def load_model(self): """ 加载模型 """ model = get_testing_model() model.load_weights(self.weights_path) params, model_params = config_reader(self.config_path) return model, params, model_params
def process_one_img(): img_path = os.path.join(DATA_DIR, 'test_img.jpg') keras_weights_file = os.path.join(ROOT_DIR, "model/keras/model.h5") model = get_testing_model() model.load_weights(keras_weights_file) output_img = os.path.join(img_path + ".r.jpg") output_pos = os.path.join(img_path + ".p.txt") predict_img(img_path, model, output_img, output_pos)
def get_open_pose(): #load model model = get_testing_model() model.load_weights(keras_weights_file) # load config params, model_params = config_reader() return model, params, model_params
def process_img_batch(): keras_weights_file = os.path.join(ROOT_DIR, "model/keras/model.h5") output_img_dir = os.path.join(DATA_DIR, "results") output_pos_dir = os.path.join(DATA_DIR, "pos") img_dir = os.path.join(DATA_DIR, 'frames-p') model = get_testing_model() model.load_weights(keras_weights_file) paths_list, names_list = traverse_dir_files(img_dir) for path, name in zip(paths_list, names_list): print('[Info] name: {}'.format(name)) output_img = os.path.join(output_img_dir, name + ".r.jpg") output_pos = os.path.join(output_pos_dir, name + ".p.txt") predict_img(path, model, output_img, output_pos)
def generate_model_blobs(in_video_file, starting_frame, ending_frame): from model.cmu_model import get_testing_model from processing_action import get_model_blob # load model # authors of original model don't use # vgg normalization (subtracting mean) on input images model = get_testing_model() model.load_weights(keras_weights_file) # Video reader cam = cv2.VideoCapture(in_video_file) input_fps = cam.get(cv2.CAP_PROP_FPS) ret_val, orig_image = cam.read() video_length = int(cam.get(cv2.CAP_PROP_FRAME_COUNT)) if ending_frame is None: ending_frame = video_length scale_search = [1, .5, 1.5, 2] # [.5, 1, 1.5, 2] scale_search = scale_search[0:process_speed] params['scale_search'] = scale_search if ending_frame is None: ending_frame = video_length i = 0 if starting_frame > 0: while (cam.isOpened()) and ret_val is True and i < starting_frame: ret_val, orig_image = cam.read() i += 1 blobs = {} while (cam.isOpened()) and ret_val is True and i < ending_frame: if i % frame_rate_ratio == 0: input_image = cv2.cvtColor(orig_image, cv2.COLOR_RGB2BGR) tic = time.time() blobs[i] = get_model_blob(input_image, params, model, model_params) toc = time.time() print(f"Generate model blob, frame: {i}, took {toc - tic} seconds") ret_val, orig_image = cam.read() i += 1 return blobs
def std_main(): parser = argparse.ArgumentParser() parser.add_argument('--image', type=str, required=True, help='input image') parser.add_argument('--output', type=str, default='result.png', help='output image') parser.add_argument('--model', type=str, default='model/keras/model.h5', help='path to the weights file') args = parser.parse_args() image_path = args.image output = args.output keras_weights_file = args.model tic = time.time() print('start processing...') # load model # authors of original model don't use # vgg normalization (subtracting mean) on input images model = get_testing_model() model.load_weights(keras_weights_file) # load config params, model_params = config_reader() input_image = cv2.imread(image_path) # B,G,R order all_peaks, subset, candidate = extract_parts(input_image, params, model, model_params) canvas = draw(input_image, all_peaks, subset, candidate) toc = time.time() print('processing time is %.5f' % (toc - tic)) cv2.imwrite(output, canvas) cv2.destroyAllWindows()
