels_dirs = list(yolo_dir.glob("*/labels"))
+
+ for labels_dir in labels_dirs:
+ for txt_file in labels_dir.glob("*.txt"):
+ match = re.search(r"image(\d+).txt", txt_file.name)
+ if not match:
+ continue
+
+ frame = int(match.group(1))
+
+ with open(txt_file, "r") as f:
+ lines = f.readlines()
+
+ if frame not in annotations:
+ annotations[frame] = []
+
+ for line in lines:
+ parts = line.strip().split()
+ if len(parts) >= 5:
+ x_center = float(parts[1])
+ y_center = float(parts[2])
+ width = float(parts[3])
+ height = float(parts[4])
+ conf = float(parts[5])
+ track_id = float(parts[6])
+
+ annotations[frame].append(
+ {
+ "track_id": int(track_id),
+ "bbox": [x_center, y_center, width, height],
+ "confidence": conf,
+ }
+ )
+ return annotations
+
+
+def calculate_iou(bbox1, bbox2):
+ try:
+ x1, y1, w1, h1 = map(float, bbox1)
+ x2, y2, w2, h2 = map(float, bbox2)
+ except (TypeError, ValueError) as e:
+ print(f"Ошибка формата bbox: {e}")
+ return 0.0
+
+ w1, h1 = max(0, w1), max(0, h1)
+ w2, h2 = max(0, w2), max(0, h2)
+
+ inter_x1 = max(x1 - w1 / 2, x2 - w2 / 2)
+ inter_y1 = max(y1 - h1 / 2, y2 - h2 / 2)
+ inter_x2 = min(x1 + w1 / 2, x2 + w2 / 2)
+ inter_y2 = min(y1 + h1 / 2, y2 + h2 / 2)
+
+ inter_area = max(0, inter_x2 - inter_x1) * max(0, inter_y2 - inter_y1)
+ union_area = w1 * h1 + w2 * h2 - inter_area + 1e-6
+ return inter_area / union_area
+
+
+# Оптимальное сопоставление с использованием венгерского алгоритма
+def hungarian_matching(cvat_boxes, yolo_boxes, iou_threshold):
+ cost_matrix = 1 - np.array(
+ [[calculate_iou(c["bbox"], y["bbox"]) for y in yolo_boxes] for c in cvat_boxes]
+ )
+ row_ind, col_ind = linear_sum_assignment(cost_matrix)
+
+ matches = []
+ for r, c in zip(row_ind, col_ind):
+ if 1 - cost_matrix[r, c] >= iou_threshold:
+ matches.append((r, c))
+
+ return matches
+
+
+# Сопоставление ограничивающих рамок
+def hybrid_matcher(cvat_boxes, yolo_boxes, iou_threshold, dist_threshold=0):
+ # Венгерский алгоритм (сопоставление по IoU)
+ matches = hungarian_matching(cvat_boxes, yolo_boxes, iou_threshold)
+
+ # Сопоставление оставшихся ограничивающих рамок, которые не нашли пары на предыдущем шаге, по расстоянию
+ matched_cvat = set(m[0] for m in matches)
+ matched_yolo = set(m[1] for m in matches)
+
+ for i, c_box in enumerate(cvat_boxes):
+ if i in matched_cvat:
+ continue
+
+ min_dist = dist_threshold
+ best_j = -1
+ c_pos = np.array(c_box["bbox"][:2])
+
+ for j, y_box in enumerate(yolo_boxes):
+ if j in matched_yolo:
+ continue
+
+ y_pos = np.array(y_box["bbox"][:2])
+ dist = np.linalg.norm(c_pos - y_pos)
+
+ if dist < min_dist:
+ min_dist = dist
+ best_j = j
+
+ if best_j != -1:
+ matches.append((i, best_j))
+ matched_yolo.add(best_j)
+
+ unmatched_cvat = [i for i in range(len(cvat_boxes)) if i not in matched_cvat]
+ unmatched_yolo = [j for j in range(len(yolo_boxes)) if j not in matched_yolo]
+
+ return matches, unmatched_cvat, unmatched_yolo
+
+
+# Фильтрация ложных срабатываний
+def filter_bboxes(bboxes, min_size=0, min_conf=0):
+ return [
+ b
+ for b in bboxes
+ if (b["bbox"][2] * b["bbox"][3] >= min_size)
+ and b.get("confidence", 1.0) >= min_conf
+ ]
+
+
+def draw_comparison(
+ cvat_boxes, yolo_boxes, matches, img_size=(1058, 793), filename="comparison.jpg"
+):
+ img = np.zeros((img_size[0], img_size[1], 3), dtype=np.uint8)
+
+ # Цвета в формате BGR (для OpenCV)
+ COLOR_GT = (0, 200, 0)
+ COLOR_YOLO = (0, 100, 255)
+ COLOR_MATCH = (255, 255, 0)
+ COLOR_BG = (251, 251, 251)
+
+ # Заливаем фон
+ img[:] = COLOR_BG
+
+ for box in cvat_boxes:
+ x, y, w, h = (np.array(box["bbox"]) * img_size[1]).astype(int)
+ cv2.rectangle(
+ img, (x - w // 2, y - h // 2), (x + w // 2, y + h // 2), (0, 0, 0), 3
+ )
+ cv2.rectangle(
+ img, (x - w // 2, y - h // 2), (x + w // 2, y + h // 2), COLOR_GT, 2
+ )
+
+ for box in yolo_boxes:
+ x, y, w, h = (np.array(box["bbox"]) * img_size[1]).astype(int)
+ cv2.rectangle(
+ img, (x - w // 2, y - h // 2), (x + w // 2, y + h // 2), (255, 255, 255), 2
+ )
+ cv2.rectangle(
+ img, (x - w // 2, y - h // 2), (x + w // 2, y + h // 2), COLOR_YOLO, 2
+ )
+
+ for c_idx, y_idx in matches:
+ c_pos = (np.array(cvat_boxes[c_idx]["bbox"][:2]) * img_size[1]).astype(int)
+ y_pos = (np.array(yolo_boxes[y_idx]["bbox"][:2]) * img_size[1]).astype(int)
+ cv2.line(img, tuple(c_pos), tuple(y_pos), COLOR_MATCH, 2)
+
+ legend = [("GT", COLOR_GT), ("YOLO", COLOR_YOLO), ("Matches", COLOR_MATCH)]
+
+ for i, (text, color) in enumerate(legend):
+ cv2.putText(
+ img, text, (20, 30 + i * 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2
+ )
+
+ cv2.imwrite(filename, img)
+
+
+def calculate_metrics(cvat_frames, yolo_frames, iou_threshold, visualize=False):
+ total_gt = total_fp = total_fn = total_iou = total_matches = 0
+
+ for frame_idx in range(len(cvat_frames)):
+ cvat_boxes = [
+ {"bbox": obj["bbox"], "track_id": obj.get("track_id", -1)}
+ for obj in cvat_frames[frame_idx]
+ ]
+
+ yolo_boxes_raw = [
+ {
+ "bbox": obj["bbox"],
+ "track_id": obj.get("track_id", -1),
+ "confidence": obj.get("confidence", 1.0),
+ }
+ for obj in yolo_frames[frame_idx]
+ ]
+
+ yolo_boxes = filter_bboxes(yolo_boxes_raw)
+
+ matches, unmatched_cvat, unmatched_yolo = hybrid_matcher(
+ cvat_boxes, yolo_boxes, iou_threshold
+ )
+
+ if visualize and frame_idx < 5:
+ draw_comparison(
+ cvat_boxes, yolo_boxes, matches, filename=f"match_{frame_idx}.jpg"
+ )
+
+ total_gt += len(cvat_boxes)
+ total_fp += len(unmatched_yolo)
+ total_fn += len(unmatched_cvat)
+ total_matches += len(matches)
+
+ for c_idx, y_idx in matches:
+ total_iou += calculate_iou(
+ cvat_boxes[c_idx]["bbox"], yolo_boxes[y_idx]["bbox"]
+ )
+
+ mota = 1 - (total_fp + total_fn) / max(1, total_gt)
+ motp = total_iou / max(1, total_matches) if total_matches > 0 else 0
+ precision = total_matches / max(1, total_matches + total_fp)
+ recall = total_matches / max(1, total_gt)
+ print("\nFinal Metrics:")
+ print(f"GT: {total_gt}, TP: {total_matches}, FP: {total_fp}, FN: {total_fn}")
+ print(f"MOTA: {mota:.4f}, MOTP: {motp:.4f}")
+ print(f"Precision: {precision:.4f}, Recall: {recall:.4f}")
+ print(f"Average IoU: {total_iou/max(1,total_matches):.4f}")
+ return mota, motp
+
+
+if __name__ == "__main__":
+ rename_yolo_annotations("output_botsort") # при необходимости
+ cvat_xml_path = "cvat_annotations.xml"
+ yolo_dir = "output_botsort"
+ cvat_anns = parse_cvat_xml(cvat_xml_path)
+ yolo_anns = parse_yolo_txt(yolo_dir)
+ mota, motp = calculate_metrics(
+ cvat_anns, yolo_anns, iou_threshold=0.3, visualize=True
+ )
diff --git a/src/FlotationAnalytics/output_botsort/track/labels/image0.txt b/src/FlotationAnalytics/output_botsort/track/labels/image0.txt
new file mode 100644
index 0000000..0d40f10
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track/labels/image0.txt
@@ -0,0 +1,133 @@
+0 0.896754 0.45491 0.0895153 0.193536 0.817078 1
+0 0.289141 0.10215 0.0801068 0.10906 0.816025 2
+0 0.664282 0.0693557 0.0177255 0.0483879 0.773646 3
+0 0.455541 0.554755 0.073381 0.185833 0.763832 4
+0 0.966265 0.474289 0.0668696 0.192599 0.763344 5
+0 0.468511 0.0452729 0.0608767 0.0900122 0.748392 6
+0 0.750538 0.3403 0.0193058 0.0539683 0.746722 7
+0 0.384154 0.121826 0.0701 0.1094 0.732105 8
+0 0.46868 0.222474 0.0198626 0.0615289 0.724078 9
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+0 0.851495 0.668705 0.0223278 0.0533259 0.71843 12
+0 0.696179 0.153456 0.101562 0.199884 0.71727 13
+0 0.636923 0.11886 0.0216502 0.0646397 0.711572 14
+0 0.725866 0.916035 0.0310473 0.0837824 0.709694 15
+0 0.601798 0.286305 0.0842881 0.145424 0.708349 16
+0 0.337922 0.222893 0.0193506 0.066105 0.698228 17
+0 0.76714 0.284007 0.0220984 0.0607878 0.693366 18
+0 0.559816 0.457324 0.0176983 0.0489334 0.690419 19
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diff --git a/src/FlotationAnalytics/output_botsort/track10/labels/image9.txt b/src/FlotationAnalytics/output_botsort/track10/labels/image9.txt
new file mode 100644
index 0000000..f3efb6c
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track10/labels/image9.txt
@@ -0,0 +1,111 @@
+0 0.822761 0.64635 0.12491 0.197881 0.828829 1
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diff --git a/src/FlotationAnalytics/output_botsort/track11/labels/image10.txt b/src/FlotationAnalytics/output_botsort/track11/labels/image10.txt
new file mode 100644
index 0000000..6f6acf6
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track11/labels/image10.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track12/labels/image11.txt b/src/FlotationAnalytics/output_botsort/track12/labels/image11.txt
new file mode 100644
index 0000000..d59a05f
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track12/labels/image11.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track13/labels/image12.txt b/src/FlotationAnalytics/output_botsort/track13/labels/image12.txt
new file mode 100644
index 0000000..cccb17d
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track13/labels/image12.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track14/labels/image13.txt b/src/FlotationAnalytics/output_botsort/track14/labels/image13.txt
new file mode 100644
index 0000000..1739ca1
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track14/labels/image13.txt
@@ -0,0 +1,120 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track15/labels/image14.txt b/src/FlotationAnalytics/output_botsort/track15/labels/image14.txt
new file mode 100644
index 0000000..893c5b1
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track15/labels/image14.txt
@@ -0,0 +1,123 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track16/labels/image15.txt b/src/FlotationAnalytics/output_botsort/track16/labels/image15.txt
new file mode 100644
index 0000000..57747ee
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track16/labels/image15.txt
@@ -0,0 +1,134 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track17/labels/image16.txt b/src/FlotationAnalytics/output_botsort/track17/labels/image16.txt
new file mode 100644
index 0000000..a6b7ac4
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track17/labels/image16.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track18/labels/image17.txt b/src/FlotationAnalytics/output_botsort/track18/labels/image17.txt
new file mode 100644
index 0000000..0b9a051
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track18/labels/image17.txt
@@ -0,0 +1,165 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track19/labels/image18.txt b/src/FlotationAnalytics/output_botsort/track19/labels/image18.txt
new file mode 100644
index 0000000..2126488
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track19/labels/image18.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track2/labels/image1.txt b/src/FlotationAnalytics/output_botsort/track2/labels/image1.txt
new file mode 100644
index 0000000..29b918b
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track2/labels/image1.txt
@@ -0,0 +1,98 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track20/labels/image19.txt b/src/FlotationAnalytics/output_botsort/track20/labels/image19.txt
