def detect_face(image):
"""
从图片二维矩阵中检测出人脸
:param image:
:return:
"""
det = hdface_detector(use_cuda=False)
# img_det = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
result = det.detect_face(image)
boxs = []
for one in result:
face = one['box']
boxs.append(face)
return boxs
def main(args):
det = hdface_detector(use_cuda=False)
checkpoint = torch.load(args.model_path)
plfd_backbone = PFLDInference().cuda()
plfd_backbone.load_state_dict(checkpoint)
plfd_backbone.eval()
plfd_backbone = plfd_backbone.cuda()
transform = transforms.Compose([transforms.ToTensor()])
root = args.images_path
path_list = glob.glob(os.path.join(root, "*.jpg"))
# cap = cv2.VideoCapture("")
for img_path in path_list:
img = cv2.imread(img_path)
height, width = img.shape[:2]
img_det = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
result = det.detect_face(img_det)
for i in range(len(result)):
box = result[i]['box']
cls = result[i]['cls']
pts = result[i]['pts']
x1, y1, x2, y2 = box
cv2.rectangle(img, (x1, y1), (x2, y2), (255, 255, 25))
w = x2 - x1 + 1
h = y2 - y1 + 1
size_w = int(max([w, h]) * 0.9)
size_h = int(max([w, h]) * 0.9)
cx = x1 + w // 2
cy = y1 + h // 2
x1 = cx - size_w // 2
x2 = x1 + size_w
y1 = cy - int(size_h * 0.4)
y2 = y1 + size_h
left = 0
top = 0
bottom = 0
right = 0
if x1 < 0:
left = -x1
if y1 < 0:
top = -y1
if x2 >= width:
right = x2 - width
if y2 >= height:
bottom = y2 - height
x1 = max(0, x1)
y1 = max(0, y1)
x2 = min(width, x2)
y2 = min(height, y2)
cropped = img[y1:y2, x1:x2]
print(top, bottom, left, right)
cropped = cv2.copyMakeBorder(cropped, top, bottom, left, right,
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).cuda()
pose, landmarks = plfd_backbone(input)
poses = pose.cpu().detach().numpy()[0] * 180 / np.pi
pre_landmark = landmarks[0]
pre_landmark = pre_landmark.cpu().detach().numpy().reshape(
-1, 2) * [size_w, size_h]
cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0))
for (x, y) in pre_landmark.astype(np.int32):
cv2.circle(img, (x1 - left + x, y1 - bottom + y), 1,
(255, 255, 0), 1)
plot_pose_cube(img,
poses[0],
poses[1],
poses[2],
tdx=pts['nose'][0],
tdy=pts['nose'][1],
size=(x2 - x1) // 2)
cv2.imshow('0', img)
cv2.waitKey(0)
type=str,
help='imgs dir')
parser.add_argument('--results_path',
default="./result",
type=str,
help='results dir')
parser.add_argument('--mode', default="cpu", type=str, help='cpu or gpu')
args = parser.parse_args()
if args.mode == "cpu":
caffe.set_mode_cpu()
elif args.mode == "gpu":
caffe.set_mode_gpu()
image_mean = np.array([0, 0, 0])
image_std = 255.0
det = hdface_detector(use_cuda=True)
def inference():
net = caffe.Net(args.caffe_prototxt_path, args.caffe_model_path,
caffe.TEST)
input_size = [int(v.strip()) for v in args.input_size.split(",")]
witdh = input_size[0]
height = input_size[1]
# priors = define_img_size(input_size)
net.blobs['input'].reshape(1, 3, height, witdh)
result_path = args.results_path
imgs_path = args.imgs_path
if not os.path.exists(result_path):
os.makedirs(result_path)
listdir = os.listdir(imgs_path)
