jumping.py

# -*- coding: utf-8 -*-
"""JUMPING.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/12r86XP6o_5wx5ZtZwxnUI6JMnYLouQiN

https://github.com/diegocavalca/machine-learning/blob/master/supervisioned/object.detection_tensorflow/advanced.detection.ipynb
"""

from google.colab import drive
drive.mount('/content/drive')

!apt-get install -y -qq protobuf-compiler python-pil python-lxml

!git clone --quiet https://github.com/tensorflow/models.git

import os
os.chdir('models/research')

!protoc object_detection/protos/*.proto --python_out=.

import sys
sys.path.append('/content/models/research/slim')

import os
import cv2
import time
import argparse
import multiprocessing
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
# %matplotlib inline

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

MODEL_NAME = '/content/drive/My Drive/JUMPING/ssd_mobilenet_v1_coco_11_06_2017'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

PATH_TO_LABELS

NUM_CLASSES = 90

label_map = label_map_util.load_labelmap('/content/drive/My Drive/JUMPING/' + PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

def detect_objects(image_np, sess, detection_graph):
    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
    image_np_expanded = np.expand_dims(image_np, axis=0)
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

    # Each box represents a part of the image where a particular object was detected.
    boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

    # Each score represent how level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    scores = detection_graph.get_tensor_by_name('detection_scores:0')
    classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')

    # Actual detection.
    (boxes, scores, classes, num_detections) = sess.run(
        [boxes, scores, classes, num_detections],
        feed_dict={image_tensor: image_np_expanded})

    # Visualization of the results of a detection.
    vis_util.visualize_boxes_and_labels_on_image_array(
        image_np,
        np.squeeze(boxes),
        np.squeeze(classes).astype(np.int32),
        np.squeeze(scores),
        category_index,
        use_normalized_coordinates=True,
        line_thickness=8)
    return image_np

# First test on images
PATH_TO_TEST_IMAGES_DIR = 'images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'teste-{}.jpg'.format(i)) for i in range(1, 8) ]

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)

from PIL import Image
for image_path in TEST_IMAGE_PATHS:
    image = Image.open('/content/drive/My Drive/JUMPING/' + image_path)
    image_np = load_image_into_numpy_array(image)
    plt.imshow(image_np)
    print(image.size, image_np.shape)

#Load a frozen TF model 
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

# %matplotlib inline
with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        for image_path in TEST_IMAGE_PATHS:
            image = Image.open('/content/drive/My Drive/JUMPING/' + image_path)
            print('/content/drive/My Drive/JUMPING/' + image_path)
            image_np = load_image_into_numpy_array(image)
            image_process = detect_objects(image_np, sess, detection_graph)
            print(image_process.shape)
            plt.figure(figsize=IMAGE_SIZE)
            plt.imshow(image_process)

# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML

plt.imshow(image_process)