"./results/rnet/log_bs512_lr0.001_072502/check_point/model_050.pth",
o_model_path=
"./results/onet/log_bs512_lr0.001_072602/check_point/model_050.pth",
min_face_size=24,
use_cuda=True) # 加载模型参数，构造检测器
logger.info("Init the MtcnnDetector.")
project_root = pathlib.Path()
inputPath = project_root / "data" / "test_images"
outputPath = project_root / "data" / "you_result"
outputPath.mkdir(exist_ok=True)
with torch.no_grad():
start = time.time()
for num, input_img_filename in enumerate(inputPath.iterdir()):
logger.info("Start to predict No.{} image.".format(num))
img_name = input_img_filename.name
logger.info("The name of the image is {}.".format(img_name))
img = cv2.imread(str(input_img_filename))
RGB_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
bboxs, landmarks = mtcnn_detector.detect_face(
RGB_image) # 检测得到bboxs以及特征点
img = draw_images(img, bboxs, landmarks) # 得到绘制人脸框及特征点的图片
savePath = outputPath / img_name # 图片保存路径
logger.info("Predict complete. Save image to {}.".format(
str(savePath)))
cv2.imwrite(str(savePath), img) # 保存图片
logger.info("Finish all the images.")
logger.info("Cost time: {:.3f}s".format(time.time() - start))
outputPath = project_root / "data" / "test_new_energy/results"
outputPath.mkdir(exist_ok=True)
start = time.time()
corr = 0
count = 0
for num, input_img_filename in enumerate(inputPath.iterdir()):
logger.info("Start to process No.{} image.".format(num))
img_name = input_img_filename.name
logger.info("The name of the image is {}.".format(img_name))
img = cv2.imread(str(input_img_filename))
if img is None:
continue
RGB_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
bboxs, landmarks, attrs, pboxes, rboxes = mtcnn_detector.detect_face(
RGB_image) # 检测得到bboxs以及特征点
if len(bboxs) > 0:
corr += 1
img = draw_images(img, pboxes, [], [], (255, 0, 0))
img = draw_images(img, rboxes, [], [], (0, 255, 255))
img = draw_images(img, bboxs, landmarks, attrs) # 得到绘制人脸框及特征点的图片
savePath = outputPath / img_name # 图片保存路径
logger.info("Process complete. Save image to {}.".format(
str(savePath)))
cv2.imwrite(str(savePath), img) # 保存图片
count += 1
print(float(corr) / count)
logger.info("Finish all the images.")
use_cuda=False)
# logger.info("Init the MtcnnDetector.")
imdb = get_imdb_fddb(data_dir)
nfold = len(imdb)
for i in range(nfold):
image_names = imdb[i]
dets_file_name = os.path.join(out_dir,
'FDDB-det-fold-%02d.txt' % (i + 1))
fid = open(dets_file_name, 'w')
# image_names_abs = [os.path.join(data_dir, 'originalPics', image_name + '.jpg') for image_name in image_names]
for idx, im_name in enumerate(image_names):
img_path = os.path.join(data_dir, 'originalPics', im_name + '.jpg')
img = cv2.imread(img_path)
boxes, _ = MtcnnDetector.detect_face(img)
if boxes is None:
fid.write(im_name + '\n')
fid.write(str(1) + '\n')
fid.write('%f %f %f %f %f\n' % (0, 0, 0, 0, 0.99))
continue
fid.write(im_name + '\n')
fid.write(str(len(boxes)) + '\n')
for box in boxes:
fid.write(
'%f %f %f %f %f\n' %
(float(box[0]), float(box[1]), float(box[2] - box[0] + 1),
float(box[3] - box[1] + 1), box[4]))