def main(): print('Initializing main function') # Withdrawing tkinter # Loading model dirs list_folder_data = [ ('/home/mauricio/CNN/Classes/Door', 0.05), ('/home/mauricio/CNN/Classes/Tires', 0.05), ('/home/mauricio/CNN/Classes/Walk', 0.05), ] list_hmm = [] for folder_data in list_folder_data: label_name = get_label_from_folder(folder_data[0]) full_model_dir = os.path.join(hnn_model_folder, '{0}.pkl'.format(label_name)) list_hmm.append(ClassHMM(full_model_dir)) # Initializing instances instance_pose = ClassOpenPose() instance_nn = ClassNN.load_from_params(nn_model_dir) option = input('Select 1 to train, 2 to eval hmm, 3 to preprocess: ') if option == '1': print('Train hmm selection') train_hmm(list_folder_data, list_hmm, instance_nn, instance_pose) elif option == '2': eval_hmm(list_folder_data, list_hmm, instance_nn, instance_pose) elif option == '3': recalculate = False pre_process_images(instance_pose, list_folder_data, recalculate) else: print('Invalid argument: {0}'.format(option))
def main(): print('Initializing main function') # Initializing instances instance_pose = ClassOpenPose() instance_net = ClassNN.load_from_params(model_dir) # Withdrawing list Tk().withdraw() # Select directory to process init_dir = '/home/mauricio/CNN/Images' options = {'initialdir': init_dir} dir_name = filedialog.askdirectory(**options) if not dir_name: print('Directory not selected') else: # Loading images list_files = os.listdir(dir_name) list_files.sort() desc_list = list() for file in list_files: full_path = os.path.join(dir_name, file) print('Processing image {0}'.format(full_path)) image = cv2.imread(full_path) arr = instance_pose.recognize_image(image) arr_pass = list() for person_arr in arr: if ClassUtils.check_vector_integrity_part( person_arr, min_pose_score): arr_pass.append(person_arr) if len(arr_pass) != 1: print('Invalid len {0} for image {1}'.format( len(arr_pass), full_path)) continue else: result_des = ClassDescriptors.get_person_descriptors( arr_pass[0], min_pose_score) descriptor_arr = result_des['fullDesc'] # Add descriptors to list desc_list.append(descriptor_arr) # Convert to numpy array print('Total poses: {0}'.format(len(desc_list))) # Transform list and predict desc_list_np = np.asarray(desc_list, dtype=np.float) print('ndim pose list: {0}'.format(desc_list_np.ndim)) list_classes = list() predict_results = instance_net.predict_model_array(desc_list_np) for result in predict_results: list_classes.append(result['classes']) print('Predict results: {0}'.format(list_classes)) print('Classes label: {0}'.format(instance_net.label_names)) print('Done!')
def main(): print('Initializing main function') # Prompt for user input cam_number_str = input('Insert camera number to process: ') cam_number = int(cam_number_str) # Open video from opencv cap = cv2.VideoCapture(cam_number) # Initializing open pose distance instance_pose = ClassOpenPose() # Initializing variables model_dir = '/home/mauricio/models/nn_classifier' instance_nn = ClassNN.load_from_params(model_dir) while True: # Capture frame-by-frame ret, frame = cap.read() # Processing frame with openpose arr, frame = instance_pose.recognize_image_tuple(frame) # Check if there is one frame with vector integrity arr_pass = list() min_score = 0.05 # Checking vector integrity for all elements # Verify there is at least one arm and one leg for elem in arr: if ClassUtils.check_vector_integrity_pos(elem, min_score): arr_pass.append(elem) if len(arr_pass) != 1: print('Invalid len for arr_pass: {0}'.format(arr_pass)) else: person_array = arr_pass[0] # Getting person descriptors results = ClassDescriptors.get_person_descriptors( person_array, min_score) # Descriptors data_to_add = results['angles'] data_to_add += ClassUtils.get_flat_list( results['transformedPoints']) data_np = np.asanyarray(data_to_add, dtype=np.float) # Getting result predict result_predict = instance_nn.predict_model(data_np) detected_class = result_predict['classes'] label_class = get_label_name(instance_nn.label_names, detected_class) print('Detected: {0} - Label: {1}'.format(detected_class, label_class)) # Draw text class into image - Evaluation purposes font = cv2.FONT_HERSHEY_SIMPLEX font_scale = 0.6 font_color = (255, 255, 255) line_type = 2 cv2.putText(frame, '{0}'.format(label_class), (0, 0), font, font_scale, font_color, line_type) # Display the resulting frame cv2.imshow('frame', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break # When everything done, release the capture cap.release() cv2.destroyAllWindows() print('Done!')