new file mode 100644
index 0000000..45d39d7
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track20/labels/image19.txt
@@ -0,0 +1,181 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track21/labels/image20.txt b/src/FlotationAnalytics/output_botsort/track21/labels/image20.txt
new file mode 100644
index 0000000..98a477e
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track21/labels/image20.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track22/labels/image21.txt b/src/FlotationAnalytics/output_botsort/track22/labels/image21.txt
new file mode 100644
index 0000000..ffc078f
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track22/labels/image21.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track23/labels/image22.txt b/src/FlotationAnalytics/output_botsort/track23/labels/image22.txt
new file mode 100644
index 0000000..48c2295
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track23/labels/image22.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track24/labels/image23.txt b/src/FlotationAnalytics/output_botsort/track24/labels/image23.txt
new file mode 100644
index 0000000..3608177
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track24/labels/image23.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track25/labels/image24.txt b/src/FlotationAnalytics/output_botsort/track25/labels/image24.txt
new file mode 100644
index 0000000..7f8dcc5
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track25/labels/image24.txt
@@ -0,0 +1,181 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track26/labels/image25.txt b/src/FlotationAnalytics/output_botsort/track26/labels/image25.txt
new file mode 100644
index 0000000..5b2edde
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track26/labels/image25.txt
@@ -0,0 +1,181 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track27/labels/image26.txt b/src/FlotationAnalytics/output_botsort/track27/labels/image26.txt
new file mode 100644
index 0000000..8c31049
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track27/labels/image26.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track28/labels/image27.txt b/src/FlotationAnalytics/output_botsort/track28/labels/image27.txt
new file mode 100644
index 0000000..7937101
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track28/labels/image27.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track29/labels/image28.txt b/src/FlotationAnalytics/output_botsort/track29/labels/image28.txt
new file mode 100644
index 0000000..a28499d
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track29/labels/image28.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track3/labels/image2.txt b/src/FlotationAnalytics/output_botsort/track3/labels/image2.txt
new file mode 100644
index 0000000..575eed9
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track3/labels/image2.txt
@@ -0,0 +1,96 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track30/labels/image29.txt b/src/FlotationAnalytics/output_botsort/track30/labels/image29.txt
new file mode 100644
index 0000000..66b8f8f
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track30/labels/image29.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track31/labels/image30.txt b/src/FlotationAnalytics/output_botsort/track31/labels/image30.txt
new file mode 100644
index 0000000..fbe925d
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track31/labels/image30.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track32/labels/image31.txt b/src/FlotationAnalytics/output_botsort/track32/labels/image31.txt
new file mode 100644
index 0000000..24c5698
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track32/labels/image31.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track33/labels/image32.txt b/src/FlotationAnalytics/output_botsort/track33/labels/image32.txt
new file mode 100644
index 0000000..c9b7fb2
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track33/labels/image32.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track34/labels/image33.txt b/src/FlotationAnalytics/output_botsort/track34/labels/image33.txt
new file mode 100644
index 0000000..17341ec
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track34/labels/image33.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track35/labels/image34.txt b/src/FlotationAnalytics/output_botsort/track35/labels/image34.txt
new file mode 100644
index 0000000..6874985
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track35/labels/image34.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track36/labels/image35.txt b/src/FlotationAnalytics/output_botsort/track36/labels/image35.txt
new file mode 100644
index 0000000..7f248da
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track36/labels/image35.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track37/labels/image36.txt b/src/FlotationAnalytics/output_botsort/track37/labels/image36.txt
new file mode 100644
index 0000000..85aa64a
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track37/labels/image36.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track38/labels/image37.txt b/src/FlotationAnalytics/output_botsort/track38/labels/image37.txt
new file mode 100644
index 0000000..e1fd023
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track38/labels/image37.txt
@@ -0,0 +1,172 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track39/labels/image38.txt b/src/FlotationAnalytics/output_botsort/track39/labels/image38.txt
new file mode 100644
index 0000000..f7b3189
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track39/labels/image38.txt
@@ -0,0 +1,168 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track4/labels/image3.txt b/src/FlotationAnalytics/output_botsort/track4/labels/image3.txt
new file mode 100644
index 0000000..939d953
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track4/labels/image3.txt
@@ -0,0 +1,90 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track40/labels/image39.txt b/src/FlotationAnalytics/output_botsort/track40/labels/image39.txt
new file mode 100644
index 0000000..d01d8f8
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track40/labels/image39.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track41/labels/image40.txt b/src/FlotationAnalytics/output_botsort/track41/labels/image40.txt
new file mode 100644
index 0000000..6bde097
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track41/labels/image40.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track42/labels/image41.txt b/src/FlotationAnalytics/output_botsort/track42/labels/image41.txt
new file mode 100644
index 0000000..32d7c8f
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track42/labels/image41.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track43/labels/image42.txt b/src/FlotationAnalytics/output_botsort/track43/labels/image42.txt
new file mode 100644
index 0000000..d3a4d2a
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track43/labels/image42.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track44/labels/image43.txt b/src/FlotationAnalytics/output_botsort/track44/labels/image43.txt
new file mode 100644
index 0000000..adcf012
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track44/labels/image43.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track45/labels/image44.txt b/src/FlotationAnalytics/output_botsort/track45/labels/image44.txt
new file mode 100644
index 0000000..1aee854
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track45/labels/image44.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track46/labels/image45.txt b/src/FlotationAnalytics/output_botsort/track46/labels/image45.txt
new file mode 100644
index 0000000..3550e10
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track46/labels/image45.txt
@@ -0,0 +1,142 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track47/labels/image46.txt b/src/FlotationAnalytics/output_botsort/track47/labels/image46.txt
new file mode 100644
index 0000000..5a4e997
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track47/labels/image46.txt
@@ -0,0 +1,142 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track48/labels/image47.txt b/src/FlotationAnalytics/output_botsort/track48/labels/image47.txt
new file mode 100644
index 0000000..fab5a7a
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track48/labels/image47.txt
@@ -0,0 +1,141 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track49/labels/image48.txt b/src/FlotationAnalytics/output_botsort/track49/labels/image48.txt
new file mode 100644
index 0000000..374932a
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track49/labels/image48.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track5/labels/image4.txt b/src/FlotationAnalytics/output_botsort/track5/labels/image4.txt
new file mode 100644
index 0000000..a71c395
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track5/labels/image4.txt
@@ -0,0 +1,94 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track50/labels/image49.txt b/src/FlotationAnalytics/output_botsort/track50/labels/image49.txt
new file mode 100644
index 0000000..c807a46
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track50/labels/image49.txt
@@ -0,0 +1,150 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track51/labels/image50.txt b/src/FlotationAnalytics/output_botsort/track51/labels/image50.txt
new file mode 100644
index 0000000..11715d8
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track51/labels/image50.txt
@@ -0,0 +1,152 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track52/labels/image51.txt b/src/FlotationAnalytics/output_botsort/track52/labels/image51.txt
new file mode 100644
index 0000000..808ef2f
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track52/labels/image51.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track53/labels/image52.txt b/src/FlotationAnalytics/output_botsort/track53/labels/image52.txt
new file mode 100644
index 0000000..5461724
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track53/labels/image52.txt
@@ -0,0 +1,153 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track54/labels/image53.txt b/src/FlotationAnalytics/output_botsort/track54/labels/image53.txt
new file mode 100644
index 0000000..bb2437b
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track54/labels/image53.txt
@@ -0,0 +1,153 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track55/labels/image54.txt b/src/FlotationAnalytics/output_botsort/track55/labels/image54.txt
new file mode 100644
index 0000000..1d85b1f
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track55/labels/image54.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track56/labels/image55.txt b/src/FlotationAnalytics/output_botsort/track56/labels/image55.txt
new file mode 100644
index 0000000..b8867c0
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track56/labels/image55.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track57/labels/image56.txt b/src/FlotationAnalytics/output_botsort/track57/labels/image56.txt
new file mode 100644
index 0000000..fa02ad4