default='model/model.h5', help='path to the weights file') args = parser.parse_args() image_path = args.image output = args.output keras_weights_file = args.model tic = time.time() print('start processing...') # load model # authors of original model don't use # vgg normalization (subtracting mean) on input images model = get_testing_model() model.load_weights(keras_weights_file) # load config params, model_params = config_reader() #immagine da classificare input_image = cv2.imread('images.jpg') # B,G,R order body_parts, all_peaks, subset, candidate = extract_parts( input_image, params, model, model_params) canvas, dict, lis1, lis2 = draw(input_image, all_peaks, subset, candidate) cv2.imwrite(output, canvas) Concatena.salva_csv_dist(lis1, lis2, 'none')
def process(input_image, params, model_params): # load openpose model # vgg normalization (subtracting mean) on input images model = get_testing_model() model.load_weights('model/keras/model.h5') # for openpose # find connection in the specified sequence, center 29 is in the position 15 limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ [1, 16], [16, 18], [3, 17], [6, 18]] # the middle joints heatmap correpondence mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \ [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \ [55, 56], [37, 38], [45, 46]] # visualize ''' colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] ''' oriImg = input_image # B,G,R order multiplier = [x * model_params['boxsize'] / oriImg.shape[0] for x in params['scale_search']] heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19)) paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38)) for m in range(len(multiplier)): scale = multiplier[m] imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) imageToTest_padded, pad = util.padRightDownCorner(imageToTest, model_params['stride'], model_params['padValue']) input_img = np.transpose(np.float32(imageToTest_padded[:,:,:,np.newaxis]), (3,0,1,2)) # required shape (1, width, height, channels) output_blobs = model.predict(input_img) # extract outputs, resize, and remove padding heatmap = np.squeeze(output_blobs[1]) # output 1 is heatmaps heatmap = cv2.resize(heatmap, (0, 0), fx=model_params['stride'], fy=model_params['stride'], interpolation=cv2.INTER_CUBIC) heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) paf = np.squeeze(output_blobs[0]) # output 0 is PAFs paf = cv2.resize(paf, (0, 0), fx=model_params['stride'], fy=model_params['stride'], interpolation=cv2.INTER_CUBIC) paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) heatmap_avg = heatmap_avg + heatmap / len(multiplier) paf_avg = paf_avg + paf / len(multiplier) all_peaks = [] peak_counter = 0 for part in range(18): map_ori = heatmap_avg[:, :, part] map = gaussian_filter(map_ori, sigma=3) map_left = np.zeros(map.shape) map_left[1:, :] = map[:-1, :] map_right = np.zeros(map.shape) map_right[:-1, :] = map[1:, :] map_up = np.zeros(map.shape) map_up[:, 1:] = map[:, :-1] map_down = np.zeros(map.shape) map_down[:, :-1] = map[:, 1:] peaks_binary = np.logical_and.reduce( (map >= map_left, map >= map_right, map >= map_up, map >= map_down, map > params['thre1'])) peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks] id = range(peak_counter, peak_counter + len(peaks)) peaks_with_score_and_id = [peaks_with_score[i] + (id[i],) for i in range(len(id))] all_peaks.append(peaks_with_score_and_id) peak_counter += len(peaks) connection_all = [] special_k = [] mid_num = 10 for k in range(len(mapIdx)): score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]] candA = all_peaks[limbSeq[k][0] - 1] candB = all_peaks[limbSeq[k][1] - 1] nA = len(candA) nB = len(candB) indexA, indexB = limbSeq[k] if (nA != 0 and nB != 0): connection_candidate = [] for i in range(nA): for j in range(nB): vec = np.subtract(candB[j][:2], candA[i][:2]) norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1]) # failure case when 2 body parts overlaps if norm == 0: continue vec = np.divide(vec, norm) startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \ np.linspace(candA[i][1], candB[j][1], num=mid_num))) vec_x = np.array( [score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \ for I in range(len(startend))]) vec_y = np.array( [score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \ for I in range(len(startend))]) score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1]) score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min( 0.5 * oriImg.shape[0] / norm - 1, 0) criterion1 = len(np.nonzero(score_midpts > params['thre2'])[0]) > 0.8 * len( score_midpts) criterion2 = score_with_dist_prior > 0 if criterion1 and criterion2: connection_candidate.append([i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]]) connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True) connection = np.zeros((0, 5)) for c in range(len(connection_candidate)): i, j, s = connection_candidate[c][0:3] if (i not in connection[:, 3] and j not in connection[:, 4]): connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]]) if (len(connection) >= min(nA, nB)): break connection_all.append(connection) else: special_k.append(k) connection_all.append([]) # last number in each row is the total parts number of that person # the second last number in each row is the score of the overall configuration subset = -1 * np.ones((0, 20)) candidate = np.array([item for sublist in all_peaks for item in sublist]) for k in range(len(mapIdx)): if k not in special_k: partAs = connection_all[k][:, 0] partBs = connection_all[k][:, 1] indexA, indexB = np.array(limbSeq[k]) - 1 for i in range(len(connection_all[k])): # = 1:size(temp,1) found = 0 subset_idx = [-1, -1] for j in range(len(subset)): # 1:size(subset,1): if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]: subset_idx[found] = j found += 1 if found == 1: j = subset_idx[0] if (subset[j][indexB] != partBs[i]): subset[j][indexB] = partBs[i] subset[j][-1] += 1 subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] elif found == 2: # if found 2 and disjoint, merge them j1, j2 = subset_idx membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2] if len(np.nonzero(membership == 2)[0]) == 0: # merge subset[j1][:-2] += (subset[j2][:-2] + 1) subset[j1][-2:] += subset[j2][-2:] subset[j1][-2] += connection_all[k][i][2] subset = np.delete(subset, j2, 0) else: # as like found == 1 subset[j1][indexB] = partBs[i] subset[j1][-1] += 1 subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] # if find no partA in the subset, create a new subset elif not found and k < 17: row = -1 * np.ones(20) row[indexA] = partAs[i] row[indexB] = partBs[i] row[-1] = 2 row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + \ connection_all[k][i][2] subset = np.vstack([subset, row]) # delete some rows of subset which has few parts occur deleteIdx = []; for i in range(len(subset)): if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4: deleteIdx.append(i) subset = np.delete(subset, deleteIdx, axis=0) ''' for i in range(18): for j in range(len(all_peaks[i])): cv2.circle(input_image, all_peaks[i][j][0:2], 4, colors[i], thickness=-1) stickwidth = 4 ''' for n in range(len(subset)): right_shoulder_X = int(candidate[int(subset[n][2]), 0]) right_shoulder_Y = int(candidate[int(subset[n][2]), 1]) left_shoulder_X = int(candidate[int(subset[n][5]), 0]) left_shoulder_Y = int(candidate[int(subset[n][5]), 1]) if abs(right_shoulder_X - left_shoulder_X) >= 100: body_ori = input_image[right_shoulder_Y:, right_shoulder_X:left_shoulder_X] body_RGB = cv2.cvtColor(body_ori, cv2.COLOR_BGR2RGB) # reshape the image to be a list of pixels body_reshape = body_RGB.reshape((body_RGB.shape[0] * body_RGB.shape[1], 3)) # cluster the pixel intensities clt = KMeans(n_clusters = 5) # reshape the image to be a list of pixels clt.fit(body_reshape) # 找出各色之比重 hist = utils.centroid_histogram(clt) #找出顏色比重最重的hist index color_idx = np.where(hist==np.max(hist)) #主色RDB為: clt.cluster_centers_[color_idx] color = clt.cluster_centers_[color_idx].flatten().tolist() else: continue return color[0], color[1], color[2]
