赠书 | Python人脸五官姿态检测( 三 ) 作者|李秋键出品|AI科技大本营近几个月

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模型的调用和显示（1）根据点坐标绘制标注图形：
def point_line(point,line):x1 = line[0]y1 = line[1]x2 = line[2]y2 = line[3]x3 = point[0]y3 = point[1]k1 = (y2 - y1)*1.0 /(x2 -x1)b1 = y1 *1.0 - x1 *k1 *1.0k2 = -1.0/k1b2 = y3 *1.0 -x3 * k2 *1.0x = (b2 - b1) * 1.0 /(k1 - k2)y = k1 * x *1.0 +b1 *1.0return [x,y]def point_point(point_1,point_2):x1 = point_1[0]y1 = point_1[1]x2 = point_2[0]y2 = point_2[1]distance = ((x1-x2)**2+(y1-y2)**2)**0.5return distancedef point_line(point,line):x1 = line[0]y1 = line[1]x2 = line[2]y2 = line[3]x3 = point[0]y3 = point[1]k1 = (y2 - y1)*1.0 /(x2 -x1)b1 = y1 *1.0 - x1 *k1 *1.0k2 = -1.0/k1b2 = y3 *1.0 -x3 * k2 *1.0x = (b2 - b1) * 1.0 /(k1 - k2)y = k1 * x *1.0 +b1 *1.0return [x,y]（2）主函数定义：通过加载训练好的模型和定义好的特征参数，通过摄像头获取的视频流或是读取本地视频和图片进行输出姿态特点：
def main(args):checkpoint = torch.load(args.model_path, map_location=device)plfd_backbone = PFLDInference.to(device)plfd_backbone.load_state_dict(checkpoint['plfd_backbone'])plfd_backbone.evalplfd_backbone = plfd_backbone.to(device)transform = transforms.Compose([transforms.ToTensor()])videoCapture = cv2.VideoCapture(args.image_name)fps = videoCapture.get(cv2.CAP_PROP_FPS)size = (int(videoCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),int(videoCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))print("fps:",fps,"size:",size)videoWriter = cv2.VideoWriter("./video/result.avi",cv2.VideoWriter_fourcc('X','V','I','D'),fps,size)success,img = videoCapture.readcv2.imwrite("1.jpg",img)while success:height, width = img.shape[:2]model_test = AntiSpoofPredict(args.device_id)image_bbox = model_test.get_bbox(img)x1 = image_bbox[0]y1 = image_bbox[1]x2 = image_bbox[0] + image_bbox[2]y2 = image_bbox[1] + image_bbox[3]w = x2 - x1h = y2 - y1size = int(max([w, h]))cx = x1 + w/2cy = y1 + h/2x1 = cx - size/2x2 = x1 + sizey1 = cy - size/2y2 = y1 + sizedx = max(0, -x1)dy = max(0, -y1)x1 = max(0, x1)y1 = max(0, y1)edx = max(0, x2 - width)edy = max(0, y2 - height)x2 = min(width, x2)y2 = min(height, y2)cropped = img[int(y1):int(y2), int(x1):int(x2)]if (dx > 0 or dy > 0 or edx > 0 or edy > 0):cropped = cv2.copyMakeBorder(cropped, dy, edy, dx, edx, cv2.BORDER_CONSTANT, 0)cropped = cv2.resize(cropped, (112, 112))input = cv2.resize(cropped, (112, 112))input = cv2.cvtColor(input, cv2.COLOR_BGR2RGB)input = transform(input).unsqueeze(0).to(device)_, landmarks = plfd_backbone(input)pre_landmark = landmarks[0]pre_landmark = pre_landmark.cpu.detach.numpy.reshape(-1, 2) * [112, 112]point_dict = {}i = 0for (x,y) in pre_landmark.astype(np.float32):point_dict[f'{i}'] = [x,y]i += 1效果如下图所示：

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