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track57/labels/image56.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track58/labels/image57.txt b/src/FlotationAnalytics/output_botsort/track58/labels/image57.txt
new file mode 100644
index 0000000..17d8c29
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track58/labels/image57.txt
@@ -0,0 +1,143 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track59/labels/image58.txt b/src/FlotationAnalytics/output_botsort/track59/labels/image58.txt
new file mode 100644
index 0000000..f2d778d
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track59/labels/image58.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track6/labels/image5.txt b/src/FlotationAnalytics/output_botsort/track6/labels/image5.txt
new file mode 100644
index 0000000..74516a6
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track6/labels/image5.txt
@@ -0,0 +1,94 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track60/labels/image59.txt b/src/FlotationAnalytics/output_botsort/track60/labels/image59.txt
new file mode 100644
index 0000000..4b02820
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track60/labels/image59.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track61/labels/image60.txt b/src/FlotationAnalytics/output_botsort/track61/labels/image60.txt
new file mode 100644
index 0000000..251adab
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track61/labels/image60.txt
@@ -0,0 +1,137 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track62/labels/image61.txt b/src/FlotationAnalytics/output_botsort/track62/labels/image61.txt
new file mode 100644
index 0000000..777a68d
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track62/labels/image61.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track63/labels/image62.txt b/src/FlotationAnalytics/output_botsort/track63/labels/image62.txt
new file mode 100644
index 0000000..63089a3
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track63/labels/image62.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track64/labels/image63.txt b/src/FlotationAnalytics/output_botsort/track64/labels/image63.txt
new file mode 100644
index 0000000..788eaf2
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track64/labels/image63.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track65/labels/image64.txt b/src/FlotationAnalytics/output_botsort/track65/labels/image64.txt
new file mode 100644
index 0000000..951144e
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track65/labels/image64.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track66/labels/image65.txt b/src/FlotationAnalytics/output_botsort/track66/labels/image65.txt
new file mode 100644
index 0000000..b2df0b4
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track66/labels/image65.txt
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diff --git a/src/FlotationAnalytics/output_botsort/track7/labels/image6.txt b/src/FlotationAnalytics/output_botsort/track7/labels/image6.txt
new file mode 100644
index 0000000..dcfec30
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track7/labels/image6.txt
@@ -0,0 +1,92 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track8/labels/image7.txt b/src/FlotationAnalytics/output_botsort/track8/labels/image7.txt
new file mode 100644
index 0000000..65f0159
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track8/labels/image7.txt
@@ -0,0 +1,100 @@
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diff --git a/src/FlotationAnalytics/output_botsort/track9/labels/image8.txt b/src/FlotationAnalytics/output_botsort/track9/labels/image8.txt
new file mode 100644
index 0000000..8844f32
--- /dev/null
+++ b/src/FlotationAnalytics/output_botsort/track9/labels/image8.txt
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+0 0.760665 0.146369 0.0161639 0.0426387 0.679967 76
+0 0.275225 0.487648 0.0202934 0.056887 0.53394 77
+0 0.927249 0.345305 0.0607632 0.0912821 0.587726 80
+0 0.822693 0.281281 0.0414379 0.0870397 0.485279 92
+0 0.721026 0.395321 0.0241219 0.0644827 0.644597 95
+0 0.540199 0.408703 0.0497367 0.0938289 0.725465 98
+0 0.667081 0.420318 0.0193394 0.0335503 0.6353 102
+0 0.446315 0.668699 0.0396666 0.0900663 0.527828 109
+0 0.484342 0.703945 0.0237138 0.0509304 0.556534 111
+0 0.412458 0.348159 0.0581793 0.0869443 0.792039 119
+0 0.764766 0.319233 0.0436619 0.0792938 0.480112 121
+0 0.174858 0.242506 0.0873753 0.180188 0.806539 128
+0 0.63606 0.452795 0.0434691 0.0607245 0.564154 139
+0 0.900579 0.794789 0.0158781 0.0454446 0.745913 154
+0 0.279375 0.175099 0.115662 0.263464 0.671621 156
+0 0.949185 0.430296 0.0465981 0.0825715 0.481434 163
+0 0.237675 0.822093 0.0148911 0.0509376 0.559149 106
+0 0.504626 0.550795 0.0242232 0.0631467 0.546981 174
+0 0.98618 0.653872 0.0241547 0.101474 0.532054 185
+0 0.448506 0.549733 0.0364135 0.0753375 0.59295 189
+0 0.332835 0.858204 0.0262791 0.0815141 0.639391 10
+0 0.913173 0.283021 0.0193472 0.0441495 0.63522 219
+0 0.670597 0.140262 0.0140732 0.0425174 0.514672 225
+0 0.903736 0.244389 0.0170175 0.0354563 0.644056 230
+0 0.984377 0.756328 0.0299979 0.0996255 0.486372 234
+0 0.297028 0.591537 0.0223682 0.068047 0.729205 236
+0 0.524522 0.711493 0.0410138 0.0917732 0.571157 19
+0 0.149279 0.531222 0.0760771 0.1328 0.307955 71
+0 0.639563 0.678515 0.016148 0.0417534 0.571866 82
+0 0.457109 0.77493 0.0214503 0.0668671 0.536701 243
+0 0.895275 0.184421 0.070644 0.0933082 0.687876 249
+0 0.880657 0.292892 0.0173878 0.0348601 0.536979 260
+0 0.62676 0.54471 0.0202842 0.0513676 0.51571 113
+0 0.0835625 0.732303 0.0160754 0.0679639 0.559357 131
+0 0.835884 0.198753 0.0180511 0.0478804 0.754223 269
+0 0.925316 0.232244 0.0176443 0.0421216 0.66268 272
+0 0.750976 0.0757723 0.145534 0.139602 0.760043 274
+0 0.106936 0.412369 0.0500433 0.10083 0.533031 275
+0 0.976395 0.345266 0.0186308 0.04696 0.498342 294
+0 0.633914 0.323555 0.0219186 0.051651 0.688963 299
+0 0.791126 0.140039 0.0225546 0.0546308 0.604793 302
+0 0.101455 0.659031 0.0196735 0.0715838 0.46116 126
+0 0.283625 0.542745 0.0220901 0.0608528 0.742284 17
+0 0.874574 0.883016 0.0275321 0.0614494 0.665226 32
+0 0.970494 0.487403 0.0149669 0.0424539 0.600192 72
+0 0.946082 0.265379 0.0160846 0.0405017 0.748607 312
+0 0.327785 0.0514147 0.0174822 0.050815 0.693685 327
+0 0.375983 0.0419973 0.0506797 0.0753084 0.713173 329
+0 0.212716 0.346667 0.0231467 0.0601955 0.703757 331
+0 0.433614 0.0424345 0.0429908 0.077352 0.548503 337
+0 0.564509 0.890535 0.0153436 0.0443998 0.679267 343
+0 0.604431 0.607683 0.0219343 0.0396501 0.604698 344
+0 0.50024 0.407633 0.0285189 0.107286 0.532087 58
+0 0.494108 0.795428 0.0200105 0.0663442 0.487329 74
+0 0.589525 0.843058 0.0253099 0.0827245 0.631445 75
+0 0.531564 0.367735 0.0265454 0.0974595 0.661548 89
+0 0.338082 0.676766 0.0813207 0.174072 0.748527 46
+0 0.834858 0.515651 0.0121384 0.0394052 0.330304 84
+0 0.869408 0.84053 0.0150842 0.0358866 0.537063 40
+0 0.762486 0.793289 0.0531799 0.0962273 0.615132 78
+0 0.530382 0.336787 0.0207072 0.0472039 0.537269 54
+0 0.651352 0.575824 0.0162086 0.0368126 0.488533 57
+0 0.204062 0.728205 0.0226039 0.0737137 0.586813 34
+0 0.760537 0.575316 0.0153615 0.0310088 0.582649 117
+0 0.192022 0.409986 0.0217216 0.0554597 0.479167 204
+0 0.669865 0.45452 0.0148823 0.0305038 0.481071 105
+0 0.939875 0.867118 0.0163806 0.0429485 0.52592 73
+0 0.441102 0.428776 0.0230642 0.0595756 0.627659 83
+0 0.764996 0.855672 0.0348782 0.0778808 0.66653 149
+0 0.48749 0.347395 0.0272696 0.0664011 0.482546 114
diff --git a/src/FlotationAnalytics/requirements.txt b/src/FlotationAnalytics/requirements.txt
new file mode 100644
index 0000000..edfe920
--- /dev/null
+++ b/src/FlotationAnalytics/requirements.txt
@@ -0,0 +1,17 @@
+streamlit
+opencv-python
+ultralytics
+timm==0.4.9
+imgaug==0.4.0
+six==1.16.0
+hub==3.0.1
+wandb
+scipy==1.10.1
+pillow==9.3.0
+numpy==1.23.4
+pandas==1.5.2
+tqdm
+matplotlib==3.6.3
+pytest
+hydra-core
+omegaconf
\ No newline at end of file
diff --git a/src/FlotationAnalytics/research/countr.py b/src/FlotationAnalytics/research/countr.py
new file mode 100644
index 0000000..39316dc
--- /dev/null
+++ b/src/FlotationAnalytics/research/countr.py
@@ -0,0 +1,204 @@
+!git clone https://github.com/Verg-Avesta/CounTR.git
+
+!pip install -r /content/CounTR/requirements.txt --upgrade --force-reinstall
+
+!pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
+!pip install timm==0.4.9 #0.3.2
+!pip install numpy
+!pip install matplotlib tqdm
+!pip install tensorboard
+!pip install scipy
+!pip install imgaug
+!pip install opencv-python
+!pip3 install hub
+
+"""
+Изменить в файле /content/CounTR/util/misc.py from torch._six import inf на inf = float('inf')
+Изменить в файле /content/CounTR/util/pos_embed.py omega = np.arange(embed_dim // 2, dtype=np.float) на omega = np.arange(embed_dim // 2, dtype=float)
+"""
+
+with open('/usr/local/lib/python3.11/dist-packages/timm/models/layers/helpers.py', 'r') as f:
+ text = f.read()
+
+print(text)
+
+!python /content/CounTR/demo_zero.py --input_path frames/ --output_path results/
+
+"""заменить файл demo_zero.py"""
+import cv2
+import os
+import time
+import torch
+import torch.nn as nn
+import torchvision
+import numpy as np
+from itertools import chain
+from argparse import ArgumentParser
+from pathlib import Path
+from PIL import Image, ImageDraw
+from torchvision import transforms
+
+import timm
+import models_mae_cross
+from util.misc import measure_time
+
+assert "0.4.5" <= timm.__version__ <= "0.4.9" # Проверяем версию timm
+device = torch.device('cuda')
+shot_num = 0
+
+def extract_frames(video_path, output_folder):
+ """
+ Разбиваем видео на кадры и сохраняем их в output_folder.
+ Также вычисляем средний вектор перемещения объектов.
+ """
+ os.makedirs(output_folder, exist_ok=True)
+
+ cap = cv2.VideoCapture(str(video_path))
+ if not cap.isOpened():
+ raise Exception(f"Ошибка: Не удалось открыть видео {video_path}")
+
+ frame_idx = 0
+ prev_gray = None
+ total_motion = np.array([0.0, 0.0])
+ num_vectors = 0
+
+ while True:
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+ frame_path = os.path.join(output_folder, f"frame_{frame_idx:05d}.jpg")
+ cv2.imwrite(frame_path, frame)
+
+ # Определяем средний вектор движения объектов
+ if prev_gray is not None:
+ flow = cv2.calcOpticalFlowFarneback(prev_gray, frame_gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
+ mean_flow = np.mean(flow, axis=(0, 1))
+ total_motion += mean_flow
+ num_vectors += 1
+
+ prev_gray = frame_gray
+ frame_idx += 1
+
+ cap.release()
+
+ avg_motion = total_motion / num_vectors if num_vectors > 0 else np.array([0, 0])
+ print(f"Кадры сохранены в {output_folder} ({frame_idx} кадров).")
+ print(f"Средний вектор перемещения объектов: ({avg_motion[0]:.2f}, {avg_motion[1]:.2f})")
+ return frame_idx, avg_motion
+
+def frames_to_video(input_folder, output_video, fps=30):
+ """Преобразуем кадры обратно в видео"""
+ frame_files = sorted(Path(input_folder).glob("viz_*.jpg"))
+
+ if not frame_files:
+ raise Exception("Ошибка: Не найдено обработанных кадров!")