parser.add_argument('--output', type=str, default='result.png', help='output image') parser.add_argument('--model', type=str, default='model/keras/model.h5', help='path to the weights file') #model/keras/model.h5 args = parser.parse_args() input_image = args.image output = args.output keras_weights_file = args.model print('start processing...') # load model # authors of original model don't use # vgg normalization (subtracting mean) on input images model = get_testing_model(GPU=True) if False: # if model.h5 is trained on multi GPU from keras.utils import training_utils model = training_utils.multi_gpu_model(model,gpus=2) model.load_weights(keras_weights_file) # load config params, model_params = config_reader() tic = time.time() # generate image with body parts canvas = process(input_image, params, model_params) toc = time.time() print ('processing time is %.5f' % (toc - tic))
def video_cap(Excersise): path_model_h5 = 'model.h5' keras_weights_file = path_model_h5 #Analysis for the every n frames frame_rate_ratio = 1 #Int 1 (fastest, lowest quality) to 4 (slowest, highest quality) process_speed = 1 #ending_frame = args.end print('start processing...') # Video input video_file = '/home/saireddy/SaiReddy/Desk/Flask/OrbitPose/videos/push.mp4' print(video_file) # Output location video_output = '/home/saireddy/SaiReddy/Desk/Flask/OrbitPose/videos/output/push4.avi' model = get_testing_model() model.load_weights(keras_weights_file) # load config params, model_params = config_reader() # Video reader #cam = cv2.VideoCapture(video_file) cam = cv2.VideoCapture(video_file) input_fps = cam.get(cv2.CAP_PROP_FPS) print("input frames per second:-", input_fps) ret_val, orig_image = cam.read() video_length = int(cam.get(cv2.CAP_PROP_FRAME_COUNT)) print("total frames in a video:-", video_length) # Video writer output_fps = input_fps / frame_rate_ratio print("out put frames:-", output_fps) frame_width = int(cam.get(3)) frame_height = int(cam.get(4)) print("width:-", frame_width) print("Height:-", frame_height) out = cv2.VideoWriter(video_output, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), output_fps, (frame_width, frame_height)) #out = cv2.VideoWriter(video_output, cv2.VideoWriter_fourcc(*'DIVX'),output_fps, (frame_width, frame_height)) scale_search = [1, .5, 1.5, 2] # [.5, 1, 1.5, 2] scale_search = scale_search[0:process_speed] params['scale_search'] = scale_search i = 0 # default is 0 count = 0 while (cam.isOpened()) and ret_val is True: if ret_val is None: break if i % frame_rate_ratio == 0: input_image = cv2.cvtColor(orig_image, cv2.COLOR_RGB2BGR) tic = time.time() if Excersise == 'Normal': all_peaks, subset, candidate = extract_parts( input_image, params, model, model_params) canvas, theta, theta1, theta2, theta3, Angle1, Angle2, Angle3, Angle4 = draw( orig_image, all_peaks, subset, candidate) cv2.rectangle(canvas, (0, 0), (265, 35), color=(0, 255, 0), thickness=2) cv2.putText(canvas, "right Hand angle :- {0:.2f}".format(float(theta)), (30, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255)) cv2.putText(canvas, "right leg angle :- {0:.2f}".format(float(theta2)), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255)) cv2.rectangle(canvas, (645, 0), (900, 35), color=(0, 255, 0), thickness=2) cv2.putText(canvas, "left Hand angle :- {0:.2f}".format(float(theta1)), (650, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 225)) cv2.putText(canvas, "left leg angle :- {0:.2f}".format(float(theta3)), (650, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255)) elif Excersise == 'Fat': all_peaks1, subset1, candidate1 = extract_parts_angle( input_image, params, model, model_params) canvas, theta5, theta6, Angle5, Angle6 = draw_angle( orig_image, all_peaks1, subset1, candidate1) cv2.rectangle(canvas, (645, 0), (900, 35), color=(0, 255, 0), thickness=2) cv2.putText(canvas, "Left :- {0:.2f}".format(float(theta5)), (650, 15), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 225)) cv2.putText(canvas, "Right :- {0:.2f}".format(float(theta6)), (650, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255)) elif Excersise == 'Pushups': print("HIIIIIII") all_peaks2, subset2, candidate2 = extract_parts_push( input_image, params, model, model_params) canvas, theta7, theta8, Angle7, Angle8 = draw_push( orig_image, all_peaks2, subset2, candidate2) print("THeta 7, theta8 ", theta7, theta8) if float(theta7) > 170.0 or float(theta8) > 170.0: count = count + 1 cv2.rectangle(canvas, (645, 0), (900, 60), color=(255, 25, 100), thickness=2) cv2.putText(canvas, "Left :- {0:.2f}".format(float(theta7)), (650, 30), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 225)) cv2.putText(canvas, "Right :- {0:.2f}".format(float(theta8)), (650, 45), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 255)) cv2.putText(canvas, f"count :- {count}".format(float(theta8)), (300, 35), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 255)) else: cv2.rectangle(canvas, (645, 0), (900, 60), color=(255, 25, 100), thickness=2) cv2.putText(canvas, "Left :- {0:.2f}".format(float(theta7)), (650, 30), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0)) cv2.putText(canvas, "Right :- {0:.2f}".format(float(theta8)), (650, 45), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0)) else: print("Sorry Wrong Excersise was given") print('Processing frame: ', i) toc = time.time() out.write(canvas) ret_val, orig_image = cam.read() i += 1 if cv2.waitKey(25) & 0xFF == ord('q'): break cv2.destroyAllWindows() cam.release() convert_video( '/home/saireddy/SaiReddy/Desk/Flask/OrbitPose/videos/output/pose14.avi', '/home/saireddy/SaiReddy/Desk/Flask/OrbitPose/videos/output/pose14.mp4' ) return Angle1, Angle2, Angle3, Angle4
def load_m(model_path): keras_weights_file = "model.h5" global model model = get_testing_model() model.load_weights(keras_weights_file)
def video_cap(): path_model_h5 = 'model.h5' keras_weights_file = path_model_h5 #Analysis for the every n frames frame_rate_ratio = 1 #Int 1 (fastest, lowest quality) to 4 (slowest, highest quality) process_speed = 1 #ending_frame = args.end print('start processing...') # Video input video_file = '/home/saireddy/SaiReddy/Desk/Flask/OrbitPose/videos/sit.mp4' print(video_file) # Output location video_output = '/home/saireddy/SaiReddy/Desk/Flask/OrbitPose/videos/output/sit.avi' model = get_testing_model() model.load_weights(keras_weights_file) # load config params, model_params = config_reader() # Video reader #cam = cv2.VideoCapture(video_file) cam = cv2.VideoCapture(video_file) input_fps = cam.get(cv2.CAP_PROP_FPS) print("input frames per second:-", input_fps) ret_val, orig_image = cam.read() video_length = int(cam.get(cv2.CAP_PROP_FRAME_COUNT)) print("total frames in a video:-", video_length) # Video writer output_fps = input_fps / frame_rate_ratio print("out put frames:-", output_fps) frame_width = int(cam.get(3)) frame_height = int(cam.get(4)) print("width:-", frame_width) print("Height:-", frame_height) out = cv2.VideoWriter(video_output, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), output_fps, (frame_width, frame_height)) #out = cv2.VideoWriter(video_output, cv2.VideoWriter_fourcc(*'DIVX'),output_fps, (frame_width, frame_height)) scale_search = [1, .5, 1.5, 2] # [.5, 1, 1.5, 2] scale_search = scale_search[0:process_speed] params['scale_search'] = scale_search i = 0 # default is 0 while (cam.isOpened()) and ret_val is True: if ret_val is None: break if i % frame_rate_ratio == 0: input_image = cv2.cvtColor(orig_image, cv2.COLOR_RGB2BGR) tic = time.time() all_peaks1, subset1, candidate1 = extract_parts_angle( input_image, params, model, model_params) canvas, theta5, theta6, Angle5, Angle6 = draw_angle( orig_image, all_peaks1, subset1, candidate1) cv2.rectangle(canvas, (645, 0), (900, 35), color=(0, 255, 0), thickness=2) cv2.putText(canvas, "Left :- {0:.2f}".format(float(theta5)), (650, 15), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 225)) cv2.putText(canvas, "Right :- {0:.2f}".format(float(theta6)), (650, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255)) print('Processing frame: ', i) toc = time.time() print(Angle5) print(Angle6) out.write(canvas) ret_val, orig_image = cam.read() i += 1 if cv2.waitKey(25) & 0xFF == ord('q'): break return Angle5, Angle6
