def test(inoutDir, outputDir, model): # 原模型的P，R，net + 自行训练后的Onet，展示并保存检测后的图片
pnet, rnet, onet_jiang = create_mtcnn_net(
p_model_path="./original_model/pnet_epoch.pt",
r_model_path="./original_model/rnet_epoch.pt",
o_model_path="./original_model/" + model + ".pt",
use_cuda=False)
mtcnn_detector = MtcnnDetector(pnet=pnet,
rnet=rnet,
onet=onet_jiang,
min_face_size=24)
files = os.listdir(inoutDir)
i = 0
for image in files:
i += 1
image = os.path.join("./lfpw_test/", image)
img = cv2.imread(image)
img_bg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
bboxs, landmarks1 = mtcnn_detector.detect_face(img) # 原始的图片用原始网络检测
vis_face(img_bg, bboxs, landmarks1,
outputDir + model + "-" + str(i) + ".jpg") # 保存图片
import cv2
from mtcnn.core.detect import create_mtcnn_net, MtcnnDetector
from mtcnn.core.vision import vis_face
if __name__ == '__main__':
pnet, rnet, onet = create_mtcnn_net(
p_model_path="./original_model/pnet_epoch.pt",
r_model_path="./original_model/rnet_epoch.pt",
o_model_path="./original_model/onet_epoch.pt",
use_cuda=False)
mtcnn_detector = MtcnnDetector(pnet=pnet,
rnet=rnet,
onet=onet,
min_face_size=24)
img = cv2.imread("./s_l.jpg")
img_bg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# b, g, r = cv2.split(img)
# img2 = cv2.merge([r, g, b])
bboxs, landmarks = mtcnn_detector.detect_face(img)
# print box_align
save_name = 'r_4.jpg'
vis_face(img_bg, bboxs, landmarks, save_name)
def test_Onet_without_PRnet(self, annotation, outputDir, test_moudel, xxyy,
savePic):
imagedb = ImageDB(annotation)
gt_imdb = imagedb.load_imdb()
pnet, rnet, onet_jiang = create_mtcnn_net(
p_model_path="./original_model/pnet_epoch.pt",
r_model_path="./original_model/rnet_epoch.pt",
o_model_path="./original_model/" + test_moudel + ".pt",
use_cuda=False)
mtcnn_detector = MtcnnDetector(pnet=pnet,
rnet=rnet,
onet=onet_jiang,
min_face_size=24)
test_data = TrainImageReader(gt_imdb,
48,
batch_size=100,
shuffle=False) # 读入1个batch的数据
# train_data.reset()
total_errors = 0
cnt = 0
for i, (images, (gt_labels, gt_bboxes,
gt_landmarks)) in enumerate(test_data): # 取1个batch
list_imgs = [images[i, :, :, :]
for i in range(images.shape[0])] # 100张图片
list_bboxes = [gt_bboxes[i, :] for i in range(gt_bboxes.shape[0])]
list_gt_landmarks = [
gt_landmarks[i, :] for i in range(gt_landmarks.shape[0])
]
mix = list(zip(list_imgs, list_bboxes, list_gt_landmarks))
batch_errors = []
for img, gt_bbox, gt_landmark in mix: # 取1个图片
if xxyy:
bboxs, landmarks = mtcnn_detector.detect_onet_xxyy(
img, gt_bbox) # 原始的图片用原始网络检测,xxyy
else:
bboxs, landmarks = mtcnn_detector.detect_onet(
img, gt_bbox) # 原始的图片用原始网络检测,xxyy
if landmarks.size:
cnt += 1
bboxs = bboxs[:1] # 多个检测框保留第一个
landmarks = landmarks[:1]
if savePic:
vis_face(img, bboxs, landmarks,
self.output_dir + str(cnt) + ".jpg") # 保存图片
gt_landmark = np.array(gt_landmark).reshape(5, 2)
landmarks = np.array(landmarks).reshape(5, 2)
normDist = np.linalg.norm(gt_landmark[1] -
gt_landmark[0]) # 左右眼距离
error = np.mean(
np.sqrt(np.sum(
(landmarks - gt_landmark)**2, axis=1))) / normDist
batch_errors.append(error)
batch_errors = np.array(batch_errors).sum()
total_errors += batch_errors
print("%s: %s pics mean error is %s" %
(datetime.datetime.now(), cnt, total_errors / cnt))
if cnt > 999:
print("%s:%s pics mean error is %s" %
(datetime.datetime.now(), cnt, total_errors / cnt))
f = open("landmark_test.txt", "a+")
f.write("%s, moudel_name:%s.pt, %s pics mean error is %s\n" %
(datetime.datetime.now(), test_moudel, cnt,
np.array(total_errors).reshape(1, -1).sum() / cnt))
f.close()
return
print("%s:%s pics mean error is %s" %
(datetime.datetime.now(), cnt, total_errors / cnt))
import cv2
from mtcnn.core.detect import create_mtcnn_net, MtcnnDetector
from mtcnn.core.vision import vis_face
import warnings
warnings.filterwarnings("ignore")
if __name__ == '__main__':
use_cuda = True
pnet, rnet, onet = create_mtcnn_net(
p_model_path="./original_model/pnet_epoch.pt",
r_model_path="./original_model/rnet_epoch.pt",
o_model_path="./original_model/onet_epoch.pt",
use_cuda=use_cuda)
mtcnn_detector = MtcnnDetector(pnet=pnet,
rnet=rnet,
onet=onet,
min_face_size=24)
img = cv2.imread("./img/part2_002268.jpg")
img_bg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#b, g, r = cv2.split(img)
#img2 = cv2.merge([r, g, b])
bboxs, landmarks = mtcnn_detector.detect_face(img)
print(bboxs)
# print box_align
save_file = './img/result.jpg'
bboxs = mtcnn_detector.box_expand(bboxs, 0.3, 0.25)
vis_face(img_bg, bboxs, landmarks, save_file)