+
+ first_frame = cv2.imread(str(frame_files[0]))
+ height, width, _ = first_frame.shape
+
+ fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+ out = cv2.VideoWriter(output_video, fourcc, fps, (width, height))
+
+ for frame_file in frame_files:
+ frame = cv2.imread(str(frame_file))
+ out.write(frame)
+
+ out.release()
+ print(f"Видео сохранено: {output_video}")
+
+def load_image(img_path: str):
+ image = Image.open(img_path).convert('RGB')
+ image.load()
+ W, H = image.size
+ new_H = 384
+ new_W = 16 * int((W / H * 384) / 16)
+ image = transforms.Resize((new_H, new_W))(image)
+ Normalize = transforms.Compose([transforms.ToTensor()])
+ image = Normalize(image)
+ boxes = torch.Tensor([])
+ return image, boxes, W, H
+
+def run_one_image(samples, boxes, model, output_path, img_name, old_w, old_h):
+ _, _, h, w = samples.shape
+
+ density_map = torch.zeros([h, w])
+ density_map = density_map.to(device, non_blocking=True)
+ start = 0
+ prev = -1
+ with measure_time() as et:
+ with torch.no_grad():
+ while start + 383 < w:
+ output, = model(samples[:, :, :, start:start + 384], boxes, shot_num)
+ output = output.squeeze(0)
+ b1 = nn.ZeroPad2d(padding=(start, w - prev - 1, 0, 0))
+ d1 = b1(output[:, 0:prev - start + 1])
+ b2 = nn.ZeroPad2d(padding=(prev + 1, w - start - 384, 0, 0))
+ d2 = b2(output[:, prev - start + 1:384])
+
+ b3 = nn.ZeroPad2d(padding=(0, w - start, 0, 0))
+ density_map_l = b3(density_map[:, 0:start])
+ density_map_m = b1(density_map[:, start:prev + 1])
+ b4 = nn.ZeroPad2d(padding=(prev + 1, 0, 0, 0))
+ density_map_r = b4(density_map[:, prev + 1:w])
+
+ density_map = density_map_l + density_map_r + density_map_m / 2 + d1 / 2 + d2
+
+ prev = start + 383
+ start = start + 128
+ if start + 383 >= w:
+ if start == w - 384 + 128:
+ break
+ else:
+ start = w - 384
+
+ pred_cnt = torch.sum(density_map / 60).item()
+
+ fig = samples[0]
+ pred_fig = torch.stack((density_map, torch.zeros_like(density_map), torch.zeros_like(density_map)))
+ count_im = Image.new(mode="RGB", size=(w, h), color=(0, 0, 0))
+ draw = ImageDraw.Draw(count_im)
+ draw.text((w-70, h-50), f"{pred_cnt:.3f}", (255, 255, 255))
+ count_im = np.array(count_im).transpose((2, 0, 1))
+ count_im = torch.tensor(count_im, device=device)
+ fig = fig / 2 + pred_fig / 2 + count_im
+ fig = torch.clamp(fig, 0, 1)
+ fig = transforms.Resize((old_h, old_w))(fig)
+ torchvision.utils.save_image(fig, output_path / f'viz_{img_name}.jpg')
+ return pred_cnt, et
+
+if __name__ == '__main__':
+ start_time = time.time()
+
+ video_path = "/content/drive/MyDrive/Проекты/Отслеживание_в_реальном_времени/Sort+other_methods_time/PseCo/video_cut1.mp4"
+ frames_folder = "frames"
+ output_video = "output_video.mp4"
+
+ # разбиваем видео на кадры и аналирируем движение
+ num_frames, avg_motion = extract_frames(video_path, frames_folder)
+
+ p = ArgumentParser()
+ p.add_argument("--input_path", type=Path, required=True)
+ p.add_argument("--output_path", type=Path, default="results")
+ p.add_argument("--model_path", type=Path, default="/content/drive/MyDrive/Проекты/Отслеживание_в_реальном_времени/Sort+other_methods_time/CounTR/FSC147.pth")
+
+ args = p.parse_args()
+
+ args.output_path.mkdir(exist_ok=True, parents=True)
+
+ model = models_mae_cross.__dict__['mae_vit_base_patch16'](norm_pix_loss='store_true')
+ model.to(device)
+ model.load_state_dict(torch.load(args.model_path, map_location='cpu')['model'], strict=False)
+ model.eval()
+
+ if args.input_path.is_dir():
+ inputs = sorted(list(chain(args.input_path.glob("*.jpg"), args.input_path.glob("*.png"))))
+ for i, img_path in enumerate(inputs):
+ samples, boxes, old_w, old_h = load_image(img_path)
+ result, elapsed_time = run_one_image(samples.unsqueeze(0).to(device), boxes.unsqueeze(0).to(device), model, args.output_path, img_path.stem, old_w, old_h)
+ print(f"[{i+1}/{len(inputs)}] {img_path.name}:\tcount = {result:.2f} - time = {elapsed_time.duration:.2f}")
+
+ frames_to_video("results", output_video)
+
+ print(f"Время работы программы: {time.time() - start_time:.2f} секунд")
\ No newline at end of file
diff --git a/src/FlotationAnalytics/research/opticalflow.py b/src/FlotationAnalytics/research/opticalflow.py
new file mode 100644
index 0000000..a91c6e7
--- /dev/null
+++ b/src/FlotationAnalytics/research/opticalflow.py
@@ -0,0 +1,239 @@
+import cv2
+import numpy as np
+
+video_path = "/content/drive/MyDrive/Проекты/Мультитрекинг однородных объектов/YOLOv5-QCB+SORT/input_yolov5.mp4"
+cap = cv2.VideoCapture(video_path)
+
+if not cap.isOpened():
+ print("Ошибка: не удалось открыть видео.")
+ exit()
+
+frame_width = int(cap.get(3))
+frame_height = int(cap.get(4))
+fps = int(cap.get(cv2.CAP_PROP_FPS))
+
+output_path = "output.mp4"
+fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+
+# Параметры детектора углов (Shi-Tomasi)
+feature_params = dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)
+
+# Параметры Лукаса-Канаде для оптического потока
+lk_params = dict(winSize=(15, 15), maxLevel=2,
+ criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
+
+ret, old_frame = cap.read()
+if not ret:
+ print("Ошибка при загрузке первого кадра")
+ cap.release()
+ out.release()
+ exit()
+
+old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
+p0 = cv2.goodFeaturesToTrack(old_gray, mask=None, **feature_params)
+mask = np.zeros_like(old_frame)
+
+while True:
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+ p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
+ if p1 is not None:
+ good_new = p1[st == 1]
+ good_old = p0[st == 1]
+
+ for i, (new, old) in enumerate(zip(good_new, good_old)):
+ a, b = new.ravel()
+ c, d = old.ravel()
+ mask = cv2.line(mask, (int(a), int(b)), (int(c), int(d)), (0, 255, 0), 2)
+ frame = cv2.circle(frame, (int(a), int(b)), 5, (0, 0, 255), -1)
+
+ img = cv2.add(frame, mask)
+
+ else:
+ img = frame
+
+ out.write(img)
+ old_gray = frame_gray.copy()
+ p0 = good_new.reshape(-1, 1, 2) if len(good_new) > 0 else None
+
+cap.release()
+out.release()
+
+print(f"Видео сохранено как {output_path}")
+
+import cv2
+import numpy as np
+import time
+
+video_path = "/content/drive/MyDrive/Проекты/Мультитрекинг однородных объектов/YOLOv5-QCB+SORT/input_yolov5.mp4"
+cap = cv2.VideoCapture(video_path)
+
+if not cap.isOpened():
+ print("Ошибка: не удалось открыть видео.")
+ exit()
+
+frame_width = int(cap.get(3))
+frame_height = int(cap.get(4))
+fps = int(cap.get(cv2.CAP_PROP_FPS))
+
+output_path = "output_tracking.mp4"
+fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+
+ret, old_frame = cap.read()
+if not ret:
+ print("Ошибка при загрузке первого кадра")
+ cap.release()
+ out.release()
+ exit()
+
+old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
+feature_params = dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)
+p0 = cv2.goodFeaturesToTrack(old_gray, mask=None, **feature_params)
+mask = np.zeros_like(old_frame)
+
+start_time = time.time()
+
+# Хранение среднего вектора перемещения по всем кадрам
+total_dx, total_dy, frame_count = 0, 0, 0
+
+while True:
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+ # Вычисляем оптический поток (метод Лукаса-Канаде)
+ p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None)
+
+ if p1 is not None and st is not None:
+ good_new = p1[st == 1]
+ good_old = p0[st == 1]
+ dx_values, dy_values = [], []
+
+ for i, (new, old) in enumerate(zip(good_new, good_old)):
+ a, b = new.ravel()
+ c, d = old.ravel()
+ dx, dy = a - c, b - d
+ dx_values.append(dx)
+ dy_values.append(dy)
+ mask = cv2.line(mask, (int(a), int(b)), (int(c), int(d)), (0, 255, 0), 2)
+ frame = cv2.circle(frame, (int(a), int(b)), 5, (0, 0, 255), -1)
+
+ if dx_values and dy_values:
+ avg_dx = np.mean(dx_values)
+ avg_dy = np.mean(dy_values)
+ else:
+ avg_dx, avg_dy = 0, 0
+
+ total_dx += avg_dx
+ total_dy += avg_dy
+ frame_count += 1
+
+ text = f"Avg Flow: ({avg_dx:.2f}, {avg_dy:.2f})"
+ cv2.putText(frame, text, (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+
+ p0 = good_new.reshape(-1, 1, 2)
+
+ output_frame = cv2.add(frame, mask)
+ out.write(output_frame)
+ old_gray = frame_gray.copy()
+
+global_avg_dx = total_dx / frame_count if frame_count > 0 else 0
+global_avg_dy = total_dy / frame_count if frame_count > 0 else 0
+
+end_time = time.time()
+total_time = end_time - start_time
+
+cap.release()
+out.release()
+
+print(f"Видео сохранено как {output_path}")
+print(f"Время работы мультитрекинга: {total_time:.2f} секунд")
+print(f"Средний вектор перемещения пузырей за всё видео: ({global_avg_dx:.2f}, {global_avg_dy:.2f})")
+
+import cv2
+import numpy as np
+import time
+
+video_path = "/content/drive/MyDrive/Проекты/Отслеживание_в_реальном_времени/Sort+other_methods_time/Optical_flow/video_cut1.mp4"
+cap = cv2.VideoCapture(video_path)
+
+if not cap.isOpened():
+ print("Ошибка: не удалось открыть видео.")
