def face_parsing(_image_info, _face_box_info, _face_landmark_info):
"""
人脸语义分区
Args:
_image_info: 待识别的完整图像
_face_box_info: 人脸所在区域
_face_landmark_info: 人脸landmark坐标信息
Returns: 人脸不同区域的mask的key
"""
to_return_result = {
'parsing_info': {
'bucket_name': '',
'path': ''
},
}
oss_handler = get_oss_handler()
img = oss_handler.download_image_file(_image_info['bucket_name'],
_image_info['path'])
cropped_image = get_rotated_box_roi_from_image(img,
_face_box_info,
_scale_ratio=1.5)
face_parsing_result = face_parsing_handler.execute(cropped_image,
_face_landmark_info)
parsing_result = face_parsing_result['semantic_segmentation']
date_string = get_date_string()
name_string = get_uuid_name()
target_path = os.path.join(date_string, name_string)
target_path = oss_handler.upload_numpy_array('intermediate', target_path,
parsing_result)
to_return_result['parsing_info']['bucket_name'] = 'intermediate'
to_return_result['parsing_info']['path'] = target_path
return to_return_result
def text_recognize(_image_info, _box_info):
"""
文本识别
Args:
_image_info: 待识别的完整图像
_box_info: 图像中文本区域的位置
Returns: 文本区域位置的识别结果
"""
to_return_result = {'text': ''}
oss_handler = get_oss_handler()
img = oss_handler.download_image_file(
_image_info['bucket_name'],
_image_info['path']
)
cropped_image = get_rotated_box_roi_from_image(img, _box_info)
get_image_rotation = text_orientation_op.execute(cropped_image)
if get_image_rotation['orientation'] == TextImageOrientation.ORIENTATION_90:
rotated_image, _ = rotate_degree_img(cropped_image, 90)
elif get_image_rotation['orientation'] == TextImageOrientation.ORIENTATION_180:
rotated_image, _ = rotate_degree_img(cropped_image, 180)
elif get_image_rotation['orientation'] == TextImageOrientation.ORIENTATION_270:
rotated_image, _ = rotate_degree_img(cropped_image, 270)
else:
rotated_image = cropped_image
recognize_result = text_recognize_op.execute(rotated_image)
to_return_result['text'] = recognize_result['text']
return to_return_result
def text_recognize(_image_info, _box_info):
"""
文本识别
Args:
_image_info: 待识别的完整图像
_box_info: 图像中文本区域的位置
Returns: 文本区域位置的识别结果
"""
to_return_result = {'text': ''}
oss_handler = get_oss_handler()
img = oss_handler.download_image_file(_image_info['bucket_name'],
_image_info['path'])
cropped_image = get_rotated_box_roi_from_image(img, _box_info)
recognize_result = text_recognize_op.execute(cropped_image)
to_return_result['text'] = recognize_result['text']
return to_return_result
def face_liveness_detect(_image_info, _face_box_info):
"""
静默人脸活体检测
Args:
_image_info: 待识别的完整图像
_face_box_info: 人脸所在区域
Returns: 人脸的特征向量
"""
to_return_result = {"is_fake": False}
oss_handler = get_oss_handler()
img = oss_handler.download_image_file(_image_info['bucket_name'],
_image_info['path'])
cropped_image = get_rotated_box_roi_from_image(img, _face_box_info, 2.7)
liveness_result = mini_fasnetv2_handler.execute(cropped_image)
score = liveness_result['classification_scores']
to_return_result['is_fake'] = (score[1] > score[0]) and (score[1] >
score[2])
return to_return_result
def face_embedding(_image_info, _face_box_info, _face_landmark_info):
"""
人脸特征向量提取
Args:
_image_info: 待识别的完整图像
_face_box_info: 人脸所在区域
_face_landmark_info: 人脸landmark坐标信息
Returns: 人脸的特征向量
"""
to_return_result = {"face_feature_vector": []}
oss_handler = get_oss_handler()
