diff --git a/rope/Dicts.py b/rope/Dicts.py index f1581310..5be86d08 100644 --- a/rope/Dicts.py +++ b/rope/Dicts.py @@ -1,6 +1,15 @@ DEFAULT_DATA = { # Buttons 'AddMarkerButtonDisplay': 'icon', +'AutoRotateSwitchState': False, # 或者 True,取决于你希望的默认状态 +'AutoRotateSwitchInfoText': '自动旋转图像以检测人脸', # 添加一个信息文本 +'AutoRotateSwitchState': False, +'AutoRotateSwitchInfoText': '自动旋转图像以检测人脸', +'AutoRotateSwitchIconOn': 'path/to/icon_on.png', +'AutoRotateSwitchIconOff': 'path/to/icon_off.png', +'AutoRotateSwitchIconHover': 'path/to/icon_hover.png', +'AutoRotateSwitchDisplay': 'both', # 或 'icon' 或 'text' +'AutoRotateSwitchText': 'Auto Rotate', 'AddMarkerButtonIconHover': './rope/media/add_marker_hover.png', 'AddMarkerButtonIconOff': './rope/media/add_marker_off.png', 'AddMarkerButtonIconOn': './rope/media/add_marker_off.png', diff --git a/rope/GUI.py b/rope/GUI.py index 2437f845..84ed68c5 100644 --- a/rope/GUI.py +++ b/rope/GUI.py @@ -481,6 +481,10 @@ def create_gui(self): row += top_border_delta self.static_widget['3'] = GE.Separator_x(self.layer['parameters_frame'], 0, row) row += bottom_border_delta + + # 在适当的位置添加新的AutoRotateSwitch控件 + self.widget['AutoRotateSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'AutoRotateSwitch', 'Auto Rotate', 3, self.update_data, 'parameter', 398, 20, 1, row) + row += switch_delta # Orientation self.widget['OrientSwitch'] = GE.Switch2(self.layer['parameters_frame'], 'OrientSwitch', 'Orientation', 3, self.update_data, 'parameter', 398, 20, 1, row) diff --git a/rope/VideoManager.py b/rope/VideoManager.py index 3cee29e0..96d507a2 100644 --- a/rope/VideoManager.py +++ b/rope/VideoManager.py @@ -1,3 +1,4 @@ +import math import os import cv2 import tkinter as tk @@ -506,106 +507,260 @@ def thread_video_read(self, frame_number): # @profile def swap_video(self, target_image, frame_number, use_markers): - # Grab a local copy of the parameters to prevent threading issues + # 获取参数的本地副本以防止线程问题 parameters = self.parameters.copy() control = self.control.copy() - # Find out if the frame is in a marker zone and copy the parameters if true + # 检查帧是否在标记区域内,如果是则复制参数 if self.markers and use_markers: - temp=[] + temp = [] for i in range(len(self.markers)): temp.append(self.markers[i]['frame']) idx = bisect.bisect(temp, frame_number) - parameters = self.markers[idx-1]['parameters'].copy() - # Load frame into VRAM + # 将帧加载到VRAM img = torch.from_numpy(target_image.astype('uint8')).to('cuda') #HxWxc img = img.permute(2,0,1)#cxHxW - #Scale up frame if it is smaller than 512 + # 如果帧小于512,则放大 img_x = img.size()[2] img_y = img.size()[1] if img_x<512 and img_y<512: - # if x is smaller, set x to 512 if img_x <= img_y: tscale = v2.Resize((int(512*img_y/img_x), 512), antialias=True) else: tscale = v2.Resize((512, int(512*img_x/img_y)), antialias=True) - img = tscale(img) - elif img_x<512: tscale = v2.Resize((int(512*img_y/img_x), 512), antialias=True) img = tscale(img) - elif img_y<512: tscale = v2.Resize((512, int(512*img_x/img_y)), antialias=True) img = tscale(img) - # Rotate the frame - if parameters['OrientSwitch']: - img = v2.functional.rotate(img, angle=parameters['OrientSlider'], interpolation=v2.InterpolationMode.BILINEAR, expand=True) + # # 自动旋转功能 + # if parameters.get('AutoRotateSwitch', True): # 假设您添加了一个新的AutoRotateSwitch参数 + # for rotation in range(4): # 0°, 90°, 180°, 270° + # if rotation > 0: + # img = v2.functional.rotate(img, angle=90, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + + # # 检测人脸 + # kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0) + + # if len(kpss) > 0: # 如果检测到人脸,跳出循环 + # break + # else: + # # 原有的旋转功能 + # if parameters['OrientSwitch']: + # img = v2.functional.rotate(img, angle=parameters['OrientSlider'], interpolation=v2.InterpolationMode.BILINEAR, expand=True) + + # # 检测人脸 + # kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0) + + # 自动旋转功能2 + if parameters.get('AutoRotateSwitch', True): # 假设您添加了一个新的AutoRotateSwitch参数 + rotation2 = 0 + rotation3 = 0 + face_angle = 0 + offset_angle = 0 + isCorrectFace = False + for rotation in range(4): # 0°, 90°, 180°, 270° + print("开始第",rotation,"次检测") + if rotation > 0: + img = v2.functional.rotate(img, angle=90, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + rotation3 = rotation*90 + # 检测人脸 + kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0) + + if len(kpss) > 0: # 如果检测到人脸,跳出循环 + print("检测到人脸") + # 如果有人脸，判断这个人脸是不是目标人脸。 + for face_kps in kpss:# 这里face_kps指的是视频中的人脸。 + # 获取视频中的人脸的嵌入 + face_emb, _ = self.func_w_test('recognize', self.models.run_recognize, img, face_kps) + # 计算视频中的人脸的嵌入与目标人脸的嵌入的相似度 + sim = self.findCosineDistance(face_emb, self.found_faces[0]["Embedding"]) + if sim >= float(parameters["ThresholdSlider"]): + print("找到了") + print("当前旋转的角度：", rotation) + + + + # # 计算当前人脸的角度，通过眼睛和鼻子的相对位置 + # # print("face_kps:", face_kps) + # # 获取左眼、右眼和鼻子的坐标 + # left_eye = face_kps[0] + # right_eye = face_kps[1] + # nose = face_kps[2] + + # # 计算眼睛中心点 + # eye_center = (left_eye + right_eye) / 2 + + # # 计算眼睛中心到鼻子的向量 + # eye_to_nose = nose - eye_center + + # # 计算人脸的角度 + # face_angle = math.atan2(eye_to_nose[1], eye_to_nose[0]) + # face_angle = -math.degrees(face_angle) + + + # 计算当前人脸的角度，通过眼睛和嘴巴的相对位置。 + left_eye = face_kps[0] + right_eye = face_kps[1] + mouth_center = (face_kps[3]+face_kps[4])/2 + eye_center = (left_eye + right_eye) / 2 + eye_to_mouth = mouth_center - eye_center + face_angle = math.atan2(eye_to_mouth[1], eye_to_mouth[0]) + face_angle = -math.degrees(face_angle) + + + + + print("当前人脸face_angle:", face_angle) + if(rotation==0 or rotation==2): + print("检测到0，2了") + # 修正角度 + offset_angle = face_angle - rotation*90+90 + #计算offset_angle接近的整数 + offset_angle = -round(offset_angle/90)*90 + if( rotation==3): + print("检测到3了") + # 修正角度 + offset_angle = face_angle - rotation*90-90 + #计算offset_angle接近的整数 + offset_angle = round(offset_angle/90)*90 + img = v2.functional.rotate(img, angle=-90, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + if(rotation==1): + # 修正角度 + offset_angle = face_angle - rotation*90-90 + #计算offset_angle接近的整数 + offset_angle = round(offset_angle/90)*90 + + print("修正后的角度：", offset_angle) + # 先转回来 + img = v2.functional.rotate(img, angle=-rotation3, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + # 再转回去 + img = v2.functional.rotate(img, angle=offset_angle, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + + + # img = v2.functional.rotate(img, angle=-rotation2, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + + kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0) + if len(kpss) > 0: + print("第二次检测到人脸") + if( rotation==3): + img = v2.functional.rotate(img, angle=-offset_angle, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0) + offset_angle = rotation3 + isCorrectFace = True + else: + print("第二次没有检测到人脸！！！！！") + # 再转回来 + if( rotation==3): + img = v2.functional.rotate(img, angle=-offset_angle, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0) + offset_angle = rotation3 + else: + img = v2.functional.rotate(img, angle=rotation3, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + img = v2.functional.rotate(img, angle=-offset_angle, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0) + offset_angle = rotation3 + isCorrectFace = True + break + else: + print("检测到人脸但是sim值小于阈值") + offset_angle = rotation3 + continue# 继续下一次循环 + if isCorrectFace: + break + else: + print("这次没有检测到人脸") + offset_angle = 270 + continue + else: + # 原有的旋转功能 + if parameters['OrientSwitch']: + img = v2.functional.rotate(img, angle=parameters['OrientSlider'], interpolation=v2.InterpolationMode.BILINEAR, expand=True) + # 检测人脸 + kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0) + - # Find all faces in frame and return a list of 5-pt kpss - kpss = self.func_w_test("detect", self.models.run_detect, img, parameters['DetectTypeTextSel'], max_num=20, score=parameters['DetectScoreSlider']/100.0) - # Get embeddings for all faces found in the fram + + # 获取所有检测到的人脸的嵌入 ret = [] for face_kps in kpss: face_emb, _ = self.func_w_test('recognize', self.models.run_recognize, img, face_kps) ret.append([face_kps, face_emb]) - if ret: - # Loop through target faces to see if they match our found face embeddings + if len(ret) > 0: + print("ret>0") + # 遍历目标人脸,查看它们是否与我们找到的人脸嵌入匹配 for fface in ret: for found_face in self.found_faces: - # sim between face in video and already found face + # 视频中的人脸和已找到的人脸之间的相似度 sim = self.findCosineDistance(fface[1], found_face["Embedding"]) - # if the face[i] in the frame matches afound face[j] AND the found face is active (not []) + # 如果帧中的人脸[i]匹配已找到的人脸[j],并且找到的人脸是活跃的(不是[]) if sim>=float(parameters["ThresholdSlider"]) and found_face["SourceFaceAssignments"]: s_e = found_face["AssignedEmbedding"] - # s_e = found_face['ptrdata'] img = self.func_w_test("swap_video", self.swap_core, img, fface[0], s_e, parameters, control) - # img = img.permute(2,0,1) - + img = img.permute(1,2,0) - if not control['MaskViewButton'] and parameters['OrientSwitch']: - img = img.permute(2,0,1) - img = transforms.functional.rotate(img, angle=-parameters['OrientSlider'], expand=True) - img = img.permute(1,2,0) - + # 如果mask + if not control['MaskViewButton']: + # img = img.permute(2,0,1) + # if parameters['OrientSwitch']: + # img = transforms.functional.rotate(img, angle=-parameters['OrientSlider'], expand=True) + # elif parameters.get('AutoRotateSwitch', False): + # img = transforms.functional.rotate(img, angle=-90*rotation, expand=True) + # img = img.permute(1,2,0) + if parameters.get('OrientSwitch', True): + print("aaa") + img = img.permute(2,0,1) + img = transforms.functional.rotate(img, angle=-parameters['OrientSlider'], expand=True) + img = img.permute(1,2,0) + if parameters.get('AutoRotateSwitch', True): + # print("bbb") + img = img.permute(2,0,1) + # img = transforms.functional.rotate(img, angle=- rotation3+rotation2, expand=True)#完全不对 + # img = transforms.functional.rotate(img, angle=- rotation3, expand=True)#差一点对了，图片会转到“头朝上”的位置 + # img = transforms.functional.rotate(img, angle=-face_angle - 90, expand=True)#差一点对了，图片会转到“头朝上”的位置 + img = transforms.functional.rotate(img, angle=-offset_angle, expand=True)#差一点对了，图片会转到“头朝上”的位置 + img = img.permute(1,2,0) else: + print("ret=0") img = img.permute(1,2,0) - if parameters['OrientSwitch']: + # if parameters['OrientSwitch'] or parameters.get('AutoRotateSwitch', False): + if parameters['OrientSwitch'] : + print("ccc") img = img.permute(2,0,1) - img = v2.functional.rotate(img, angle=-parameters['OrientSlider'], interpolation=v2.InterpolationMode.BILINEAR, expand=True) + if parameters['OrientSwitch']: + print("ddd") + img = v2.functional.rotate(img, angle=-parameters['OrientSlider'], interpolation=v2.InterpolationMode.BILINEAR, expand=True) + # elif parameters.get('AutoRotateSwitch', False): + # img = v2.functional.rotate(img, angle=-90*rotation, interpolation=v2.InterpolationMode.BILINEAR, expand=True) + img = img.permute(1,2,0) + if parameters.get('AutoRotateSwitch', True): + print("eee") + img = img.permute(2,0,1) + # img = transforms.functional.rotate(img, angle=-90*rotation, expand=True)#完全不对 + # img = transforms.functional.rotate(img, angle=- rotation3, expand=True)#差一点对了，图片会转到“头朝上”的位置 + # img = transforms.functional.rotate(img, angle=-face_angle - 90, expand=True)#差一点对了，图片会转到“头朝上”的位置 + img = transforms.functional.rotate(img, angle=-offset_angle, expand=True)#差一点对了，图片会转到“头朝上”的位置 img = img.permute(1,2,0) - if self.perf_test: print('------------------------') - # Unscale small videos + # 缩小小视频 if img_x <512 or img_y < 512: tscale = v2.Resize((img_y, img_x), antialias=True) img = img.permute(2,0,1) img = tscale(img) img = img.permute(1,2,0) - img = img.cpu().numpy() - # if ret: - # - # for kpoint in ret[0][0]: - # for i in range(-1, 1): - # for j in range(-1, 1): - # - # img[int(kpoint[1])+i][int(kpoint[0])+j][0] = 255 - # img[int(kpoint[1])+i][int(kpoint[0])+j][1] = 255 - # img[int(kpoint[1])+i][int(kpoint[0])+j][2] = 255 - - return img.astype(np.uint8) def findCosineDistance(self, vector1, vector2):