+ exit()
+
+frame_width = int(cap.get(3))
+frame_height = int(cap.get(4))
+fps = int(cap.get(cv2.CAP_PROP_FPS))
+
+output_path = "output_плотный.mp4"
+fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+ret, old_frame = cap.read()
+if not ret:
+ print("Ошибка при загрузке первого кадра")
+ cap.release()
+ out.release()
+ exit()
+
+old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
+
+start_time = time.time()
+total_dx, total_dy, frame_count = 0, 0, 0
+
+while True:
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+ # Вычисляем плотный оптический поток (метод Гуннара Фарнебака)
+ flow = cv2.calcOpticalFlowFarneback(old_gray, frame_gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
+
+ step = 15
+ dx_values, dy_values = [], []
+
+ for y in range(0, frame.shape[0], step):
+ for x in range(0, frame.shape[1], step):
+ fx, fy = flow[y, x]
+ end_x = int(x + fx)
+ end_y = int(y + fy)
+
+ if np.sqrt(fx**2 + fy**2) > 2:
+ cv2.arrowedLine(frame, (x, y), (end_x, end_y), (0, 0, 255), 1, tipLength=0.3)
+ dx_values.append(fx)
+ dy_values.append(fy)
+
+ if dx_values and dy_values:
+ avg_dx = np.mean(dx_values)
+ avg_dy = np.mean(dy_values)
+ else:
+ avg_dx, avg_dy = 0, 0
+
+ total_dx += avg_dx
+ total_dy += avg_dy
+ frame_count += 1
+
+ text = f"Avg Flow: ({avg_dx:.2f}, {avg_dy:.2f})"
+ cv2.putText(frame, text, (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+ out.write(frame)
+ old_gray = frame_gray.copy()
+
+global_avg_dx = total_dx / frame_count if frame_count > 0 else 0
+global_avg_dy = total_dy / frame_count if frame_count > 0 else 0
+
+end_time = time.time()
+total_time = end_time - start_time
+cap.release()
+out.release()
+
+print(f"Видео сохранено как {output_path}")
+print(f"Время работы мультитрекинга: {total_time:.2f} секунд")
+print(f"Средний вектор перемещения пузырей за всё видео: ({global_avg_dx:.2f}, {global_avg_dy:.2f})")
\ No newline at end of file
diff --git a/src/FlotationAnalytics/research/pseco.py b/src/FlotationAnalytics/research/pseco.py
new file mode 100644
index 0000000..4a309e3
--- /dev/null
+++ b/src/FlotationAnalytics/research/pseco.py
@@ -0,0 +1,222 @@
+!git clone --depth 1 https://github.com/Hzzone/PseCo
+import sys
+sys.path.insert(0, './PseCo')
+!nvidia-smi
+
+!git clone https://github.com/Hzzone/PseCo
+
+!pip install -r ./PseCo/requirements.txt
+
+!mkdir ./PseCo/data && mkdir ./PseCo/data/fsc147 && mv '/content/drive/MyDrive/Проекты/Отслеживание_в_реальном_времени/Sort+other_methods_time/PseCo/checkpoints' ./PseCo/data/fsc147
+
+from google.colab import drive
+drive.mount('/content/drive')
+
+import sys
+sys.path.append("/content/PseCo")
+
+import os
+from torchvision.utils import make_grid
+from torchvision.transforms.functional import to_pil_image, to_tensor
+# %load_ext autoreload
+# %autoreload 2
+import matplotlib.pyplot as plt
+import torch.nn.functional as F
+import torch
+from PIL import Image
+import numpy as np
+import tqdm
+import albumentations as A
+import torch.nn as nn
+import torchvision
+import torchvision.ops as vision_ops
+from ops.foundation_models.segment_anything.utils.amg import batched_mask_to_box
+from ops.ops import _nms, plot_results, convert_to_cuda
+plt.rcParams["figure.dpi"] = 300
+torch.cuda.set_device(0)
+torch.autograd.set_grad_enabled(False)
+!gpustat
+
+from ops.dump_clip_features import dump_clip_image_features, dump_clip_text_features
+
+def read_image(path):
+ img = Image.open(path)
+ transform = A.Compose([
+ A.LongestMaxSize(1024),
+ A.PadIfNeeded(1024, border_mode=0, position=A.PadIfNeeded.PositionType.TOP_LEFT),
+ ])
+ img = Image.fromarray(transform(image=np.array(img))['image'])
+ return img
+
+from ops.foundation_models.segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor, build_sam, build_sam_vit_b, build_sam_vit_h
+
+sam = build_sam_vit_h().cuda().eval()
+
+project_root = './PseCo'
+
+fname_bubbles = f'{project_root}/images/пузыри.jpg'
+
+image = read_image(fname_bubbles)
+
+# few-shot
+example_boxes = torch.Tensor([
+ [400., 270., 450., 318.],
+ [620., 440., 655., 471.],
+ [373., 475., 409., 516.]
+ ])
+example_features = dump_clip_image_features(image, example_boxes).cuda()
+
+from models import ROIHeadMLP as ROIHead
+cls_head = ROIHead().cuda().eval()
+cls_head.load_state_dict(torch.load(f'{project_root}/data/fsc147/checkpoints/MLP_small_box_w1_fewshot.tar', map_location='cpu')['cls_head'])
+
+plot_results(read_image(fname_bubbles),
+ bboxes=example_boxes,
+ )
+
+import torchvision.transforms as transforms
+transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+with torch.no_grad():
+ new_image = transform(image).unsqueeze(0).cuda()
+ features = sam.image_encoder(new_image)
+
+from models import PointDecoder
+point_decoder = PointDecoder(sam).cuda().eval()
+state_dict = torch.load(f'{project_root}/data/fsc147/checkpoints/point_decoder_vith.pth',
+ map_location='cpu', weights_only=True)
+point_decoder.load_state_dict(state_dict)
+with torch.no_grad():
+ point_decoder.max_points = 1000
+ point_decoder.point_threshold = 0.05
+ point_decoder.nms_kernel_size = 3
+ outputs_heatmaps = point_decoder(features)
+ pred_heatmaps = outputs_heatmaps['pred_heatmaps'].cpu().squeeze().clamp(0, 1)
+
+plt.figure(figsize=(10, 10))
+plt.imshow(image)
+pred_points = outputs_heatmaps['pred_points'].squeeze().reshape(-1, 2)
+pred_points_score = outputs_heatmaps['pred_points_score'].squeeze()
+print(pred_points.size())
+plt.scatter(pred_points[:, 0].cpu(), pred_points[:, 1].cpu(), s=20, marker='.', c='lime')
+plt.axis('off')
+
+print(sam.image_encoder.pos_embed.shape)
+
+import cv2
+import time
+import torch
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+from models import ROIHeadMLP as ROIHead
+from ops.dump_clip_features import dump_clip_image_features
+from ops.foundation_models.segment_anything import build_sam_vit_h
+from models import PointDecoder
+from PIL import Image
+import numpy as np
+import albumentations as A
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+project_root = './PseCo'
+
+sam = build_sam_vit_h().to(device).eval()
+cls_head = ROIHead().to(device).eval()
+
+state_dict = torch.load(f'{project_root}/data/fsc147/checkpoints/MLP_small_box_w1_fewshot.tar', map_location=device)
+cls_head.load_state_dict(state_dict['cls_head'], strict=False)
+
+point_decoder = PointDecoder(sam).to(device).eval()
+state_dict = torch.load(f'{project_root}/data/fsc147/checkpoints/point_decoder_vith.pth', map_location=device)
+point_decoder.load_state_dict(state_dict)
+
+def resize_image(image):
+ transform = A.Compose([
+ A.Resize(1024, 1024),
+ A.PadIfNeeded(min_height=1024, min_width=1024, border_mode=0, position=A.PadIfNeeded.PositionType.TOP_LEFT)
+ ])
+ return Image.fromarray(transform(image=np.array(image))['image'])
+
+def process_frame(frame):
+ img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+ img = resize_image(img)
+ transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ])
+ img_tensor = transform(img).unsqueeze(0).to(device)
+
+ with torch.no_grad():
+ features = sam.image_encoder(img_tensor)
+ outputs_heatmaps = point_decoder(features)
+ pred_points = outputs_heatmaps['pred_points'].squeeze().reshape(-1, 2).cpu().numpy()
+
+ for (x, y) in pred_points:
+ cv2.circle(frame, (int(x), int(y)), 3, (0, 255, 0), -1)
+
+ return frame, pred_points
+
+def compute_optical_flow(prev_frame, curr_frame, prev_points):
+ if prev_points is None or len(prev_points) == 0:
+ return np.array([]), np.array([])
+
+ prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
+ curr_gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)
+
+ new_points, status, _ = cv2.calcOpticalFlowPyrLK(prev_gray, curr_gray, prev_points.astype(np.float32), None)
+ valid_points = new_points[status.flatten() == 1]
+ prev_points = prev_points[status.flatten() == 1]
+
+ return prev_points, valid_points
+
+def process_video(input_path, output_path):
+ cap = cv2.VideoCapture(input_path)
+ fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+ fps = int(cap.get(cv2.CAP_PROP_FPS))
+ width, height = int(cap.get(3)), int(cap.get(4))
+ out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+ prev_frame, prev_points = None, None
+ total_vectors = []
+
+ start_time = time.time()
+ frame_count = 0
+
+ while cap.isOpened():
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ frame_start_time = time.time()
+
+ processed_frame, points = process_frame(frame)
+
+ if prev_frame is not None:
+ prev_pts, new_pts = compute_optical_flow(prev_frame, processed_frame, prev_points)
+ if len(prev_pts) > 0:
+ movement_vectors = new_pts - prev_pts
+ total_vectors.extend(movement_vectors)
+
+ prev_frame, prev_points = frame, points
+ out.write(processed_frame)
+
+ frame_count += 1
+
+ cap.release()
+ out.release()
+
+ end_time = time.time()
+ total_time = end_time - start_time
+ avg_vector = np.mean(total_vectors, axis=0) if total_vectors else [0, 0]
+
+ fps_actual = frame_count / total_time
+
+ print(f"Время работы: {total_time:.2f} секунд")
+ print(f"Средний вектор перемещения: {avg_vector}")
+ print(f"Средний FPS: {fps_actual:.2f}")
+
+input_video = "/content/видео.mp4"
+output_video = "output.mp4"
+process_video(input_video, output_video)
\ No newline at end of file
diff --git a/src/FlotationAnalytics/research/sort.py b/src/FlotationAnalytics/research/sort.py
new file mode 100644
index 0000000..362559c
--- /dev/null
+++ b/src/FlotationAnalytics/research/sort.py
@@ -0,0 +1,717 @@
+### Загружаем YOLO
+!pip install opencv-python-headless torch torchvision torchaudio
+!git clone https://github.com/ultralytics/yolov5
+!wget https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt
+
+### По сегментации делаем разметку
+import cv2
+import os
+import numpy as np
+
+# Извлечение bbox из маски
+def extract_bounding_boxes(mask):
+ contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+ boxes = []
+ for cnt in contours:
+ x, y, w, h = cv2.boundingRect(cnt)
+ boxes.append([x, y, x+w, y+h])
+ return boxes
+
+# Конвертация bbox в YOLO формат
+def convert_bbox_to_yolo(box, image_width, image_height):
+ x1, y1, x2, y2 = box
+ x_center = (x1 + x2) / 2 / image_width
+ y_center = (y1 + y2) / 2 / image_height
+ width = (x2 - x1) / image_width
+ height = (y2 - y1) / image_height
+ return x_center, y_center, width, height
+
+def process_images_to_yolo_labels(image_folder, mask_folder, output_folder, class_id=0):
+ os.makedirs(output_folder, exist_ok=True)
+
+ for image_name in os.listdir(image_folder):
+ if not image_name.lower().endswith('.jpg'):
+ continue
+
+ base_name = os.path.splitext(image_name)[0]
+ image_path = os.path.join(image_folder, image_name)
+ mask_path = os.path.join(mask_folder, f"{base_name}.png")
+ txt_path = os.path.join(output_folder, f"{base_name}.txt")
+
+ if not os.path.exists(mask_path):
+ print(f"Маска для {image_name} не найдена. Пропуск...")