img = oss_handler.download_image_file(_image_info['bucket_name'],
_image_info['path'])
cropped_image = get_rotated_box_roi_from_image(img, _face_box_info, 1.35)
embedding_result = asia_face_embedding_handler.execute(
cropped_image, _face_landmark_info)
to_return_result['face_feature_vector'] = embedding_result[
'feature_vector']
return to_return_result
def face_landmark(_image_info, _face_box_info):
"""
人脸landmark检测
Args:
_image_info: 待识别的完整图像
_face_box_info: 人脸所在区域
Returns: 人脸landmark坐标
"""
to_return_result = {
'points_count': 106,
'x_locations': [0] * 106,
'y_locations': [0] * 106,
}
oss_handler = get_oss_handler()
img = oss_handler.download_image_file(_image_info['bucket_name'],
_image_info['path'])
cropped_image = get_rotated_box_roi_from_image(img, _face_box_info, 1.35)
landmark_detect_result = landmark106p_detect_handler.execute(cropped_image)
to_return_result = landmark_detect_result.copy()
return to_return_result
default=8001,
help='triton grpc 端口')
args = ag.parse_args()
img = cv2.imread(args.image_path)
ultra_light_face_detect_handler = GeneralUltraLightFaceDetect(
{
'name': 'triton',
'triton_url': args.triton_url,
'triton_port': args.triton_port
}, True, 0.7, 0.5)
landmark106p_detect_handler = GeneralLandmark106p(
{
'name': 'triton',
'triton_url': args.triton_url,
'triton_port': args.triton_port
}, True)
fair_handler = AgeRaceGenderWithFair(
{
'name': 'triton',
'triton_url': args.triton_url,
'triton_port': args.triton_port,
}, True)
# 假设图中有且仅有一人
all_features = []
face_detect_result = ultra_light_face_detect_handler.execute(img)
face_bbox_info = face_detect_result['locations'][0]['box_info']
cropped_face = get_rotated_box_roi_from_image(img, face_bbox_info, 1.25)
landmark_result = landmark106p_detect_handler.execute(cropped_face)
attribute_result = fair_handler.execute(cropped_face, landmark_result)
pprint(attribute_result)
dest='triton_port',
type=int,
default=8001,
help='triton grpc 端口')
args = ag.parse_args()
# 假设图中只有一个人头
img = cv2.imread(args.image_path)
ultra_light_face_detect_handler = GeneralUltraLightFaceDetect(
{
'name': 'triton',
'triton_url': args.triton_url,
'triton_port': args.triton_port
}, True, 0.7, 0.5)
face_bbox = ultra_light_face_detect_handler.execute(
img)['locations'][0]['box_info']
cropped_image_2_7 = get_rotated_box_roi_from_image(img, face_bbox, 2.7)
cropped_image_4_0 = get_rotated_box_roi_from_image(img, face_bbox, 4.0)
cv2.imshow('cropped_image_4_0', cropped_image_4_0)
cv2.imshow('cropped_image_2_7', cropped_image_2_7)
cv2.waitKey(0)
cv2.destroyAllWindows()
mini_fasnetv1se_handler = GeneralMiniFASNetV1SE(
{
'name': 'triton',
'triton_url': args.triton_url,
'triton_port': args.triton_port
}, True)
mini_fasnetv2_handler = GeneralMiniFASNetV2(
{
'name': 'triton',
'triton_url': args.triton_url,
'triton_url': args.triton_url,
'triton_port': args.triton_port
}, True)
ultra_light_face_detect_handler = GeneralUltraLightFaceDetect({
'name': 'triton',
'triton_url': args.triton_url,
'triton_port': args.triton_port
}, True, 0.7, 0.5)
landmark106p_result = landmark106p_detect_handler.execute(img)
landmark106p_result_image = img.copy()
landmark106p_all_points = [(x, y) for x, y in
zip(landmark106p_result['x_locations'],
landmark106p_result['y_locations'])
]
annotate_circle_on_image(landmark106p_result_image, landmark106p_all_points, (0, 255, 0), 3, -1)
