def PoseEstimatorPredict(image_path,plot = False,resolution ='432x368', scales = '[None]',model = 'mobilenet_thin'): ''' input: image_path,图片路径,jpg plot = False,是否画图,如果True,两样内容,关键点信息+标点图片matrix resolution ='432x368', 规格 scales = '[None]', model = 'mobilenet_thin',模型选择 output: plot为false,返回一个内容:关键点信息 plot为true,返回两个内容:关键点信息+标点图片matrix ''' w, h = model_wh(resolution) e = TfPoseEstimator(get_graph_path(model), target_size=(w, h)) image = common.read_imgfile(image_path, None, None) t = time.time() humans = e.inference(image, scales=scales) # 主要的预测函数 elapsed = time.time() - t logger.info('inference image: %s in %.4f seconds.' % (image_path, elapsed)) centers = get_keypoint(image,humans) # 拿上关键点信息 if plot: # 画图的情况下: image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) # 画图函数 fig = plt.figure() a = fig.add_subplot(2, 2, 1) a.set_title('Result') plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) return centers,image else: # 不画图的情况下: return centers
def main(input_img, model, e): ''' Query the model given an image ''' if(input_img): image = stringToImage(input_img[input_img.find(",")+1:]) image = toRGB(image) if(model == None): model = 'mobilenet_thin' humans = e.inference(image) coords = [] for human in humans: coords.append([[HUMAN_COCO_PART[k], b.x, b.y] for k, b in human.body_parts.items()]) outdata = { 'humans': coords } return outdata else: # Test with a sample image image = common.read_imgfile('./images/p1.jpg', None, None) e = TfPoseEstimator(get_graph_path('mobilenet_thin'), target_size=(432, 368)) humans = e.inference(image) coords = [] for human in humans: coords.append([[HUMAN_COCO_PART[k], b.x, b.y] for k, b in human.body_parts.items()]) outdata = { 'humans': coords } return outdata
class ConvPoseMachine(Detector): def __init__(self, input_wicth=800, input_height=640): import sys sys.path.append('./src/detector/tf-pose-estimation/src') self.model = 'cmu' import common self.enum_coco_parts = common.CocoPart self.enum_coco_colors = common.CocoColors self.enum_coco_pairs_render = common.CocoPairsRender from estimator import TfPoseEstimator from networks import get_graph_path self.image_h, self.image_w = input_height, input_wicth if self.image_w % 16 != 0 or self.image_h % 16 != 0: raise Exception( 'Width and height should be multiples of 16. w=%d, h=%d' % (self.image_w, self.image_h)) print('Warming up detector ConvPoseMachine....') import time s = time.time() self.estimator = TfPoseEstimator(get_graph_path(self.model), target_size=(self.image_w, self.image_h)) e = time.time() print('ConvPoseMachine Warmed, Time: {}'.format(e - s)) def predict(self, imgcv): # Build model based on input image size img_h, img_w, _ = imgcv.shape humans = self.estimator.inference(imgcv) formatted_dets = [] for human in humans: key_point = {} # draw point for i in range(self.enum_coco_parts.Background.value): if i not in human.body_parts.keys(): continue body_part = human.body_parts[i] x = int( (body_part.x * self.image_w + 0.5) * img_w / self.image_w) y = int( (body_part.y * self.image_h + 0.5) * img_h / self.image_h) center = (x, y) key_point[i] = center detection = get_default_detection() detection['person_keypoint'] = key_point formatted_dets.append(detection) return formatted_dets
def index(): try: data = request.data with open('/tmp/temp.jpg', 'wb') as f: f.write(data) img = common.read_imgfile('/tmp/temp.jpg', 432, 368) scales = ast.literal_eval(args.scales) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) humans = e.inference(img, scales=scales) return jsonify({"humans": list(map(lambda x: x.to_dict(), humans))}) except Exception as e: return jsonify({"error": str(traceback.format_exc())})
class tfOpenpose: def __init__(self, zoom=1.0, resolution='656x368', model='cmu', show_process=False): self.zoom = zoom self.resolution = resolution self.model = model self.show_process = show_process logger.debug('initialization %s : %s' % (model, get_graph_path(model))) self.w, self.h = model_wh(resolution) self.e = TfPoseEstimator(get_graph_path(model), target_size=(self.w, self.h)) def detect(self, image): logger.debug('image preprocess+') if self.zoom < 1.0: canvas = np.zeros_like(image) img_scaled = cv2.resize(image, None, fx=self.zoom, fy=self.zoom, interpolation=cv2.INTER_LINEAR) dx = (canvas.shape[1] - img_scaled.shape[1]) // 2 dy = (canvas.shape[0] - img_scaled.shape[0]) // 2 canvas[dy:dy + img_scaled.shape[0], dx:dx + img_scaled.shape[1]] = img_scaled image = canvas elif self.zoom > 1.0: img_scaled = cv2.resize(image, None, fx=self.zoom, fy=self.zoom, interpolation=cv2.INTER_LINEAR) dx = (img_scaled.shape[1] - image.shape[1]) // 2 dy = (img_scaled.shape[0] - image.shape[0]) // 2 image = img_scaled[dy:image.shape[0], dx:image.shape[1]] logger.debug('image process+') humans = self.e.inference(image) logger.debug('postprocess+') image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) fps_time = 0 logger.debug('show+') cv2.putText(image, "FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.imshow('tf-pose-estimation result', image) fps_time = time.time() logger.debug('finished+')
def main(): parser = argparse.ArgumentParser( description='tf-pose-estimation run by folder') parser.add_argument('--folder', type=str, default='./images/') parser.add_argument('--resolution', type=str, default='432x368', help='network input resolution. default=432x368') parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin') parser.add_argument('--scales', type=str, default='[None]', help='for multiple scales, eg. [1.0, (1.1, 0.05)]') args = parser.parse_args() scales = ast.literal_eval(args.scales) w, h = model_wh(args.resolution) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) types = ('*.png', '*.jpg') files_grabbed = [] for files in types: files_grabbed.extend(glob.glob(os.path.join(args.folder, files))) all_humans = dict() if not os.path.exists('output'): os.mkdir('output') for i, file in enumerate(files_grabbed): # estimate human poses from a single image ! image = common.read_imgfile(file, None, None) t = time.time() humans = e.inference(image, scales=scales) elapsed = time.time() - t logger.info('inference image #%d: %s in %.4f seconds.' % (i, file, elapsed)) image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) # cv2.imshow('tf-pose-estimation result', image) filename = 'pose_{}.png'.format(i) output_filepath = os.path.join('output', filename) cv2.imwrite(output_filepath, image) logger.info('image saved: {}'.format(output_filepath)) # cv2.waitKey(5000) all_humans[file.replace(args.folder, '')] = humans with open(os.path.join(args.folder, 'pose.dil'), 'wb') as f: dill.dump(all_humans, f, protocol=dill.HIGHEST_PROTOCOL)
def joint(): try: data = request.data with open('/tmp/temp.jpg', 'wb') as f: f.write(data) img = common.read_imgfile('/tmp/temp.jpg', 432, 368) scales = ast.literal_eval(args.scales) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) humans = e.inference(img, scales=scales) image = TfPoseEstimator.draw_humans(img, humans, imgcopy=False) b_str = base64.b64encode(img2bytes( Image.fromarray(image))).decode('utf-8') return jsonify({"image": b_str}) except Exception as e: return jsonify({"error": str(traceback.format_exc())})
class Detector: def __init__(self, show=True): model = 'mobilenet_thin' w, h = model_wh('432x368') self.estimator = TfPoseEstimator(get_graph_path(model), target_size=(w, h)) self.show = show def detectCandidates(self, frame): cands = [] humans = self.estimator.inference(frame) image_h, image_w = frame.shape[:2] feat_list = [] for i in range(len(humans)): if i >= len(humans): break keys = humans[i].body_parts.keys() if len(np.setdiff1d(needed_elements, keys)): del humans[i] continue neck = humans[i].body_parts[1] lhip = humans[i].body_parts[8] rhip = humans[i].body_parts[11] center = (neck.x + lhip.x + rhip.x) / 3, (neck.y + lhip.y + rhip.y) / 3 feats = [] for idx in needed_elements: part = humans[i].body_parts[idx] feats = feats + [part.x - center[0], part.y - center[1]] feat_list.append(np.asarray(feats)) center = image_w * center[0], image_h * center[1] cv2.circle(frame, (int(center[0]), int(center[1])), 3, (255, 0, 0), 3) cands.append(np.asarray(center, dtype=np.float32)) # print feat_list[0] if (self.show): frame = TfPoseEstimator.draw_humans(frame, humans, imgcopy=False) return cands, feat_list, frame