+ continue
+
+ image = cv2.imread(image_path)
+ mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+
+ if image is None:
+ print(f"Ошибка при загрузке изображения: {image_path}")
+ continue
+ if mask is None:
+ print(f"Ошибка при загрузке маски: {mask_path}")
+ continue
+
+ boxes = extract_bounding_boxes(mask)
+ if not boxes:
+ print(f"Не найдено объектов на изображении {image_name}")
+ continue
+
+ height, width = image.shape[:2]
+ with open(txt_path, 'w') as f:
+ for box in boxes:
+ yolo_coords = convert_bbox_to_yolo(box, width, height)
+ f.write(f"{class_id} {' '.join(f'{x:.6f}' for x in yolo_coords)}\n")
+
+ print(f"Разметка для {image_name} сохранена в {txt_path}")
+
+if __name__ == "__main__":
+ image_folder = '/content/drive/MyDrive/Проекты/Мультитрекинг однородных объектов/YOLOv5-QCB+SORT/data/val/images'
+ mask_folder = '/content/drive/MyDrive/Проекты/Мультитрекинг однородных объектов/YOLOv5-QCB+SORT/data/val/masks'
+ output_folder = '/content/drive/MyDrive/Проекты/Отслеживание в реальном времени/Sort + other methods time/labels'
+
+ process_images_to_yolo_labels(image_folder, mask_folder, output_folder)
+
+
+### Обучаем YOLO
+
+data_yaml = """
+train: /content/drive/MyDrive/Проекты/Мультитрекинг однородных объектов/YOLOv5-QCB+SORT/data/train/images
+val: /content/drive/MyDrive/Проекты/Мультитрекинг однородных объектов/YOLOv5-QCB+SORT/data/val/images
+
+nc: 1
+names: ['bubble']
+"""
+
+data_path = '/content/data.yaml'
+
+with open(data_path, 'w') as f:
+ f.write(data_yaml)
+
+import os
+import subprocess
+
+data_yaml = "data.yaml"
+model_weights = "yolov5s.pt"
+epochs = 50
+batch_size = 10
+conf_thres = 0.1
+max_det = 1000
+
+command = f"python ./yolov5/train.py --data {data_yaml} --cfg yolov5s.yaml --weights {model_weights} --epochs {epochs} --batch-size {batch_size} --hyp hyp.scratch.yaml --img 640 --device 0 --conf-thres {conf_thres} --max-det {max_det}"
+subprocess.run(command, shell=True)
+
+import time
+import os
+
+start_time = time.time()
+
+!yolo train data=data.yaml model=yolov5s.pt epochs=50 batch=10 conf=0.1 max_det=1000
+
+end_time = time.time()
+print(f"Время тренировки: {end_time - start_time:.2f} секунд")
+
+### Детекция с помощью YOLO
+
+import time
+import cv2
+import os
+from ultralytics import YOLO
+
+model = YOLO('/content/runs/detect/train/weights/best.pt')
+
+video_path = '/content/drive/MyDrive/Проекты/Мультитрекинг однородных объектов/YOLOv5-QCB+SORT/input_yolov5.mp4'
+detections_folder = '/content/drive/MyDrive/Проекты/Отслеживание_в_реальном_времени/Sort+other_methods_time/Видео/детекции_пузыри_3/'
+os.makedirs(detections_folder, exist_ok=True)
+
+cap = cv2.VideoCapture(video_path)
+if not cap.isOpened():
+ print("Ошибка при открытии видео")
+ exit()
+
+frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+fps = cap.get(cv2.CAP_PROP_FPS)
+output_path = '/content/drive/MyDrive/Проекты/Мультитрекинг однородных объектов/YOLOv5-QCB+SORT/out_yolov5.mp4'
+fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+
+# Обработка кадров
+frame_number = 0
+total_time = 0
+frame_count = 0
+while True:
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ start_time = time.time()
+
+ # Применяем детекцию объектов на текущем кадре
+ results = model(frame, max_det=1000)
+
+ end_time = time.time()
+
+ inference_time = end_time - start_time
+ total_time += inference_time
+ frame_count += 1
+
+ print(f"Кадр {frame_number}: время инференса = {inference_time:.4f} сек")
+
+ # Получаем аннотации для текущего кадра (формат: x1, y1, x2, y2, conf, class)
+ annotations = results[0].boxes.data.cpu().numpy()
+
+ # Проверка на наличие детекций
+ if annotations.size > 0:
+ # Создаем текстовый файл для текущего кадра, если есть детекции
+ detections_path = os.path.join(detections_folder, f"frame_{frame_number:05d}.txt")
+ with open(detections_path, "w") as f:
+ for box in annotations:
+ x1, y1, x2, y2, conf, class_id = box
+ width = x2 - x1
+ height = y2 - y1
+ f.write(f"{int(class_id)},{x1},{y1},{width},{height},{conf}\n")
+ cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
+
+ out.write(frame)
+ frame_number += 1
+
+cap.release()
+out.release()
+
+# Выводим среднее время инференса
+print(f"\nОбщее время: {total_time:.4f} сек")
+if frame_count > 0:
+ avg_time = total_time / frame_count
+ print(f"\nСреднее время инференса: {avg_time:.4f} сек/кадр\n")
+ print(f"FPS инференса: {1 / avg_time:.2f} кадров/сек\n")
+else:
+ print("Не удалось обработать ни одного кадра.")
+print(f"Детекции для каждого кадра сохранены в {detections_folder}")
+
+### Устанавливаем все необходимое, чтобы открыть расширение .mkv
+
+!apt-get update
+!apt-get install -y ffmpeg
+
+!ffmpeg -i "/content/42.ФМ ь 23-001.mkv"
+
+# !ffmpeg -i "/content/42.ФМ ь 23-001.mkv" -ss 00:00:00 -to 00:00:08 -c copy "/content/new_video1.mkv" #обрезка видео
+
+!ffmpeg -i "/content/42.ФМ ь 23-001.mkv" -ss 00:00:00 -to 00:00:08 -c:v libx264 -preset fast -crf 23 -c:a aac -b:a 128k "/content/video_cut1.mp4"
+
+### Установка SORT
+
+!git clone https://github.com/abewley/sort.git
+
+!pip install filterpy==1.4.5
+!pip install scikit-image==0.18.1
+
+### Установка lap
+
+!python -m pip install --upgrade pip
+
+!pip install --upgrade lap
+
+!git clone https://github.com/gatagat/lap.git
+
+import lap
+
+### Делим видео на кадры
+
+import cv2
+import os
+
+def split_video_into_frames(video_path, frames_folder):
+ os.makedirs(frames_folder, exist_ok=True)
+ cap = cv2.VideoCapture(video_path)
+ frame_number = 0
+
+ while cap.isOpened():
+ ret, frame = cap.read()
+ if not ret:
+ break
+ frame_path = os.path.join(frames_folder, f"frame_{frame_number:05d}.jpg")
+ cv2.imwrite(frame_path, frame)
+ frame_number += 1
+
+ cap.release()
+ print(f"Видео разделено на {frame_number} кадров и сохранено в папке {frames_folder}")
+
+split_video_into_frames('/content/drive/MyDrive/Проекты/Мультитрекинг однородных объектов/YOLOv5-QCB+SORT/out_yolov5.mp4', '/content/drive/MyDrive/Проекты/Отслеживание_в_реальном_времени/Sort+other_methods_time/Видео/frame_detection')
+
+!pip install filterpy
+
+!python /content/sort.py
+
+### Наносим трекинг на изначальную видеозапись
+import os
+import cv2
+number_of_frames = 100
+def visualize_tracking(video_path, tracked_data_folder, output_video_path):
+ cap = cv2.VideoCapture(video_path)
+ width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+ height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+ fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+ out = cv2.VideoWriter(output_video_path, fourcc, 30.0, (width, height))
+
+ for frame_number in range(number_of_frames):
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ tracked_path = os.path.join(tracked_data_folder, f"tracked_{frame_number:05d}.txt")
+ if os.path.exists(tracked_path):
+ with open(tracked_path, "r") as f:
+ for line in f:
+ x1, y1, x2, y2, obj_id = map(float, line.strip().split(","))
+ cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
+ cv2.putText(frame, f"ID: {int(obj_id)}", (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+ out.write(frame)
+
+ cap.release()
+ out.release()
+
+visualize_tracking('/content/drive/MyDrive/Проекты/Мультитрекинг однородных объектов/YOLOv5-QCB+SORT/input_yolov5.mp4','/content/drive/MyDrive/Проекты/Отслеживание_в_реальном_времени/Sort+other_methods_time/finish','/content/last_video_cut1.mp4')
+
+# заменить код sort.py
+"""
+ SORT: A Simple, Online and Realtime Tracker
+ Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+from __future__ import print_function
+
+import os
+import numpy as np
+import matplotlib
+matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from skimage import io
+
+import glob
+import time
+import argparse
+from filterpy.kalman import KalmanFilter
+import cv2
+
+np.random.seed(0)
+
+def temporal_consistency(bubble_centers):
+ consistency_scores = []
+ for t in range(len(bubble_centers) - 1):
+ frame1 = bubble_centers[t]
+ frame2 = bubble_centers[t + 1]
+
+ if len(frame1) == len(frame2):
+ distances = [np.linalg.norm(np.array(p1) - np.array(p2)) for p1, p2 in zip(frame1, frame2)]
+ consistency_scores.append(np.mean(distances))
+ else:
+ continue
+
+ if consistency_scores:
+ return np.mean(consistency_scores)
+ else:
+ print("No valid consistency scores calculated. Returning 0.")
+ return 0
+
+def optical_flow_similarity(prev_frame, next_frame, prev_centers, next_centers):
+ prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
+ next_gray = cv2.cvtColor(next_frame, cv2.COLOR_BGR2GRAY)
+
+ flow = cv2.calcOpticalFlowFarneback(prev_gray, next_gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
+
+ similarities = []
+ for (x1, y1), (x2, y2) in zip(prev_centers, next_centers):
+ try:
+ dx, dy = flow[int(y1), int(x1)]
+ motion_vector = np.array([x2 - x1, y2 - y1])
+ flow_vector = np.array([dx, dy])
+
+ if np.linalg.norm(motion_vector) > 0 and np.linalg.norm(flow_vector) > 0:
+ cos_sim = np.dot(motion_vector, flow_vector) / (np.linalg.norm(motion_vector) * np.linalg.norm(flow_vector))
+ similarities.append(cos_sim)
+ except Exception as e:
+ print(f"Error calculating optical flow for centers ({x1}, {y1}) and ({x2}, {y2}): {e}")
+ continue
+
+ return np.mean(similarities) if similarities else None
+
+def object_recall_watershed(frame):
+ gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+ _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+ kernel = np.ones((3, 3), np.uint8)
+ opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
+ sure_bg = cv2.dilate(opening, kernel, iterations=3)
+ dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
+ _, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)
+ sure_fg = np.uint8(sure_fg)
+ unknown = cv2.subtract(sure_bg, sure_fg)
+ _, markers = cv2.connectedComponents(sure_fg)
+ markers = markers + 1
+ markers[unknown == 255] = 0
+ frame_colored = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
+ cv2.watershed(frame_colored, markers)
+ frame_colored[markers == -1] = [0, 0, 255]
+ return np.max(markers) - 1
+
+
+def linear_assignment(cost_matrix):
+ try:
+ import lap
+ _, x, y = lap.lapjv(cost_matrix, extend_cost=True)
+ return np.array([[y[i],i] for i in x if i >= 0]) #
+ except ImportError:
+ from scipy.optimize import linear_sum_assignment
+ x, y = linear_sum_assignment(cost_matrix)
+ return np.array(list(zip(x, y)))
+
+
+def iou_batch(bb_test, bb_gt):
+ """
+ From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
+ """
+ bb_gt = np.expand_dims(bb_gt, 0)
+ bb_test = np.expand_dims(bb_test, 1)
+
+ xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])
+ yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])
+ xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])
+ yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])
+ w = np.maximum(0., xx2 - xx1)
+ h = np.maximum(0., yy2 - yy1)
+ wh = w * h
+ o = wh / ((bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])
+ + (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1]) - wh)
+ return(o)
+
+
+def convert_bbox_to_z(bbox):
+ """
+ Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
+ [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
+ the aspect ratio
+ """
+ w = bbox[2] - bbox[0]
+ h = bbox[3] - bbox[1]
+
+ if w <= 0 or h <= 0:
+ print(f"Invalid bounding box: {bbox}")
+ return np.array([0, 0, 0, 0]).reshape((4, 1))
+
+ x = bbox[0] + w / 2.
