demo.py

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

# Check OpenCV version
assert cv.__version__ >= "4.9.0", \
       "Please install latest opencv-python to try this demo: python3 -m pip install --upgrade opencv-python"

# Valid combinations of backends and targets
backend_target_pairs = [
    [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA],
    [cv.dnn.DNN_BACKEND_CUDA,   cv.dnn.DNN_TARGET_CUDA_FP16],
    [cv.dnn.DNN_BACKEND_TIMVX,  cv.dnn.DNN_TARGET_NPU],
    [cv.dnn.DNN_BACKEND_CANN,   cv.dnn.DNN_TARGET_NPU]
]

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_target', '-bt', type=int, default=0,
                    help='''Choose one of the backend-target pair to run this demo:
                        {:d}: (default) OpenCV implementation + CPU,
                        {:d}: CUDA + GPU (CUDA),
                        {:d}: CUDA + GPU (CUDA FP16),
                        {:d}: TIM-VX + NPU,
                        {:d}: CANN + NPU
                    '''.format(*[x for x in range(len(backend_target_pairs))]))
parser.add_argument('--save', '-s', action='store_true',
                    help='Specify to save results. This flag is invalid when using camera.')
parser.add_argument('--vis', '-v', action='store_true',
                    help='Specify 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__':
    backend_id = backend_target_pairs[args.backend_target][0]
    target_id = backend_target_pairs[args.backend_target][1]

    detect_model = YuNet(modelPath='../face_detection_yunet/face_detection_yunet_2023mar.onnx')

    fer_model = FacialExpressionRecog(modelPath=args.model,
                                      backendId=backend_id,
                                      targetId=target_id)

    # 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)