def cnvt(img, name) : os.chdir(read_path) image = cv2.imread(img) os.chdir(old_path) model = 'mobilenet_thin' resolution = '432x368' w, h = model_wh(resolution) e = TfPoseEstimator(get_graph_path(model), target_size=(w, h)) humans = e.inference(image) blank_image = np.zeros((h,w,3), np.uint8) image = TfPoseEstimator.draw_humans(blank_image, humans, imgcopy=False) os.chdir(save_path) cv2.imwrite(name, image) print("Saved - %s As - %s" % (img, name)) os.chdir(old_path)
def generate_skeletonize_video(): """ The method takes images , save a skeleton per image and creates a video output :return: """ input_folder = "./videos/demo/" output_folder = "./videos" results_folder = "./images/demo/" input_video = "./videos/demo.mp4" images = video_utils.load_images_from_folder(input_folder) w = 432 h = 368 estimator = TfPoseEstimator(get_graph_path('mobilenet_thin'), target_size=(w, h)) count = 1 for i in images: image_parts = estimator.inference(i, scales=None) image_skeleton = TfPoseEstimator.draw_humans(i, image_parts, imgcopy=True) cv2.imwrite(r".\images\demo\{}.png".format(count), image_skeleton) count = count + 1 video_utils.create_video(input_video, results_folder, output_folder)
parser = argparse.ArgumentParser(description='tf-pose-estimation run') # Adding arguments to the programs parser.add_argument('--image', type=str, default='../images/p1.jpg') # Adding images name else it will take the default image parser.add_argument('--resolution', type=str, default='432x368', help='network input resolution. default=432x368') # Specify resolution parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin') # Specify Model parser.add_argument('--scales', type=str, default='[None]', help='for multiple scales, eg. [1.0, (1.1, 0.05)]') # Scales - Reason Unknown args = parser.parse_args() # Argument contain all the parse scales = ast.literal_eval(args.scales) w, h = model_wh(args.resolution) #Return width and height into w, h respectively after checking if its a multiple of 16 e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) # Model + width and height # estimate human poses from a single image ! image = common.read_imgfile(args.image, None, None) # image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21) t = time.time() humans = e.inference(image, scales=scales) elapsed = time.time() - t logger.info('inference image: %s in %.4f seconds.' % (args.image, elapsed)) image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) # cv2.imshow('tf-pose-estimation result', image) # cv2.waitKey() import matplotlib.pyplot as plt fig = plt.figure() a = fig.add_subplot(2, 2, 1) a.set_title('Result') plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
logger.debug('image preprocess+') if args.zoom < 1.0: canvas = np.zeros_like(image) img_scaled = cv2.resize(image, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (canvas.shape[1] - img_scaled.shape[1]) // 2 dy = (canvas.shape[0] - img_scaled.shape[0]) // 2 canvas[dy:dy + img_scaled.shape[0], dx:dx + img_scaled.shape[1]] = img_scaled image = canvas elif args.zoom > 1.0: img_scaled = cv2.resize(image, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (img_scaled.shape[1] - image.shape[1]) // 2 dy = (img_scaled.shape[0] - image.shape[0]) // 2 image = img_scaled[dy:image.shape[0], dx:image.shape[1]] logger.debug('image process+') humans = e.inference(image) logger.debug('postprocess+') image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) logger.debug('show+') cv2.putText(image, "FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.imshow('tf-pose-estimation result', image) fps_time = time.time() if cv2.waitKey(1) == 27: break logger.debug('finished+')
# get image form the camera ret_val, image = camera.read() # boilerplate canvas = np.zeros_like(image) img_scaled = cv2.resize(image, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (canvas.shape[1] - img_scaled.shape[1]) // 2 dy = (canvas.shape[0] - img_scaled.shape[0]) // 2 canvas[dy:dy + img_scaled.shape[0], dx:dx + img_scaled.shape[1]] = img_scaled image = canvas # feed image into the neural network humans = e.inference(image) # list of humans for id, human in enumerate(humans): Neck = 1 LWrist = 1 RWrist = 1 # this works ready for submission # i know it works and we did the screenshots ready to show #another change for co author for k, v in human.body_parts.items(): if POSE_COCO_BODY_PARTS[k] == "Neck": Neck = v.y elif POSE_COCO_BODY_PARTS[k] == "RWrist": RWrist = v.y elif POSE_COCO_BODY_PARTS[k] == "LWrist": LWrist = v.y
class Terrain(object): def __init__(self): """ Initialize the graphics window and mesh surface """ # Initialize plot. plt.ion() f = plt.figure(figsize=(5, 5)) f2 = plt.figure(figsize=(6, 5)) self.window3DBody = f.gca(projection='3d') self.window3DBody.set_title('3D_Body') self.windowStable = f2.add_subplot(1, 1, 1) self.windowStable.set_title('Stable') self.windowStable.set_xlabel('Time') self.windowStable.set_ylabel('Distant') self.windowStable.set_ylim([0, 1500]) #plt.show() self.times = [0] self.stable = [0] self.recordHead = [] self.fps_time = 0 model = 'mobilenet_thin_432x368' w, h = model_wh(model) #model = 'cmu' #w,h = 656,368 camera = 1 #1 mean external camera , 0 mean internal camera self.lines = {} self.connection = [[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], [5, 6], [0, 7], [7, 8], [8, 9], [9, 10], [8, 11], [11, 12], [12, 13], [8, 14], [14, 15], [15, 16]] self.e = TfPoseEstimator(get_graph_path(model), target_size=(w, h)) self.cam = cv2.VideoCapture(camera) ret_val, image = self.cam.read(cv2.IMREAD_COLOR) self.poseLifting = Prob3dPose( './src/lifting/models/prob_model_params.mat') self.statusBodyWindow = 0 try: keypoints = self.mesh(image) except: pass def mesh(self, image): image_h, image_w = image.shape[:2] width = 300 height = 300 pose_2d_mpiis = [] visibilities = [] zoom = 1.0 if zoom < 1.0: canvas = np.zeros_like(image) img_scaled = cv2.resize(image, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (canvas.shape[1] - img_scaled.shape[1]) // 2 dy = (canvas.shape[0] - img_scaled.shape[0]) // 2 canvas[dy:dy + img_scaled.shape[0], dx:dx + img_scaled.shape[1]] = img_scaled image = canvas elif zoom > 1.0: img_scaled = cv2.resize(image, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (img_scaled.shape[1] - image.shape[1]) // 2 dy = (img_scaled.shape[0] - image.shape[0]) // 2 image = img_scaled[dy:image.shape[0], dx:image.shape[1]] humans = self.e.inference(image, scales=[None]) package = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) image = package[0] status_part_body_appear = package[1] name_part_body = [ "Nose", "Neck", "RShoulder", "RElbow", "RWrist", "LShoulder", "LElbow", "LWrist", "RHip", "RKnee", "RAnkle", "LHip", "LKnee", "LAnkle", "REye", "LEye", "REar", "LEar", ] detected_part = [] for human in humans: pose_2d_mpii, visibility = common.MPIIPart.from_coco(human) pose_2d_mpiis.append([(int(x * width + 0.5), int(y * height + 0.5)) for x, y in pose_2d_mpii]) visibilities.append(visibility) cv2.putText( image, "FPS: %f [press 'q'to quit]" % (1.0 / (time.time() - self.fps_time)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) image = cv2.resize(image, (width, height)) cv2.imshow('tf-pose-estimation result', image) pose_2d_mpiis = np.array(pose_2d_mpiis) visibilities = np.array(visibilities) transformed_pose2d, weights = self.poseLifting.transform_joints( pose_2d_mpiis, visibilities) pose_3d = self.poseLifting.compute_3d(transformed_pose2d, weights) for i, single_3d in enumerate(pose_3d): #plot_pose(single_3d) plot_pose_adapt(single_3d, self.window3DBody) self.fps_time = time.time() #Matt plot lib #print(pose_3d) #---------------------------------------- #pyQT graph pose_3dqt = np.array(pose_3d[0]).transpose() bodyPartName = [ 'C_Hip', 'R_Hip', 'R_Knee', 'R_Ankle', 'L_Hip', 'L_Knee', 'L_Ankle', 'Center', 'C_Shoulder', 'Neck', 'Head', 'L_Shoulder', 'L_Elbow', 'L_Wrist', 'R_Shoulder', 'R_Elbow', 'R_Wrist' ] #for part in range(len(pose_3dqt)): # print(bodyPartName[part],pose_3dqt[part]) #for id_part in range(len(status_part_body_appear)): #check this part body appear or not # if status_part_body_appear[id_part] == 1: # print("%-10s"%name_part_body[id_part],": appear") # detected_part.append(id_part) #else: # print("%-10s"%name_part_body[id_part],": disappear") #list_to_check_fall_deteced = [[1,8] , #neck,RHIP # [1,9], #neck RKnee # [1,10], #neck RAnkle # [1,11], #neck LHip # [1,12], #neck LKne e # [1,13]] #neck LAnkle if int(self.fps_time) % 1 == 0: #every 1 second record self.times = self.times + [self.times[-1] + 1] if len(self.stable) > 1000: self.stable = self.stable[200:] self.recordHead = self.recordHead[200:] if self.stable == [0]: self.stable = self.stable + [0] self.recordHead = [pose_3dqt[10][2]] + [pose_3dqt[10][2]] else: #highest 800 , 550-600 average self.stable = self.stable + [ abs(pose_3dqt[10][2] - self.recordHead[-1]) ] self.recordHead = self.recordHead + [pose_3dqt[10][2]] status_found = 0 for id_part in detected_part: #if id_part in [8,9,10,11,12,13] and 1 in detected_part: # status_found = 1 if id_part in [8, 11] and 1 in detected_part: status_found = 1 if status_found: print("-------Ready for detece--------") if self.fall_detection(pose_3dqt): print("-----\nFOUND !!!