+ y = bbox[1] + h / 2.
+ s = w * h
+ r = w / float(h)
+ return np.array([x, y, s, r]).reshape((4, 1))
+
+def convert_x_to_bbox(x,score=None):
+ """
+ Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
+ [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
+ """
+ w = np.sqrt(x[2] * x[3])
+ h = x[2] / w
+ if(score==None):
+ return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.]).reshape((1,4))
+ else:
+ return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.,score]).reshape((1,5))
+
+
+class KalmanBoxTracker(object):
+ """
+ This class represents the internal state of individual tracked objects observed as bbox.
+ """
+ count = 0
+ def __init__(self,bbox):
+ """
+ Initialises a tracker using initial bounding box.
+ """
+ #define constant velocity model
+ self.kf = KalmanFilter(dim_x=7, dim_z=4)
+ self.kf.F = np.array([[1,0,0,0,1,0,0],[0,1,0,0,0,1,0],[0,0,1,0,0,0,1],[0,0,0,1,0,0,0], [0,0,0,0,1,0,0],[0,0,0,0,0,1,0],[0,0,0,0,0,0,1]])
+ self.kf.H = np.array([[1,0,0,0,0,0,0],[0,1,0,0,0,0,0],[0,0,1,0,0,0,0],[0,0,0,1,0,0,0]])
+
+ self.kf.R[2:,2:] *= 10.
+ self.kf.P[4:,4:] *= 1000. #give high uncertainty to the unobservable initial velocities
+ self.kf.P *= 10.
+ self.kf.Q[-1,-1] *= 0.01
+ self.kf.Q[4:,4:] *= 0.01
+
+ self.kf.x[:4] = convert_bbox_to_z(bbox)
+ self.time_since_update = 0
+ self.id = KalmanBoxTracker.count
+ KalmanBoxTracker.count += 1
+ self.history = []
+ self.hits = 0
+ self.hit_streak = 0
+ self.age = 0
+
+ def update(self,bbox):
+ """
+ Updates the state vector with observed bbox.
+ """
+ self.time_since_update = 0
+ self.history = []
+ self.hits += 1
+ self.hit_streak += 1
+ self.kf.update(convert_bbox_to_z(bbox))
+
+ def predict(self):
+ """
+ Advances the state vector and returns the predicted bounding box estimate.
+ """
+ if((self.kf.x[6]+self.kf.x[2])<=0):
+ self.kf.x[6] *= 0.0
+ self.kf.predict()
+ self.age += 1
+ if(self.time_since_update>0):
+ self.hit_streak = 0
+ self.time_since_update += 1
+ self.history.append(convert_x_to_bbox(self.kf.x))
+ return self.history[-1]
+
+ def get_state(self):
+ """
+ Returns the current bounding box estimate.
+ """
+ return convert_x_to_bbox(self.kf.x)
+
+
+def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
+ """
+ Assigns detections to tracked object (both represented as bounding boxes)
+
+ Returns 3 lists of matches, unmatched_detections and unmatched_trackers
+ """
+ if(len(trackers)==0):
+ return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)
+
+ iou_matrix = iou_batch(detections, trackers)
+
+ if min(iou_matrix.shape) > 0:
+ a = (iou_matrix > iou_threshold).astype(np.int32)
+ if a.sum(1).max() == 1 and a.sum(0).max() == 1:
+ matched_indices = np.stack(np.where(a), axis=1)
+ else:
+ matched_indices = linear_assignment(-iou_matrix)
+ else:
+ matched_indices = np.empty(shape=(0,2))
+
+ unmatched_detections = []
+ for d, det in enumerate(detections):
+ if(d not in matched_indices[:,0]):
+ unmatched_detections.append(d)
+ unmatched_trackers = []
+ for t, trk in enumerate(trackers):
+ if(t not in matched_indices[:,1]):
+ unmatched_trackers.append(t)
+
+ #filter out matched with low IOU
+ matches = []
+ for m in matched_indices:
+ if(iou_matrix[m[0], m[1]]= self.min_hits or self.frame_count <= self.min_hits):
+ ret.append(np.concatenate((d,[trk.id+1])).reshape(1,-1)) # +1 as MOT benchmark requires positive
+ i -= 1
+ # remove dead tracklet
+ if(trk.time_since_update > self.max_age):
+ self.trackers.pop(i)
+ if(len(ret)>0):
+ return np.concatenate(ret)
+ return np.empty((0,5))
+
+def parse_args():
+ """Parse input arguments."""
+ parser = argparse.ArgumentParser(description='SORT demo')
+ parser.add_argument('--display', dest='display', help='Display online tracker output (slow) [False]',action='store_true')
+ parser.add_argument("--seq_path", help="Path to detections.", type=str, default='data')
+ parser.add_argument("--phase", help="Subdirectory in seq_path.", type=str, default='train')
+ parser.add_argument("--max_age",
+ help="Maximum number of frames to keep alive a track without associated detections.",
+ type=int, default=1)
+ parser.add_argument("--min_hits",
+ help="Minimum number of associated detections before track is initialised.",
+ type=int, default=3)
+ parser.add_argument("--iou_threshold", help="Minimum IOU for match.", type=float, default=0.3)
+ args = parser.parse_args()
+ return args
+
+def analyze_tracking(pred_tracks):
+ total_objects = 0
+ total_tracks = 0
+ track_durations = []
+ prev_frame_objects = set()
+ new_objects = 0
+ lost_objects = 0
+
+ for frame_index in sorted(pred_tracks.keys()):
+ frame = pred_tracks[frame_index]
+ current_frame_objects = set(frame)
+ total_objects += len(frame)
+
+ # Считаем количество новых объектов (FP) и потерянных объектов (FN)
+ new_objects += len(current_frame_objects - prev_frame_objects)
+ lost_objects += len(prev_frame_objects - current_frame_objects)
+
+ # Считаем продолжительность треков (каждый трек - это объект, который появляется на нескольких кадрах)
+ for obj in frame:
+ track_durations.append(frame_index + 1) # Длительность - это номер кадра, на котором появился объект
+
+ prev_frame_objects = current_frame_objects
+ total_tracks += len(frame)
+
+ # Среднее количество объектов на кадр
+ avg_objects_per_frame = total_objects / len(pred_tracks) if len(pred_tracks) > 0 else 0
+ # Средняя продолжительность треков
+ avg_track_duration = np.mean(track_durations) if len(track_durations) > 0 else 0
+ # Частота появления новых объектов (FP) и исчезновения объектов (FN)
+ new_object_frequency = new_objects / len(pred_tracks) if len(pred_tracks) > 0 else 0
+ lost_object_frequency = lost_objects / len(pred_tracks) if len(pred_tracks) > 0 else 0
+
+ return {
+ "avg_objects_per_frame": avg_objects_per_frame,
+ "avg_track_duration": avg_track_duration,
+ "new_object_frequency": new_object_frequency,
+ "lost_object_frequency": lost_object_frequency
+ }
+
+if __name__ == "__main__":
+ frames_folder = '/content/drive/MyDrive/Проекты/Отслеживание_в_реальном_времени/Sort+other_methods_time/Видео/frame_detection'
+ detections_folder = '/content/drive/MyDrive/Проекты/Отслеживание_в_реальном_времени/Sort+other_methods_time/Видео/детекции_пузыри_3/'
+ output_folder = '/content/drive/MyDrive/Проекты/Отслеживание_в_реальном_времени/Sort+other_methods_time/finish/'
+ os.makedirs(output_folder, exist_ok=True)
+
+ tracker = Sort()
+ frame_files = sorted([f for f in os.listdir(frames_folder) if f.endswith(".jpg")])
+
+ bubble_centers = {}
+
+ for frame_number, frame_file in enumerate(frame_files):
+ detections_path = os.path.join(detections_folder, f"frame_{frame_number:05d}.txt")
+ detections = []
+
+ with open(detections_path, "r") as f:
+ for line in f:
+ det = list(map(float, line.strip().split(",")))
+ x1, y1, width, height, conf = det[1:]
+ x2, y2 = x1 + width, y1 + height
+ detections.append([x1, y1, x2, y2, conf])
+
+ detections = np.array(detections) if detections else np.empty((0, 5))
+ tracked_objects = tracker.update(detections)
+
+ bubble_centers[frame_number] = []
+ if len(tracked_objects) > 0:
+ for obj in tracked_objects:
+ x1, y1, x2, y2, obj_id = obj
+ center_x = (x1 + x2) / 2
+ center_y = (y1 + y2) / 2
+ bubble_centers[frame_number].append((center_x, center_y))
+ with open(os.path.join(output_folder, f"tracked_{frame_number:05d}.txt"), "w") as f:
+ for obj in tracked_objects:
+ f.write(",".join(map(str, obj)) + "\n")
+
+ current_frame = cv2.imread(os.path.join(frames_folder, frame_file))
+
+ if current_frame is None:
+ print(f"Error: Could not load frame {frame_number}")
+ continue
+
+ watershed_bubble_count = object_recall_watershed(current_frame)
+
+ # Анализ темпоральной согласованности и оптического потока
+ if frame_number > 0:
+ prev_frame = cv2.imread(os.path.join(frames_folder, frame_files[frame_number - 1]))
+
+ if prev_frame is None:
+ print(f"Error: Could not load previous frame {frame_files[frame_number - 1]}")
+ continue
+
+ temporal_score = temporal_consistency(bubble_centers)
+ flow_score = optical_flow_similarity(prev_frame, current_frame,
+ bubble_centers[frame_number - 1],
+ bubble_centers[frame_number])
+
+ print(f"Frame {frame_number}: Temporal Consistency = {temporal_score}, "
+ f"Optical Flow Similarity = {flow_score}, "
+ f"Watershed Bubble Count = {watershed_bubble_count}")
+
+ analysis = analyze_tracking(bubble_centers)
+
+ print(f"Среднее количество объектов на кадр: {analysis['avg_objects_per_frame']:.2f}")
+ print(f"Средняя продолжительность треков: {analysis['avg_track_duration']:.2f} кадров")
+ print(f"Частота появления новых объектов (FP): {analysis['new_object_frequency']:.2f}")
