import sys import argparse import copy import datetime import numpy as np import cv2 as cv from facial_fer_model import FacialExpressionRecog sys.path.append('../face_detection_yunet') from yunet import YuNet def str2bool(v): if v.lower() in ['on', 'yes', 'true', 'y', 't']: return True elif v.lower() in ['off', 'no', 'false', 'n', 'f']: return False else: raise NotImplementedError backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA] targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16] help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA" help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16" try: backends += [cv.dnn.DNN_BACKEND_TIMVX] targets += [cv.dnn.DNN_TARGET_NPU] help_msg_backends += "; {:d}: TIMVX" help_msg_targets += "; {:d}: NPU" except: print('This version of OpenCV does not support TIM-VX and NPU. Visit https://github.com/opencv/opencv/wiki/TIM-VX-Backend-For-Running-OpenCV-On-NPU for more information.') parser = argparse.ArgumentParser(description='Facial Expression Recognition') parser.add_argument('--input', '-i', type=str, help='Path to the input image. Omit for using default camera.') parser.add_argument('--model', '-m', type=str, default='./facial_expression_recognition_mobilefacenet_2022july.onnx', help='Path to the facial expression recognition model.') parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends)) parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets)) parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This flag is invalid when using camera.') parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.') args = parser.parse_args() def visualize(image, det_res, fer_res, box_color=(0, 255, 0), text_color=(0, 0, 255)): print('%s %3d faces detected.' % (datetime.datetime.now(), len(det_res))) output = image.copy() landmark_color = [ (255, 0, 0), # right eye (0, 0, 255), # left eye (0, 255, 0), # nose tip (255, 0, 255), # right mouth corner (0, 255, 255) # left mouth corner ] for ind, (det, fer_type) in enumerate(zip(det_res, fer_res)): bbox = det[0:4].astype(np.int32) fer_type = FacialExpressionRecog.getDesc(fer_type) print("Face %2d: %d %d %d %d %s." % (ind, bbox[0], bbox[1], bbox[0]+bbox[2], bbox[1]+bbox[3], fer_type)) cv.rectangle(output, (bbox[0], bbox[1]), (bbox[0]+bbox[2], bbox[1]+bbox[3]), box_color, 2) cv.putText(output, fer_type, (bbox[0], bbox[1]+12), cv.FONT_HERSHEY_DUPLEX, 0.5, text_color) landmarks = det[4:14].astype(np.int32).reshape((5, 2)) for idx, landmark in enumerate(landmarks): cv.circle(output, landmark, 2, landmark_color[idx], 2) return output def process(detect_model, fer_model, frame): h, w, _ = frame.shape detect_model.setInputSize([w, h]) dets = detect_model.infer(frame) if dets is None: return False, None, None fer_res = np.zeros(0, dtype=np.int8) for face_points in dets: fer_res = np.concatenate((fer_res, fer_model.infer(frame, face_points[:-1])), axis=0) return True, dets, fer_res if __name__ == '__main__': detect_model = YuNet(modelPath='../face_detection_yunet/face_detection_yunet_2022mar.onnx') fer_model = FacialExpressionRecog(modelPath=args.model, backendId=args.backend, targetId=args.target) # If input is an image if args.input is not None: image = cv.imread(args.input) # Get detection and fer results status, dets, fer_res = process(detect_model, fer_model, image) if status: # Draw results on the input image image = visualize(image, dets, fer_res) # Save results if args.save: cv.imwrite('result.jpg', image) print('Results saved to result.jpg\n') # Visualize results in a new window if args.vis: cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE) cv.imshow(args.input, image) cv.waitKey(0) else: # Omit input to call default camera deviceId = 0 cap = cv.VideoCapture(deviceId) while cv.waitKey(1) < 0: hasFrame, frame = cap.read() if not hasFrame: print('No frames grabbed!') break # Get detection and fer results status, dets, fer_res = process(detect_model, fer_model, frame) if status: # Draw results on the input image frame = visualize(frame, dets, fer_res) # Visualize results in a new window cv.imshow('FER Demo', frame)