\n-----") #---------- keypoints = pose_3d[0].transpose() return keypoints / 80 def fall_detection(self, pose_3dqt): print("VALUE Z : ", "NECK , C_HIP", ((pose_3dqt[10][2] - pose_3dqt[0][2])**2)**(1 / 2)) #print("VALUE Z : ","NECK , R_HIP",((pose_3dqt[10][2] - pose_3dqt[1][2])**2)**(1/2)) #print("VALUE Z : ","NECK , R_KNEE",((pose_3dqt[10][2] - pose_3dqt[2][2])**2)**(1/2)) #print("VALUE Z : ","NECK , R_ANKLE",((pose_3dqt[10][2] - pose_3dqt[3][2])**2)**(1/2)) #print("VALUE Z : ","NECK , L_HIP",((pose_3dqt[10][2] - pose_3dqt[4][2])**2)**(1/2)) #print("VALUE Z : ","NECK , L_KNEE",((pose_3dqt[10][2] - pose_3dqt[5][2])**2)**(1/2)) #print("VALUE Z : ","NECK , L_ANKLE",((pose_3dqt[10][2] - pose_3dqt[6][2])**2)**(1/2)) #NECK C_HIP if ((pose_3dqt[10][2] - pose_3dqt[0][2])**2)**(1 / 2) <= 200: return True #NECK R_HIP Z-graph #elif ((pose_3dqt[10][2] - pose_3dqt[1][2])**2)**(1/2) <= 200: # return True #NECK R_Knee #elif ((pose_3dqt[10][2] - pose_3dqt[2][2])**2)**(1/2) <= 200: # return True #NECK RAnkle #elif ((pose_3dqt[10][2] - pose_3dqt[3][2])**2)**(1/2) <= 200: # return True #NECK LHip #elif ((pose_3dqt[10][2] - pose_3dqt[4][2])**2)**(1/2) <= 200: # return True #NECK LKnee #elif ((pose_3dqt[10][2] - pose_3dqt[5][2])**2)**(1/2) <= 200: # return True #NECK LAnkle #elif ((pose_3dqt[10][2] - pose_3dqt[6][2])**2)**(1/2) <= 200: # return True return False def update(self): """ update the mesh and shift the noise each time """ ret_val, image = self.cam.read() try: keypoints = self.mesh(image) self.generateGraphStable() except AssertionError: print('body not in image') else: pass def generateGraphStable(self): plt.plot(self.times, self.stable) plt.pause(0.1) def start(self): """ get the graphics window open and setup """ #if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'): #QtGui.QApplication.instance().exec_() def animation(self): while True: self.update() if cv2.waitKey(1) == ord('q'): self.cam.release() cv2.destroyAllWindows() break
e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) result = [] for i, k in enumerate(tqdm(keys)): img_meta = cocoGt.loadImgs(k)[0] img_idx = img_meta['id'] img_name = os.path.join(image_dir, img_meta['file_name']) image = read_imgfile(img_name, None, None) if image is None: logger.error('image not found, path=%s' % img_name) sys.exit(-1) # inference the image with the specified network humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=args.resize_out_ratio) scores = 0 ann_idx = cocoGt.getAnnIds(imgIds=[img_idx], catIds=[1]) anns = cocoGt.loadAnns(ann_idx) for human in humans: item = { 'image_id': img_idx, 'category_id': 1, 'keypoints': write_coco_json(human, img_meta['width'], img_meta['height']), 'score': human.score
class Terrain(object): def __init__(self): """ Initialize the graphics window and mesh surface """ # setup the view window self.app = QtGui.QApplication(sys.argv) self.window = gl.GLViewWidget() self.window.setWindowTitle('Terrain') self.window.setGeometry(0, 110, 1920, 1080) self.window.setCameraPosition(distance=30, elevation=12) self.window.show() gx = gl.GLGridItem() gy = gl.GLGridItem() gz = gl.GLGridItem() gx.rotate(90, 0, 1, 0) gy.rotate(90, 1, 0, 0) gx.translate(-10, 0, 0) gy.translate(0, -10, 0) gz.translate(0, 0, -10) self.window.addItem(gx) self.window.addItem(gy) self.window.addItem(gz) model = 'mobilenet_thin_432x368' camera = 0 w, h = model_wh(model) self.e = TfPoseEstimator(get_graph_path(model), target_size=(w, h)) self.cam = cv2.VideoCapture(camera) ret_val, image = self.cam.read() self.poseLifting = Prob3dPose( './src/lifting/models/prob_model_params.mat') keypoints = self.mesh(image) self.points = gl.GLScatterPlotItem(pos=keypoints, color=pg.glColor((0, 255, 0)), size=15) self.window.addItem(self.points) def mesh(self, image): image_h, image_w = image.shape[:2] width = 640 height = 480 pose_2d_mpiis = [] visibilities = [] humans = self.e.inference(image, scales=[None]) for human in humans: pose_2d_mpii, visibility = common.MPIIPart.from_coco(human) pose_2d_mpiis.append([(int(x * width + 0.5), int(y * height + 0.5)) for x, y in pose_2d_mpii]) visibilities.append(visibility) pose_2d_mpiis = np.array(pose_2d_mpiis) visibilities = np.array(visibilities) transformed_pose2d, weights = self.poseLifting.transform_joints( pose_2d_mpiis, visibilities) pose_3d = self.poseLifting.compute_3d(transformed_pose2d, weights) keypoints = pose_3d[0].transpose() return keypoints / 80 def update(self): """ update the mesh and shift the noise each time """ ret_val, image = self.cam.read() try: keypoints = self.mesh(image) except AssertionError: print('body not in image') else: self.points.setData(pos=keypoints) def start(self): """ get the graphics window open and setup """ if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'): QtGui.QApplication.instance().exec_() def animation(self, frametime=10): """ calls the update method to run in a loop """ timer = QtCore.QTimer() timer.timeout.connect(self.update) timer.start(frametime) self.start()
def main(): parser = argparse.ArgumentParser(description='tf-pose-estimation run') parser.add_argument('--image', type=str, default='./images/p3.jpg') parser.add_argument( '--resolution', type=str, default='432x368', help='network input resolution. default=432x368') parser.add_argument( '--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin') parser.add_argument( '--scales', type=str, default='[None]', help='for multiple scales, eg. [1.0, (1.1, 0.05)]') parser.add_argument( '--stick_only', action='store_true', help='save output without other analysis') parser.add_argument('--plot_3d', action='store_true', help='save 3d poses') args = parser.parse_args() scales = ast.literal_eval(args.scales) w, h = model_wh(args.resolution) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) # estimate human poses from a single image ! image = common.read_imgfile(args.image, None, None) # image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21) t = time.time() humans = e.inference(image, scales=scales) elapsed = time.time() - t logger.info('inference image: %s in %.4f seconds.' % (args.image, elapsed)) image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) # cv2.imshow('tf-pose-estimation result', image) # cv2.waitKey() import matplotlib.pyplot as plt fig = plt.figure() a = fig.add_subplot(2, 2, 1) a.set_title('Result') rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if args.stick_only: cv2.imwrite('output/test.png', image) logger.info('image saved: {}'.format('output/test.png')) import sys sys.exit(0) plt.imshow(rgb_image) bgimg = cv2.cvtColor(image.astype(np.uint8), cv2.COLOR_BGR2RGB) bgimg = cv2.resize( bgimg, (e.heatMat.shape[1], e.heatMat.shape[0]), interpolation=cv2.INTER_AREA) # show network output a = fig.add_subplot(2, 2, 2) plt.imshow(bgimg, alpha=0.5) tmp = np.amax(e.heatMat[:, :, :-1], axis=2) plt.imshow(tmp, cmap=plt.cm.gray, alpha=0.5) plt.colorbar() tmp2 = e.pafMat.transpose((2, 0, 1)) tmp2_odd = np.amax(np.absolute(tmp2[::2, :, :]), axis=0) tmp2_even = np.amax(np.absolute(tmp2[1::2, :, :]), axis=0) a = fig.add_subplot(2, 2, 3) a.set_title('Vectormap-x') # plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5) plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5) plt.colorbar() a = fig.add_subplot(2, 2, 4) a.set_title('Vectormap-y') # plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5) plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5) plt.colorbar() if not os.path.exists('output'): os.mkdir('output') plt.savefig('output/test.png') logger.info('image saved: {}'.format('output/test.png')) if not args.plot_3d: import sys sys.exit(0) #For plotting in 3d logger.info('3d lifting initialization.') poseLifting = Prob3dPose('./src/lifting/models/prob_model_params.mat') image_h, image_w = image.shape[:2] standard_w = 640 standard_h = 480 pose_2d_mpiis = [] visibilities = [] for human in humans: pose_2d_mpii, visibility = common.MPIIPart.from_coco(human) pose_2d_mpiis.append([(int(x * standard_w + 0.5), int(y * standard_h + 0.5)) for x, y in pose_2d_mpii]) visibilities.append(visibility) pose_2d_mpiis = np.array(pose_2d_mpiis) visibilities = np.array(visibilities) transformed_pose2d, weights = poseLifting.transform_joints( pose_2d_mpiis, visibilities) pose_3d = poseLifting.compute_3d(transformed_pose2d, weights) for i, single_3d in enumerate(pose_3d): plot_pose(single_3d) plt.draw() plt.savefig('output/pose_3d_test.png') logger.info('3d plot saved: {}'.format('output/pose_3d_test.png'))
class Terrain(object): def __init__(self): """ Initialize the graphics window and mesh surface """ self.bitFalling = 0 # Initialize plot. self.times = [] self.recordVelocity = [] self.recordNeck = [] self.recordYTopRectangle = [] self.recordHIP = [] self.recordTimeList = [] self.globalTime = 0 self.highestNeck = 0 # self.hightestNeckTime = 0 self.highestHIP = 0 self.saveTimesStartFalling = -1 self.quoutaFalling = 0 self.surpriseMovingTime = -1 self.detectedHIP_Y = 0 self.detectedNECK_Y = 0 self.extraDistance = 0 self.fgbg = cv2.createBackgroundSubtractorMOG2(history=1, varThreshold=500, detectShadows=False) self.secondNeck = 0 self.human_in_frame = False self.lastTimesFoundNeck = -1 self.width = 300 self.height = 200 self.quotaVirtureNeck = 3 self.used_quotaVirtureNeck = 0 model = 'mobilenet_thin_432x368' w, h = model_wh(model) self.e = TfPoseEstimator(get_graph_path(model), target_size=(w, h)) self.recordAcceleration = [] def reduceRecord(self): self.recordNeck = self.recordNeck[-100:] self.recordHIP = self.recordHIP[-100:] self.times = self.times[-100:] self.recordVelocity = self.recordVelocity[-100:] self.recordTimeList = self.recordTimeList[-100:] self.recordYTopRectangle = self.recordYTopRectangle[-100:] self.recordAcceleration = self.recordAcceleration[-100:] def getLastRecordTime(self): if self.recordTimeList == []: return 0 return self.recordTimeList[-1] def addCountTimes(self): if self.times == []: self.times = self.times + [1] else: self.times = self.times + [self.times[-1] + 1] def addRecordTime(self, time): self.recordTimeList = self.recordTimeList + [time] def addRecordHIP(self, hip): self.recordHIP = self.recordHIP + [hip] def addRecordNeck(self, neck): self.recordNeck = self.recordNeck + [neck] def addRecordVelocity(self, neck, time): v = (abs(neck[-1] - neck[-2]) / abs(time[-1] - time[-2])) self.recordVelocity = self.recordVelocity + [int(v)] def destroyAll(self): self.times = [] self.recordNeck = [] self.recordHIP = [] self.recordTimeList = [] self.recordVelocity = [] self.recordAcceleration = [] self.recordYTopRectangle = [] self.quoutaFalling = 0 self.resetSurpriseMovingTime() self.resetBitFalling() def detecedFirstFalling(self): self.detectedNECK_Y = self.highestNeck self.detectedHIP_Y = self.highestHIP print( '-----!!!!falling!!!!!!----------------------------------------------' ) self.surpriseMovingTime = self.globalTime self.saveTimesStartFalling = self.times[-1] print('set extraDistance') self.extraDistance = (self.detectedHIP_Y - self.detectedNECK_Y) * (1 / 2) print('extraDis : ', self.extraDistance) print('set complete ') def countdownFalling(self): # print('StartTime From: ',self.surpriseMovingTime) print('!!!!!Countdown[10] : ', self.globalTime - self.surpriseMovingTime, '!!!!!------------') # print('would like to Cancel Countdown \nTake your neck to same level as NECK , HIP : ',self.detectedNECK_Y,self.detectedHIP_Y) # print('current your NECK : ',self.getLastNeck()) # print('extraTotal:',self.detectedHIP_Y+self.extraDistance) #maybe not Falling but make sure with NECK last must move up to this position # print('Check in second stage.') def resetSurpriseMovingTime(self): self.surpriseMovingTime = -1 def getLastNeck(self): return self.recordNeck[-1] def getLastTimes(self): return self.times[-1] def getSecondNeck(self): return self.secondNeck def getLastTimesFoundNeck(self): return self.lastTimesFoundNeck def addStatusFall(self, image): color = (0, 255, 0) if self.surpriseMovingTime != -1: color = (0, 0, 255) cv2.circle(image, (10, 10), 10, color, -1) def savesecondNeck(self, image): blur = cv2.GaussianBlur(image, (5, 5), 0) fgmask = self.fgbg.apply(blur) cnts = cv2.findContours(fgmask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if imutils.is_cv2() else cnts[1] x_left = -1 y_left = -1 x_right = -1 y_right = -1 for c in cnts: # if the contour is too small, ignore it # if cv2.contourArea(c) > 500: # continue # compute the bounding box for the contour, draw it on the frame, # and update the text (x, y, w, h) = cv2.boundingRect(c) if x_left == -1: x_left = x y_left = y if x < x_left: x_left = x if y < y_left: y_left = y if x + w > x_right: x_right = x + w if y + h > y_right: y_right = y + h if (x_left == 0 and y_left == 0 and x_right == self.width and y_right == self.height) == False: if self.human_in_frame and y_left != -1: # cv2.rectangle(image, (x_left, y_left), (x_right, y_right), (0, 255, 0), 2) self.secondNeck = y_left print('second Neck : ', self.secondNeck) self.recordYTopRectangle = self.recordYTopRectangle + [ self.secondNeck ] # cv2.imshow('na',fgmask) def processFall(self, image): print('processing falling ---------') totalTime = 0 loop = 1 for i in range(1, len(self.recordTimeList)): totalTime += self.recordTimeList[-i] - self.recordTimeList[-(i + 1)] loop += 1 if totalTime >= 1: break print('totalTime(velocity):', totalTime, loop) if len(self.recordVelocity) >= 2: #calculate acceleration ac = (max(self.recordVelocity[-loop:]) - min( self.recordVelocity[-loop:])) / abs(self.recordTimeList[-1] - self.recordTimeList[-loop]) self.recordAcceleration += [ac] print('acceleration :', self.recordAcceleration[-loop:]) print('highestNeck', self.highestNeck) print('highestHIP', self.highestHIP) print('time duration : ', (self.recordTimeList[-1] - self.recordTimeList[-2])) print('max-Velocity :', max(self.recordVelocity[-loop:])) print('velocityCurrent:', self.recordVelocity[-loop:]) if self.highestHIP != 0 and len( self.recordNeck) > 1 and self.surpriseMovingTime == -1: #NECK new Y point > NECK lastest Y point falling #high , y low || low , y high print('LAST_NECK', self.getLastNeck(), 'HIGHTEST_HIP', self.highestHIP) #get max human's velocity in last 1 second vHumanFall = max(self.recordVelocity[-loop:]) t = self.recordTimeList[-1] #get minimum time per frame in last 1 second for i in range(1, loop): if abs(self.recordTimeList[-i] - self.recordTimeList[-(i + 1)]) < t: t = abs(self.recordTimeList[-i] - self.recordTimeList[-(i + 1)]) print(max(self.recordAcceleration[-loop:]), ((self.highestHIP - self.highestNeck) / (t**2))) # Max velcity vM = (self.highestHIP - self.highestNeck) / t # Max acceleration aM = ( (self.highestHIP - self.highestNeck) / t) / abs(self.recordTimeList[-1] - self.recordTimeList[-loop]) i = 0.3 print((vHumanFall / vM) * (1 - i) + i * (max(self.recordAcceleration[-loop:]) / (aM)), '> 0.35 ??') if self.getLastNeck() < self.highestHIP: self.quoutaFalling = 0 if self.getLastNeck( ) >= self.highestHIP and self.quoutaFalling < 2: print('~~falling~~') self.quoutaFalling += 1 # final equation after normalized and weight wA at 0.3 if ((vHumanFall / vM) * (1 - i) + i * (max(self.recordAcceleration[-loop:]) / (aM))) > 0.35: #0.4 self.detecedFirstFalling() elif self.surpriseMovingTime != -1: self.countdownFalling() if self.globalTime - self.surpriseMovingTime >= 2 and ( self.getLastNeck() <= (self.detectedHIP_Y - self.extraDistance)): print('Recover From STATE') print('---------------------------------------') self.destroyAll() #in 10 second person not movig up --> FALL DETECTED elif self.globalTime - self.surpriseMovingTime >= 10: print('Warning : Falling happening') self.setFalling() def mesh(self, image): image = common.read_imgfile(image, None, None) image = cv2.resize(image, (self.width, self.height)) print('start-inderence', time.time()) humans = self.e.inference(image, scales=[None]) print('end-inderence', time.time()) self.resetBitFalling() self.savesecondNeck(image) package = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) self.globalTime = time.time() #time of after drawing image = package[0] #status_part_body_appear = package[1] center_each_body_part = package[2] #camera not found NECK more than 10 second then reset list if self.globalTime - self.getLastRecordTime() >= 12: print('RESET STABLE,RECORDNECK,HIP,etc. [complete 12 second]') self.destroyAll() if self.globalTime - self.getLastRecordTime() >= 2: print('maybe NECK or HUMAN not found [complete 2 second]') self.human_in_frame = False print('end Initialize mesh') # print(status_part_body_appear) #when draw2D stick man # name_part_body = ["Nose", # 0 # "Neck", # 1 # "RShoulder", # 2 # "RElbow", # 3 # "RWrist", # 4 # "LShoulder", # 5 # "LElbow", # 6 # "LWrist", # 7 # "RHip", # 8 # "RKnee", # 9 # "RAnkle", # 10 # "LHip", # 11 # "LKnee", # 12 # "LAnkle", # 13 # "REye", # 14 # "LEye", # 15 # "REar", # 16 # "LEar", # 17 # ] # detected_part = [] self.addRecordTime(self.globalTime) print('start record everything') if 1 in center_each_body_part: # print(self.globalTime - self.getLastRecordTime()) self.addCountTimes() self.human_in_frame = True self.lastTimesFoundNeck = self.recordTimeList[-1] self.used_quotaVirtureNeck = 0 self.addRecordNeck(center_each_body_part[1][1]) if len(self.recordNeck) >= 2: self.addRecordVelocity(self.recordNeck, self.recordTimeList) if 11 in center_each_body_part: self.addRecordHIP(center_each_body_part[11][1]) print('neck :| HIP: ', self.recordHIP[-1] - self.recordNeck[-1]) elif 8 in center_each_body_part: self.addRecordHIP(center_each_body_part[8][1]) print('neck :| HIP: ', self.recordHIP[-1] - self.recordNeck[-1]) elif self.used_quotaVirtureNeck < self.quotaVirtureNeck and self.secondNeck >= self.getLastNeck( ): # print(self.globalTime - self.getLastRecordTime()) self.addCountTimes() self.lastTimesFoundNeck = self.recordTimeList[-1] self.addRecordNeck(self.getSecondNeck()) if len(self.recordNeck) >= 2: self.addRecordVelocity(self.recordNeck, self.recordTimeList) print('addSecond Neck', self.used_quotaVirtureNeck) self.used_quotaVirtureNeck += 1 if len(self.recordNeck) > 300: self.reduceRecord() # print('find highest neck , hip') totalTime = 0 loop = 1 for i in range(1, len(self.recordTimeList)): totalTime += self.recordTimeList[-i] - self.recordTimeList[-(i + 1)] loop += 1 if totalTime >= 2: break print('totalTime:', totalTime, loop) minNumber = -1 if len(self.recordNeck) < loop: loop = len(self.recordNeck) for i in range(1, loop + 1): if minNumber == -1 or self.recordNeck[-i] <= minNumber: self.highestNeck = self.recordNeck[ -i] #more HIGH more low value # self.highestNeckTime = self.recordTimeList[-i] minNumber = self.recordNeck[-i] if len(self.recordHIP) > 1: #11 L_HIP if 11 in center_each_body_part: self.highestHIP = min(self.recordHIP[-loop:]) #8 R_HIP elif 8 in center_each_body_part: self.highestHIP = min(self.recordHIP[-loop:]) if len(self.recordNeck) > 1: self.processFall(image) print('end processing falling end mash()') def setFalling(self): self.bitFalling = 1 def getBitFalling(self): return self.bitFalling def resetBitFalling(self): self.bitFalling = 0
class dataScriptGenerator(object): def __init__(self): self.parser = argparse.ArgumentParser( description='tf-pose-estimation run') self.parser.add_argument( '--resolution', type=str, default='432x368', help='network input resolution. default=432x368') self.parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin') self.parser.add_argument( '--scales', type=str, default='[None]', help='for multiple scales, eg. [1.0, (1.1, 0.05)]') self.args = self.parser.parse_args() self.scales = ast.literal_eval(self.args.scales) self.w, self.h = model_wh(self.args.resolution) self.e = TfPoseEstimator(get_graph_path(self.args.model), target_size=(self.w, self.h)) # This method is called to return all humans found in images within the OurTest images folder # Generates a csv file with the latest human skeleton keypoint data # Also plots each skeleton onto the images def adHocData(self): directory_in_str = sys.path[0] + "\\..\\images\\OurTest\\" # Delete old csv files try: os.remove(outputfile) os.remove(cleanedOutputfile) except OSError: pass # Run through every image in the folder for file in os.listdir(directory_in_str): filename = os.fsdecode(file) if filename.endswith(".jpg") or filename.endswith(".png"): fullpath = directory_in_str + filename # Estimate human poses from a single image ! image = common.read_imgfile(fullpath, None, None) # image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21) t = time.time() humans = self.e.inference(image, scales=self.scales) elapsed = time.time() - t logger.info('inference image: %s in %.4f seconds.' % (fullpath, elapsed)) image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) # cv2.imshow('tf-pose-estimation result', image) # cv2.waitKey() myFile = open(outputfile, 'a') # myFile.write(str(filename) + ',') # print(filename) myFile.write('\n') myFile.close() # Attempt to plot our skeletons onto the image try: fig = plt.figure() a = fig.add_subplot(2, 2, 1) a.set_title('Result') plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) bgimg = cv2.cvtColor(image.astype(np.uint8), cv2.COLOR_BGR2RGB) bgimg = cv2.resize( bgimg, (self.e.heatMat.shape[1], self.e.heatMat.shape[0]), interpolation=cv2.INTER_AREA) # show network output a = fig.add_subplot(2, 2, 2) plt.imshow(bgimg, alpha=0.5) tmp = np.amax(self.e.heatMat[:, :, :-1], axis=2) plt.imshow(tmp, cmap=plt.cm.gray, alpha=0.5) plt.colorbar() tmp2 = self.e.pafMat.transpose((2, 0, 1)) tmp2_odd = np.amax(np.absolute(tmp2[::2, :, :]), axis=0) tmp2_even = np.amax(np.absolute(tmp2[1::2, :, :]), axis=0) a = fig.add_subplot(2, 2, 3) a.set_title('Vectormap-x') # plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5) plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5) plt.colorbar() a = fig.add_subplot(2, 2, 4) a.set_title('Vectormap-y') # plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5) plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5) plt.colorbar() plt.show() logger.info('3d lifting initialization.') poseLifting = Prob3dPose( sys.path[0] + '\\lifting\\models\\prob_model_params.mat') image_h, image_w = image.shape[:2] standard_w = 640 standard_h = 480 pose_2d_mpiis = [] visibilities = [] for human in humans: pose_2d_mpii, visibility = common.MPIIPart.from_coco( human) pose_2d_mpiis.append([(int(x * standard_w + 0.5), int(y * standard_h + 0.5)) for x, y in pose_2d_mpii]) visibilities.append(visibility) pose_2d_mpiis = np.array(pose_2d_mpiis) visibilities = np.array(visibilities) transformed_pose2d, weights = poseLifting.transform_joints( pose_2d_mpiis, visibilities) pose_3d = poseLifting.compute_3d(transformed_pose2d, weights) for i, single_3d in enumerate(pose_3d): plot_pose(single_3d) plt.show() except: print("Error when plotting image ") dataScriptGenerator.dataCleanup(self) # This method is called to return all humans found in the latest image within LiveTest folder # Generates a csv file with the human skeleton keypoint data # Does not plot skeletons onto images, this is as basic and optimized as possible def liveData(self): directory_in_str = sys.path[0] + r"/../images/LiveTest/" try: os.remove(outputfile) os.remove(cleanedOutputfile) except OSError: pass for file in os.listdir(directory_in_str): filename = os.fsdecode(file) if filename.endswith(".jpg") or filename.endswith(".png"): fullpath = directory_in_str + filename # estimate human poses from a single image ! image = common.read_imgfile(fullpath, None, None) humans = self.e.inference(image, scales=self.scales) image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) # cv2.imshow('tf-pose-estimation result', image) # cv2.waitKey() myFile = open(outputfile, 'a') # myFile.write(str(filename) + ',') # print(filename) myFile.write('\n') # break myFile.close() # Cleans the generated csv file, removing data which has one or less knee keypoints missing def dataCleanup(self): keypointData = open(outputfile, 'r') keypointReader = csv.reader(keypointData) cleanKeypointData = open(cleanedOutputfile, 'w', newline='') cleanKeypointWriter = csv.writer(cleanKeypointData) for row in keypointReader: badKeypointCount = 0 # 16 is the start of our x y pairs for L and R hip, knee and ankle keypoints for keypointIndex in range(16, 28, 2): if (row[keypointIndex] == "-1"): badKeypointCount += 1 if (badKeypointCount < 4): # Good data, lets write it to our new clean file cleanKeypointWriter.writerow(row[:len(row) - 1]) keypointData.close() cleanKeypointData.close()