+ print(f"Частота исчезновения объектов (FN): {analysis['lost_object_frequency']:.2f}")
\ No newline at end of file
diff --git a/src/FlotationAnalytics/tests/test_metrics.py b/src/FlotationAnalytics/tests/test_metrics.py
new file mode 100644
index 0000000..4c988b9
--- /dev/null
+++ b/src/FlotationAnalytics/tests/test_metrics.py
@@ -0,0 +1,252 @@
+import pytest
+import numpy as np
+from app.main import TrackingQualityAnalyzer
+from app.main import DeepSortTracker
+from app.main import VideoTracker
+
+@pytest.fixture
+def analyzer():
+ return TrackingQualityAnalyzer()
+
+def test_initial_state(analyzer):
+ assert len(analyzer.metrics['frame']) == 0
+ assert analyzer.prev_tracks == {}
+ assert analyzer.prev_frame is None
+
+def test_calculate_iou():
+ analyzer = TrackingQualityAnalyzer()
+ box1 = [0, 0, 10, 10]
+ box2 = [0, 0, 10, 10]
+ assert analyzer.calculate_iou(box1, box2) == 1.0
+
+ box3 = [5, 5, 10, 10]
+ assert analyzer.calculate_iou(box1, box3) == 0.14285714285714285
+
+ box4 = [20, 20, 30, 30]
+ assert analyzer.calculate_iou(box1, box4) == 0.0
+
+def test_update_metrics_simple(analyzer):
+ frame1 = np.zeros((100, 100, 3), dtype=np.uint8)
+ frame2 = np.zeros((100, 100, 3), dtype=np.uint8)
+
+ current_tracks = [
+ [1, 10, 10, 20, 20], # track_id, x, y, w, h
+ [2, 30, 30, 10, 10]
+ ]
+ analyzer.update_metrics(1, current_tracks, frame1)
+
+ assert len(analyzer.metrics['displacement']) == 0
+ assert len(analyzer.metrics['active_tracks']) == 0
+ assert len(analyzer.metrics['track_lengths']) == 2
+
+ current_tracks = [
+ [1, 12, 12, 20, 20],
+ [2, 31, 31, 10, 10]
+ ]
+ analyzer.update_metrics(2, current_tracks, frame2)
+
+ assert len(analyzer.metrics['displacement']) == 1
+ assert analyzer.metrics['active_tracks'] == [2]
+ assert analyzer.metrics['track_lengths'] == {1: 2, 2: 2}
+ assert analyzer.metrics['coverage'][0] == 1.0
+ assert analyzer.metrics['temporal_consistency'][0] > 0
+
+def test_tracking_score_calculation(analyzer):
+ analyzer.metrics = {
+ 'displacement': [5.0, 6.0, 4.5],
+ 'coverage': [0.9, 0.8, 0.85],
+ 'temporal_consistency': [0.7, 0.75, 0.8],
+ 'optical_flow': [2.0, 2.5, 3.0],
+ 'track_lengths': {1: 10, 2: 15, 3: 8},
+ 'active_tracks': [2, 3, 2],
+ 'frame': [1, 2, 3]
+ }
+
+ normalized_score = analyzer.get_tracking_score()
+ assert 0 <= normalized_score <= 2.0
+
+ raw_score = analyzer.get_tracking_score(normalize=False)
+ assert isinstance(raw_score, float)
+
+ custom_weights = {
+ 'avg_displacement': -0.3,
+ 'avg_coverage': 0.4,
+ 'avg_temporal_consistency': 0.3,
+ 'avg_optical_flow': -0.2,
+ 'track_length_mean': 0.15,
+ 'max_active_tracks': 0.05
+ }
+ custom_score = analyzer.get_tracking_score(weights=custom_weights)
+ assert 0 <= custom_score <= 2.0
+ assert not np.isclose(custom_score, normalized_score)
+
+def test_empty_metrics(analyzer):
+ assert analyzer.get_final_metrics() == {}
+ assert analyzer.get_tracking_score() == 0.0
+
+def test_plot_generation(analyzer):
+ for i in range(5):
+ analyzer.metrics['frame'].append(i)
+ analyzer.metrics['displacement'].append(i * 0.5)
+ analyzer.metrics['coverage'].append(min(0.9, i * 0.2))
+ analyzer.metrics['temporal_consistency'].append(min(0.8, i * 0.15))
+ analyzer.metrics['optical_flow'].append(i * 0.3)
+ analyzer.metrics['active_tracks'].append(i + 1)
+ analyzer.metrics['track_lengths'][i] = i + 5
+
+ fig = analyzer.generate_metrics_plots()
+ assert fig is not None
+ assert len(fig.get_axes()) == 6
+
+def test_optical_flow_calculation(analyzer):
+ frame1 = np.zeros((100, 100, 3), dtype=np.uint8)
+ frame2 = np.zeros((100, 100, 3), dtype=np.uint8)
+ frame3 = np.zeros((100, 100, 3), dtype=np.uint8)
+
+ frame2[50:70, 50:70] = 255
+
+ analyzer.update_metrics(1, [[1, 45, 45, 30, 30]], frame1)
+ assert len(analyzer.metrics['optical_flow']) == 0
+
+ analyzer.update_metrics(2, [[1, 50, 50, 30, 30]], frame2)
+ assert len(analyzer.metrics['optical_flow']) == 1
+ assert analyzer.metrics['optical_flow'][0] > 0
+
+ analyzer.update_metrics(3, [[1, 55, 55, 30, 30]], frame3)
+ assert len(analyzer.metrics['optical_flow']) == 2
+ assert analyzer.metrics['optical_flow'][1] >= 0
+
+def test_deepsort_tracker_initialization():
+ tracker = DeepSortTracker(img_size=(640, 480))
+ assert tracker.img_size == (640, 480)
+ assert tracker.nms_max_overlap == 0.6
+ assert tracker.iou_threshold == 0.3
+ assert tracker.tracker is not None
+
+ tracker = DeepSortTracker(
+ img_size=(1280, 720),
+ nms_max_overlap=0.5,
+ max_cosine_distance=0.4,
+ max_age=50,
+ min_hits=5,
+ iou_threshold=0.2
+ )
+ assert tracker.img_size == (1280, 720)
+ assert tracker.nms_max_overlap == 0.5
+ assert tracker.iou_threshold == 0.2
+
+def test_prepare_detections():
+ tracker = DeepSortTracker(img_size=(640, 480))
+ detections = np.array([
+ [10, 10, 50, 50, 0.9, 0], # x1, y1, x2, y2, conf, class
+ [20, 20, 60, 60, 0.8, 1]
+ ])
+
+ prepared = tracker.prepare_detections(detections)
+ assert len(prepared) == 2
+ assert prepared[0].tlwh.tolist() == [10, 10, 40, 40] # x, y, w, h
+ assert prepared[0].confidence == 0.9
+ assert prepared[1].tlwh.tolist() == [20, 20, 40, 40]
+ assert prepared[1].confidence == 0.8
+
+ empty_detections = np.empty((0, 6))
+ prepared = tracker.prepare_detections(empty_detections)
+ assert len(prepared) == 0
+
+def test_update_with_empty_detections():
+ tracker = DeepSortTracker(img_size=(640, 480))
+ results = tracker.update([])
+ assert len(results) == 0
+
+
+def test_update_with_multiple_detections():
+ tracker = DeepSortTracker(img_size=(640, 480))
+ detections = np.array([
+ [10, 10, 50, 50, 0.9, 0],
+ [20, 20, 60, 60, 0.8, 1],
+ [30, 30, 70, 70, 0.7, 2]
+ ])
+ results = tracker.update(detections)
+ assert len(results) <= len(detections)
+ for result in results:
+ assert len(result) == 5
+
+@pytest.fixture
+def tracker():
+ return VideoTracker("model/FSC147.pth", "some_tracker_type")
+
+def test_empty_input(tracker):
+ points = np.array([])
+ scores = np.array([])
+ result = tracker.non_max_suppression(points, scores)
+ assert len(result) == 0
+
+def test_single_point(tracker):
+ points = np.array([[10, 20]])
+ scores = np.array([0.9])
+ result = tracker.non_max_suppression(points, scores)
+ assert np.array_equal(result, points)
+
+def test_no_suppression_needed(tracker):
+ points = np.array([[10, 20], [50, 60], [100, 120]])
+ scores = np.array([0.9, 0.8, 0.7])
+ result = tracker.non_max_suppression(points, scores)
+ assert np.array_equal(result, points)
+
+def test_basic_suppression(tracker):
+ points = np.array([
+ [10, 10],
+ [12, 12],
+ [50, 50],
+ [52, 52]
+ ])
+ scores = np.array([0.9, 0.8, 0.7, 0.6])
+ expected = np.array([[10, 10], [50, 50]])
+ result = tracker.non_max_suppression(points, scores, radius=5)
+ assert np.array_equal(result, expected)
+
+def test_custom_radius(tracker):
+ points = np.array([
+ [10, 10],
+ [15, 15],
+ [30, 30]
+ ])
+ scores = np.array([0.9, 0.8, 0.7])
+ result_small_radius = tracker.non_max_suppression(points, scores, radius=2)
+ assert len(result_small_radius) == 3
+
+ result_large_radius = tracker.non_max_suppression(points, scores, radius=10)
+ assert len(result_large_radius) == 2
+ assert np.array_equal(result_large_radius, np.array([[10, 10], [30, 30]]))
+
+def test_scores_ordering(tracker):
+ points = np.array([
+ [10, 10],
+ [11, 11],
+ [50, 50]
+ ])
+ scores = np.array([0.8, 0.9, 0.7])
+ result = tracker.non_max_suppression(points, scores, radius=5)
+ assert np.array_equal(result, np.array([[11, 11], [50, 50]]))
+
+def test_edge_case_radius_zero(tracker):
+ points = np.array([
+ [10, 10],
+ [10, 10],
+ [20, 20]
+ ])
+ scores = np.array([0.9, 0.8, 0.7])
+ result = tracker.non_max_suppression(points, scores, radius=0)
+ assert len(result) == 3
+
+def test_multiple_close_points(tracker):
+ points = np.array([
+ [10, 10],
+ [11, 11],
+ [12, 12],
+ [50, 50],
+ [51, 51]
+ ])
+ scores = np.array([0.9, 0.8, 0.7, 0.6, 0.5])
+ result = tracker.non_max_suppression(points, scores, radius=5)
+ assert np.array_equal(result, np.array([[10, 10], [50, 50]]))
\ No newline at end of file
diff --git a/src/FlotationAnalytics/video/video1.mp4 b/src/FlotationAnalytics/video/video1.mp4
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diff --git a/src/FlotationAnalytics/video/video2.mp4 b/src/FlotationAnalytics/video/video2.mp4
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index 0000000..09ff9c4
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diff --git a/src/FlotationAnalytics/video/video3.mov b/src/FlotationAnalytics/video/video3.mov
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index 0000000..0142f0f
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