def cameraLoad(): parser = argparse.ArgumentParser() parser.add_argument('--device', type=int, default=0, help='ID of the device to open') #parser에서 받는거 parser.add_argument('--model', type=str, default='model/keras/model.h5', help='path to the weights file') #모델경로 parser.add_argument('--frame_ratio', type=int, default=7, help='analyze every [n] frames') # --process_speed changes at how many times the model analyzes each frame at a different scale parser.add_argument( '--process_speed', type=int, default=1, help= 'Int 1 (fastest, lowest quality) to 4 (slowest, highest quality)') parser.add_argument('--out_name', type=str, default=None, help='name of the output file to write') parser.add_argument('--mirror', type=bool, default=True, help='whether to mirror the camera') # 받은 값들 저장하는것 args = parser.parse_args() device = args.device keras_weights_file = args.model frame_rate_ratio = args.frame_ratio process_speed = args.process_speed out_name = args.out_name mirror = args.mirror print('start processing...') # load model # authors of original model don't use # vgg normalization (subtracting mean) on input images model = get_testing_model() model.load_weights(keras_weights_file) # load config params, model_params = config_reader() # Video reader cam = cv2.VideoCapture(device) # 디바이스=0은 카메라, 파일넣고싶으면 파일 경로 넣으면 됨 # CV_CAP_PROP_FPS input_fps = cam.get(cv2.CAP_PROP_FPS) # 해당카메라의 프레임으로 추측 print("Running at {} fps.".format(input_fps)) ret_val, orig_image = cam.read( ) #ret = 제대로 read됐는지확인하는 부울타입, orig = 실질적인 프레임단위의 이미지 width = orig_image.shape[1] height = orig_image.shape[0] factor = 0.3 out = None # Output location if out_name is not None and ret_val is not None: # out_name이 parser 40번째줄/ out_name이 잇으면 파일생성/ output_path = 'videos/outputs/' output_format = '.mp4' video_output = output_path + out_name + output_format # Video writer output_fps = input_fps / frame_rate_ratio tmp = crop(orig_image, width, factor) fourcc = cv2.VideoWriter_fourcc(*'mp4v') out = cv2.VideoWriter(video_output, fourcc, output_fps, (tmp.shape[1], tmp.shape[0])) del tmp scale_search = [0.22, 0.25, .5, 1, 1.5, 2] # [.5, 1, 1.5, 2] scale_search = scale_search[0:process_speed] params['scale_search'] = scale_search i = 0 # default is 0 resize_fac = 8 # while(cam.isOpened()) and ret_val is True: while True: cv2.waitKey(10) if cam.isOpened() is False or ret_val is False: break if mirror: orig_image = cv2.flip(orig_image, 1) tic = time.time() cropped = crop(orig_image, width, factor) # 파일 자름 #opencv함수로 사이즈 조절하는 것 input_image = cv2.resize(cropped, (0, 0), fx=1 / resize_fac, fy=1 / resize_fac, interpolation=cv2.INTER_CUBIC) input_image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR) # generate image with body parts # extract_part all_peaks, subset, candidate = extract_parts( input_image, params, model, model_params) canvas = draw(cropped, all_peaks, subset, candidate, resize_fac=resize_fac) print('Processing frame: ', i) toc = time.time() print('processing time is %.5f' % (toc - tic)) if out is not None: # 이게 있으면 계속 출력하는 것 out.write(canvas) # 이미지 위에 만들어진 스켈레톤! # canvas = cv2.resize(canvas, (0, 0), fx=4, fy=4, interpolation=cv2.INTER_CUBIC) cv2.imshow('frame', canvas) if cv2.waitKey(1) & 0xFF == ord('q'): break ret_val, orig_image = cam.read() i += 1
def load_model(keras_weights_file): model = get_testing_model() model.load_weights(keras_weights_file) params, model_params = config_reader() return model, params, model_params