try: while 1: start = time.time() # 用于计算帧率信息 length = recvall(conn, 16) # 获得图片文件的长度,16代表获取长度 stringData = recvall(conn, int(length)) # 根据获得的文件长度,获取图片文件 data = numpy.frombuffer(stringData, numpy.uint8) # 将获取到的字符流数据转换成1维数组 image = cv2.imdecode(data, cv2.IMREAD_COLOR) # 将数组解码成图像 print(image) width = int(image.shape[1]) height = int(image.shape[0]) # print(width,height) ##2D姿态识别 humans = model.inference(image) if not args.showBG: image = np.zeros(image.shape) scales = ast.literal_eval(node_or_string='[None]') humans = model.inference(image, scales=scales) image = model.draw_humans(image, humans, imgcopy=False) # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) cv2.putText(image, "FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) # 生成2D姿态视频 video.write(image) ##2D-->3D poseLifting = Prob3dPose('./lifting/models/prob_model_params.mat') image_h, image_w = image.shape[:2]
def post(self): global fallen if ((self.request.headers['Content-Type'] == 'imagebin')): print('Received image') image = self.request.body fh = open('static/image1.jpg', 'wb') fh.write(image) fh.close() #fh = open('static/image1.jpg','ab') #fh.write(bytes([0xD9])) #fh.close() print('0') #image = cv2.imread('static/image1.jpg') print('1') print('2') parser = argparse.ArgumentParser( description='tf-pose-estimation run') parser.add_argument( '--resolution', type=str, default='432x368', help='network input resolution. default=432x368') parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin') parser.add_argument( '--scales', type=str, default='[None]', help='for multiple scales, eg. [1.0, (1.1, 0.05)]') args = parser.parse_args() scales = ast.literal_eval(args.scales) w, h = model_wh(args.resolution) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) # estimate human poses from a single image ! image = common.read_imgfile('static/image1.jpg', None, None) # image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21) t = time.time() humans = e.inference(image, scales=scales) elapsed = time.time() - t logger.info('inference image: image3.jpg in %.4f seconds.' % (elapsed)) image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) fallen = 'OKAY' for i, h in enumerate(humans): TORSO_INDEX = 1 LEFT_HIP_INDEX = 8 RIGHT_HIP_INDEX = 11 RIGHT_HAND_INDEX = 4 RIGHT_FOOT_INDEX = 12 LEFT_FOOT_INDEX = 9 # and RIGHT_HAND_INDEX in parts and RIGHT_FOOT_INDEX in parts and LEFT_FOOT_INDEX in parts: parts = h.body_parts if TORSO_INDEX in parts and LEFT_HIP_INDEX in parts and RIGHT_HIP_INDEX in parts: torso = parts[TORSO_INDEX] left_hip = parts[LEFT_HIP_INDEX] right_hip = parts[RIGHT_HIP_INDEX] tx, ty = torso.x, torso.y lhx, lhy = left_hip.x, left_hip.y rhx, rhy = right_hip.x, right_hip.y if tx < lhx or tx > rhx: fallen = 'FALL' if abs(lhy - ty) < 0.1 or abs(rhy - ty) < 0.1: fallen = 'FALL' if RIGHT_HAND_INDEX in parts and RIGHT_FOOT_INDEX in parts and LEFT_FOOT_INDEX in parts: right_foot = parts[RIGHT_FOOT_INDEX] left_foot = parts[LEFT_FOOT_INDEX] right_hand = parts[RIGHT_HAND_INDEX] righax, righay = right_hand.x, right_hand.y rfx, rfy = right_foot.x, right_foot.y lfx, lfy = left_foot.x, left_foot.y if abs(lfy - lhy) < 0.1 or abs(rhy - ty) < 0.1: fallen = 'FALL' if (lfy - lhy) > (lhy - ty): fallen = 'FALL' print(lfy - lhy, lhy - ty) lowermag = math.sqrt((lfy - lhy) * (lfy - lhy) + (lhx - lfx) * (lhx - lfx)) uppermag = math.sqrt((lhy - ty) * (lhy - ty) + (tx - lhx) * (tx - lhx)) if lowermag > uppermag: fallen = 'FALL' #cv2.putText(image, # f"Fallen: False", # (60, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, # (0, 255, 0), 5) cv2.putText(image, f"Fallen: {fallen}", (60, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 5) cv2.imwrite('static/image3.jpg', image) for client in clients: update_clients(client)
userToken = q["userToken"] taskList = int(q["taskList"]) db.hset(imageID, "userToken", userToken) db.hset(imageID, "image", image) # 此处的image是 base64格式的 raw picture img_np = data_uri_to_cv2_img(image) print("face recognition %d" % i) # 2. open pose deals imgs: # # (1) get result picture # humans = e.inference(img_np, scales=scales) # imageRuselt = TfPoseEstimator.draw_humans(img_np, humans, imgcopy=False) #mat格式 # (2) get result json humans = e.inference(img_np, scales=scales) # imageRuseltDic = TfPoseEstimator.draw_humans(img_np, humans, imgcopy=False) frozen = jsonpickle.encode( humans, unpicklable=False ) # 序列化human对象并存入,humans是一个human对象的list,human对象里面包含BordParts, 其中有每个点的信息的x,y坐标值 # # mat转base64, without 'data:image/jpeg;base64,' this head # imageRuselt1 = cv2.imencode('.jpg', imageRuselt)[1] # base64_data = base64.b64encode(imageRuselt1) # db.hset(imageID, "image", base64_data) # 此处的image是 base64格式的 db.hset(imageID, "poseResult", frozen ) # 将识别的结果poseResult (不是图片是json数据,后面连图片一起取出来标注)存入 hashset taskDone = db.hget(
dx = (canvas.shape[1] - img_scaled.shape[1]) // 2 dy = (canvas.shape[0] - img_scaled.shape[0]) // 2 canvas[dy:dy + img_scaled.shape[0], dx:dx + img_scaled.shape[1]] = img_scaled image_rgb = canvas elif args.zoom > 1.0: img_scaled = cv2.resize(image_rgb, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (img_scaled.shape[1] - image_rgb.shape[1]) // 2 dy = (img_scaled.shape[0] - image_rgb.shape[0]) // 2 image1 = img_scaled[dy:image1.shape[0], dx:image_rgb.shape[1]] humans = e.inference(image_rgb) image_rgb = TfPoseEstimator.draw_humans(image_rgb, humans, imgcopy=False) if humans: # distance values call (humans list length = people). # Nose = 0 # Neck = 1 # RShoulder = 2 # RElbow = 3 # RWrist = 4 # LShoulder = 5 # LElbow = 6 # LWrist = 7 # RHip = 8
def pose_comparison(): parser = argparse.ArgumentParser(description='tf-pose-estimation run') parser.add_argument('--image', type=str, default='./images/p1.jpg') parser.add_argument('--resolution', type=str, default='432x368', help='network input resolution. default=432x368') parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin') parser.add_argument('--scales', type=str, default='[None]', help='for multiple scales, eg. [1.0, (1.1, 0.05)]') args = parser.parse_args() scales = ast.literal_eval(args.scales) w, h = model_wh(args.resolution) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) ref_image = common.read_imgfile(REF_POSE_PATH, None, None) ref_image = cv2.resize(ref_image, (640, 480)) # estimate human poses from a single image ! image = common.read_imgfile(args.image, None, None) image = cv2.resize(image, (640, 480)) # image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21) #t = time.time() ref_humans = e.inference(ref_image, scales=scales) humans = e.inference(image, scales=scales) #elapsed = time.time() - t #logger.info('inference image: %s in %.4f seconds.' % (args.image, elapsed)) _, ref_centers = TfPoseEstimator.draw_humans_mine(ref_image, ref_humans, imgcopy=False) _, centers = TfPoseEstimator.draw_humans_mine(image, humans, imgcopy=False) ref_centers = list(ref_centers.values()) centers = list(centers.values()) ref_centers = list(sum(ref_centers, ())) centers = list(sum(centers, ())) ref_centers = np.array(ref_centers, dtype=int) centers = np.array(centers, dtype=int) shapes = [] shapes.append(ref_centers) shapes.append(centers) #create canvas on which the triangles will be visualized canvas = np.full([640, 480], 255).astype('uint8') #convert to 3 channel RGB for fun colors! canvas = cv2.cvtColor(canvas, cv2.COLOR_GRAY2RGB) #im = draw_shapes(canvas,shapes) x, y = get_translation(shapes[0]) new_shapes = [] new_shapes.append(shapes[0]) for i in range(1, len(shapes)): new_shape = procrustes_analysis(shapes[0], shapes[i]) new_shape[::2] = new_shape[::2] + x new_shape[1::2] = new_shape[1::2] + y new_shape = new_shape.astype(int) new_shapes.append(new_shape) pts_list = [] for lst in new_shapes: temp = lst.reshape(-1, 1, 2) pts = list(map(tuple, temp)) pts_list.append(pts) for i in range(18): cv2.circle(ref_image, tuple(pts_list[0][i][0]), 3, (255, 0, 0), thickness=3, lineType=8, shift=0) cv2.circle(ref_image, tuple(pts_list[1][i][0]), 3, (255, 255, 0), thickness=3, lineType=8, shift=0) cv2.imshow('tf-pose-estimation result', ref_image) cv2.waitKey(0) variations = [] for i in range(len(new_shapes)): dist = procrustes_distance(shapes[0], new_shapes[i]) variations.append(dist) print(variations)
parser.add_argument('--showBG', type=bool, default=True, help='False to show skeleton only.') args = parser.parse_args() w, h = 432, 368 e = TfPoseEstimator('graph/{}/graph_freeze.pb'.format(args.model), target_size=(w, h)) cap = cv2.VideoCapture(args.video) if cap.isOpened() is False: print("Error opening video stream or file") while cap.isOpened(): ret_val, image = cap.read() tic = time.time() humans = e.inference(image, resize_to_default=True, upsample_size=4.0) if not args.showBG: image = np.zeros(image.shape) res = TfPoseEstimator.draw_humans(image, humans, imgcopy=True) cv2.putText(res, "FPS: %f" % (1.0 / (time.time() - tic)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.imshow('rr', res) toc = time.time() logger.info('inference %.4f seconds.' % (toc - tic)) if cv2.waitKey(1) == 27: break cv2.destroyAllWindows() logger.debug('finished+')
def pose_dd(): fps_time = 0 global humans global BREAK global image parser = argparse.ArgumentParser(description='tf-pose-estimation realtime webcam') parser.add_argument('--camera', type=int, default=0) parser.add_argument('--zoom', type=float, default=1.0) parser.add_argument('--model', type=str, default='mobilenet_thin_432x368', help='cmu_640x480 / cmu_640x360 / mobilenet_thin_432x368') parser.add_argument('--show-process', type=bool, default=False, help='for debug purpose, if enabled, speed for inference is dropped.') args = parser.parse_args() ##logger.debug('initialization %s : %s' % (args.model, get_graph_path(args.model))) w, h = model_wh(args.model) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) #logger.debug('cam read+') cam = cv2.VideoCapture(args.camera) ret_val, image = cam.read() ##logger.info('cam image=%dx%d' % (image.shape[1], image.shape[0])) while True: ret_val, image = cam.read() #logger.debug('image preprocess+') if args.zoom < 1.0: canvas = np.zeros_like(image) img_scaled = cv2.resize(image, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (canvas.shape[1] - img_scaled.shape[1]) // 2 dy = (canvas.shape[0] - img_scaled.shape[0]) // 2 canvas[dy:dy + img_scaled.shape[0], dx:dx + img_scaled.shape[1]] = img_scaled image = canvas elif args.zoom > 1.0: img_scaled = cv2.resize(image, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (img_scaled.shape[1] - image.shape[1]) // 2 dy = (img_scaled.shape[0] - image.shape[0]) // 2 image = img_scaled[dy:image.shape[0], dx:image.shape[1]] #logger.debug('image process+') humans = e.inference(image) #logger.debug('postprocess+') image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) #logger.debug('show+') cv2.putText(image, "FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.imshow('tf-pose-estimation result', image) fps_time = time.time() #out.write(image) if cv2.waitKey(1) == 27: #out.release() cv2.destroyAllWindows() BREAK = True clientsocket.send("off".encode('utf-8')) print("off sent") import sys sys.exit(0) break
# parser.add_argument('--image', type=str, default='/Users/ildoonet/Downloads/me.jpg') parser.add_argument('--image', type=str, default='./images/apink2.jpg') # parser.add_argument('--model', type=str, default='mobilenet_320x240', help='cmu / mobilenet_320x240') parser.add_argument('--model', type=str, default='mobilenet_thin_432x368', help='cmu_640x480 / cmu_640x360 / mobilenet_thin_432x368') parser.add_argument('--scales', type=str, default='[None]', help='for multiple scales, eg. [1.0, (1.1, 0.05)]') args = parser.parse_args() scales = ast.literal_eval(scales) w, h = model_wh(args.model) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) # estimate human poses from a single image ! image = common.read_imgfile(args.image, None, None) # image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21) t = time.time() humans = e.inference(image, scales=[None]) # humans = e.inference(image, scales=[None, (0.7, 0.5, 1.8)]) # humans = e.inference(image, scales=[(1.2, 0.05)]) # humans = e.inference(image, scales=[(0.2, 0.2, 1.4)]) elapsed = time.time() - t logger.info('inference image: %s in %.4f seconds.' % (args.image, elapsed)) image = cv2.imread(args.image, cv2.IMREAD_COLOR) image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) cv2.imshow('tf-pose-estimation result', image) cv2.waitKey() import sys sys.exit(0)
default='mobilenet_thin_432x368', help='cmu_640x480 / cmu_640x360 / mobilenet_thin_432x368') parser.add_argument('--scales', type=str, default='[None]', help='for multiple scales, eg. [1.0, (1.1, 0.05)]') args = parser.parse_args() w, h = model_wh(args.model) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) # estimate human poses from a single image ! image = common.read_imgfile(args.image, None, None) # image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21) t = time.time() humans = e.inference(image, scales=[None]) # humans = e.inference(image, scales=[None, (0.7, 0.5, 1.8)]) # humans = e.inference(image, scales=[(1.2, 0.05)]) # humans = e.inference(image, scales=[(0.2, 0.2, 1.4)]) elapsed = time.time() - t logger.info('inference image: %s in %.4f seconds.' % (args.image, elapsed)) image = cv2.imread(args.image, cv2.IMREAD_COLOR) image, b = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) cv2.imshow('tf-pose-estimation result', image) cv2.waitKey() logger.info('3d lifting initialization.') poseLifting = Prob3dPose('./src/lifting/models/prob_model_params.mat')
dy = (canvas.shape[0] - img_scaled.shape[0]) // 2 canvas[dy:dy + img_scaled.shape[0], dx:dx + img_scaled.shape[1]] = img_scaled image = canvas elif args.zoom > 1.0: img_scaled = cv2.resize(image, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (img_scaled.shape[1] - image.shape[1]) // 2 dy = (img_scaled.shape[0] - image.shape[0]) // 2 image = img_scaled[dy:image.shape[0], dx:image.shape[1]] #logger.debug('image process+') humans = e.inference(image) #logger.debug('postprocess+') #image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) #logger.debug('show+') cv2.putText(image, text_display, (100, 400), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 255, 255), 3) cv2.putText(image, "|", (250, 400), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 255, 255), 3) image = cv2.resize(image, (800, 600)) cv2.imshow('tf-pose-estimation result', image) fps_time = time.time() if cv2.waitKey(1) == 27:
def main(): global fps_time if len(sys.argv) != 2: print("Please specify path to .svo file.") exit() filepath = sys.argv[1] ite = loadall("pickle.dat") print("Reading SVO file: {0}".format(filepath)) t = time.time() init = zcam.PyInitParameters(svo_input_filename=filepath, svo_real_time_mode=False) init.depth_mode = sl.PyDEPTH_MODE.PyDEPTH_MODE_QUALITY cam = zcam.PyZEDCamera() status = cam.open(init) if status != tp.PyERROR_CODE.PySUCCESS: print(repr(status)) exit() runtime = zcam.PyRuntimeParameters() mat = core.PyMat() depth = core.PyMat() print('Initilisation of svo took ', time.time() - t, ' seconds') key = '' graph_path = "./models/graph/mobilenet_thin/graph_opt.pb" #"./models/graph/cmu/graph_opt.pb" target = (432, 368) #(656,368) (432,368) (1312,736) e = TfPoseEstimator(graph_path, target) nbf = 0 nbp = 0 print(" Save the current image: s") print(" Quit the video reading: q\n") while key != 113: # for 'q' key t = time.time() err = cam.grab(runtime) if err == tp.PyERROR_CODE.PySUCCESS: retrieve_start = time.time() cam.retrieve_image(mat) cam.retrieve_measure(depth, sl.PyMEASURE.PyMEASURE_XYZRGBA) image = mat.get_data() retrieve_end = time.time() print('Retrieving of svo data took ', retrieve_end - retrieve_start, ' seconds') pt = np.array(depth.get_data()[:, :, 0:3], dtype=np.float64) print(pt.shape, image.shape) pt[np.logical_not(np.isfinite(pt))] = 0.0 nbf += 1 pose_start = time.time() humans = e.inference(np.array(image[:, :, 0:3])) image_h, image_w = image.shape[:2] pose_end = time.time() print("Inference took", pose_end - pose_start, 'seconds') for pid, human in enumerate(humans): for kid, bdp in enumerate(human.body_parts.values()): #print(bdp) #print(kid, bdp.x, bdp.y, bdp.score) if (bdp.score > 5.0): print((int(bdp.x * image_w + 0.5), int(bdp.y * image_h + 0 / 5))) print(pt[int(bdp.y * image_h + 0 / 5), int(bdp.x * image_w + 0.5)]) nbp += 1 cv2.circle(image, (int(bdp.x * image_w + 0.5), int(bdp.y * image_h + 0 / 5)), 5, (255, 255, 255), thickness=5, lineType=8, shift=0) #coord_uv[pid,kid,:]=np.array([int(bdp.x*image_w+0.5),int(bdp.y*image_h+0/5)]) #coord_vis[pid,kid]=bdp.score/10 image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) cv2.putText(image, "FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) #print(time.time()-t) cv2.imshow('tf-pose-estimation result', image) fps_time = time.time() key = cv2.waitKey(1) saving_image(key, mat) #time.sleep(0.033) else: key = cv2.waitKey(1) cv2.destroyAllWindows() print(nbp / nbf, (nbp / nbf) / 18) #saving_depth(cam) #saving_point_cloud(cam) cam.close() print("\nFINISH")
def post(self): global finished_post if ((self.request.headers['Content-Type'] == 'imagebin') and (finished_post == 1)): print(finished_post) finished_post = 0 print('Received image') image = self.request.body fh = open('static/image1.jpg', 'wb') fh.write(image) fh.close() #fh = open('static/image1.jpg','ab') #fh.write(bytes([0xD9])) #fh.close() print('0') #image = cv2.imread('static/image1.jpg') print('1') print('2') parser = argparse.ArgumentParser( description='tf-pose-estimation run') parser.add_argument( '--resolution', type=str, default='432x368', help='network input resolution. default=432x368') parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin') parser.add_argument( '--scales', type=str, default='[None]', help='for multiple scales, eg. [1.0, (1.1, 0.05)]') args = parser.parse_args() scales = ast.literal_eval(args.scales) w, h = model_wh(args.resolution) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) # estimate human poses from a single image ! image = common.read_imgfile('static/image1.jpg', None, None) # image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21) t = time.time() humans = e.inference(image, scales=scales) elapsed = time.time() - t logger.info('inference image: image3.jpg in %.4f seconds.' % (elapsed)) image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) #cv2.putText(image, # f"Fallen: False", # (60, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, # (0, 255, 0), 5) cv2.imwrite('static/image3.jpg', image) for client in clients: update_clients(client) print(finished_post) finished_post = 1
def doCNNTracking(args): global is_tracking,logger fps_time = 0 logger.debug('initialization %s : %s' % (args.model, get_graph_path(args.model))) w, h = model_wh(args.resolution) e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) logger.debug('cam read+') pipeline = rs.pipeline() pipeline.start() frames = pipeline.wait_for_frames() #get depth影像 depth = frames.get_depth_frame() depth_image_data = depth.as_frame().get_data() depth_image = np.asanyarray(depth_image_data) logger.info('cam depth image=%dx%d' % (depth_image.shape[1], depth_image.shape[0])) logger.info('camera ready') #計算depth影像對應至rgb影像的clip clip_box = [100,-100,290,-300] while (True): if(is_tracking): fps_time = time.time() frames = pipeline.wait_for_frames() #get rgb影像 image_frame = frames.get_color_frame() image_data = image_frame.as_frame().get_data() image = np.asanyarray(image_data) #change bgr 2 rgb image = np.array(image[...,::-1]) origen_image = image #get depth影像 depth = frames.get_depth_frame() depth_image_data = depth.as_frame().get_data() depth_image = np.asanyarray(depth_image_data) depth_image = depth_image[(int)(clip_box[0]):(int)(clip_box[1]),(int)(clip_box[2]):(int)(clip_box[3])] depth_image = cv2.resize(depth_image, (640, 480), interpolation=cv2.INTER_CUBIC) depth_image=depth_image/30 depth_image.astype(np.uint8) #深度去背的遮罩 thresh=cv2.inRange(depth_image,20,200) #去背的遮罩做影像處理 kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(1, 1)) eroded = cv2.erode(thresh,kernel) kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT,(5, 5)) dilated = cv2.dilate(eroded,kernel2) #亮度遮罩 bright_mask = np.zeros(image.shape); bright_mask.fill(200) bright_mask = bright_mask.astype(np.uint8); bright_mask = cv2.bitwise_and(bright_mask, bright_mask, mask=dilated) #rgb影像亮度處理 # image = cv2.bitwise_and(image, image, mask=dilated) image = image.astype(int)+200-bright_mask.astype(int); image = np.clip(image, 0, 255) image = image.astype(np.uint8); #tfpose image 縮放 if args.zoom < 1.0: canvas = np.zeros_like(image) img_scaled = cv2.resize(image, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (canvas.shape[1] - img_scaled.shape[1]) // 2 dy = (canvas.shape[0] - img_scaled.shape[0]) // 2 canvas[dy:dy + img_scaled.shape[0], dx:dx + img_scaled.shape[1]] = img_scaled image = canvas elif args.zoom > 1.0: img_scaled = cv2.resize(image, None, fx=args.zoom, fy=args.zoom, interpolation=cv2.INTER_LINEAR) dx = (img_scaled.shape[1] - image.shape[1]) // 2 dy = (img_scaled.shape[0] - image.shape[0]) // 2 image = img_scaled[dy:image.shape[0], dx:image.shape[1]] #tfpose 計算 humans = e.inference(image) # #得到joint # jdata = TfPoseEstimator.get_json_data(image.shape[0],image.shape[1],humans) # if(len(jdata)>2): # try: # #傳送Position資料至SERVER # chating_room.sendTrackingData(jdata,'track') # except: # print("Cannot send data to server.") #去背後深度影像 depth_masked = cv2.bitwise_and(depth_image, depth_image, mask=dilated) human_json_datas = [] for human in humans: #計算深度資料 depthDatas=[] image_h, image_w = image.shape[:2] # get point for i in range(common.CocoPart.Background.value): if i not in human.body_parts.keys(): continue body_part = human.body_parts[i] y= int(body_part.y * image_h+ 0.5) x = int(body_part.x * image_w + 0.5) s=5; mat = depth_masked[y-s if(y-s>=0) else 0:y+s if(y+s<=479) else 479,x-s if(x-s>=0) else 0:x+s if (x+s<=639) else 639] count=0; sum_depth=0; for j in range (mat.shape[0]): for k in range (mat.shape[1]): if mat[j,k]!=0: sum_depth+=mat[j,k] count+=1 if(count>0): depth=sum_depth/count else: depth=0 try: depthDatas.append(JointDepthData(i,x,y,depth).__dict__) except: print("err:"+str(x)+" "+str(y)+" "+str(body_part.x )+" "+str(body_part.y )) human_json_datas.append(json.dumps(depthDatas)) json_data = json.dumps(human_json_datas) if(len(json_data)>2): try: #傳送Depth資料至SERVER chating_room.sendTrackingData(json_data,'track_depth') except: print("Cannot send depth data to server.") depth_image = cv2.applyColorMap(cv2.convertScaleAbs(depth_image/25), cv2.COLORMAP_JET) cv2.circle(image,(320,240), 5, (255,255,255), -1) cv2.circle(image,(304,480-98), 5, (0,0,255), -1) cv2.circle(image,(377,480-197), 5, (0,160,255), -1) cv2.circle(image,(106,480-49), 5, (0,255,255), -1) cv2.circle(image,(460,480-136), 5, (0,255,0), -1) cv2.circle(image,(481,480-134), 5, (255,0,0), -1) cv2.circle(image,(85,480-143), 5, (255,160,0), -1) image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) cv2.putText(image,"FPS: %f" % (1.0 / (time.time() - fps_time)),(10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 0), 2) cv2.imshow('tf-pose-estimation result', image) if cv2.waitKey(25) & 0xFF == ord('q'): cv2.destroyAllWindows() break cv2.destroyAllWindows()