cv2.imshow('landmark106p_result_image', landmark106p_result_image)
face_detect_result = ultra_light_face_detect_handler.execute(img)
face_bbox = face_detect_result['locations'][0]['box_info']
cropped_face_region = get_rotated_box_roi_from_image(img, face_bbox, 1.25)
landmark106p_with_bbox_result = landmark106p_detect_handler.execute(cropped_face_region)
landmark106p_with_bbox_result_image = cropped_face_region.copy()
landmark106p_with_bbox_result_all_points = [(x, y) for x, y in
zip(landmark106p_with_bbox_result['x_locations'],
landmark106p_with_bbox_result['y_locations'])
]
annotate_circle_on_image(landmark106p_with_bbox_result_image, landmark106p_with_bbox_result_all_points,
(255, 0, 255), 3, -1)
cv2.imshow('landmark106p_with_bbox_result_image', landmark106p_with_bbox_result_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == '__main__':
from argparse import ArgumentParser
import cv2
from Utils.AnnotationTools import draw_rotated_bbox
from Utils.GeometryUtils import get_rotated_box_roi_from_image
ag = ArgumentParser('Text Detect Example')
ag.add_argument('-i', '--image_path', dest='image_path', type=str, required=True, help='本地图像路径')
ag.add_argument('-u', '--triton_url', dest='triton_url', type=str, required=True, help='triton url')
ag.add_argument('-p', '--triton_port', dest='triton_port', type=int, default=8001, help='triton grpc 端口')
ag.add_argument('-b', '--backbone', dest='backbone', choices=['res18', 'mbv3'], default='res18', help='DB的backbone')
args = ag.parse_args()
img = cv2.imread(args.image_path)
db_handler = GeneralDBDetect({
'name': 'triton',
'backbone_type': args.backbone,
'triton_url': args.triton_url,
'triton_port': args.triton_port},
True, 0.3, 5, 5
)
db_boxes = db_handler.execute(img)['locations']
db_result_to_show = img.copy()
for m_box_index, m_box in enumerate(db_boxes, 1):
draw_rotated_bbox(db_result_to_show, m_box['box_info'], (0, 0, 255), 2)
m_roi_image = get_rotated_box_roi_from_image(img, _rotated_box=m_box['box_info'])
cv2.imshow(f'roi No.{m_box_index}', m_roi_image)
cv2.imshow(f'db_{args.backbone}_result_to_show', db_result_to_show)
cv2.waitKey(0)
cv2.destroyAllWindows()
}, True)
ultra_light_face_detect_handler = GeneralUltraLightFaceDetect(
{
'name': 'triton',
'triton_url': args.triton_url,
'triton_port': args.triton_port
}, True, 0.7, 0.5)
landmark106p_detect_handler = GeneralLandmark106p(
{
'name': 'triton',
'triton_url': args.triton_url,
'triton_port': args.triton_port
}, True)
face_bbox = ultra_light_face_detect_handler.execute(
img)['locations'][0]['box_info']
cropped_image = get_rotated_box_roi_from_image(img, face_bbox, 1.35)
landmark_info = landmark106p_detect_handler.execute(cropped_image)
landmark106p_with_bbox_result_image = cropped_image.copy()
landmark106p_with_bbox_result_all_points = [(x, y) for x, y in zip(
landmark_info['x_locations'], landmark_info['y_locations'])]
face_parsing_with_bbox_result = face_parsing_handler.execute(
cropped_image, landmark_info)
face_parsing_with_bbox_result_image = cropped_image.copy()
face_parsing_with_bbox_result_image = annotate_segmentation(
face_parsing_with_bbox_result_image,
face_parsing_with_bbox_result['semantic_segmentation'])
cv2.imshow(f'face_parsing_with_bbox_result_image',
face_parsing_with_bbox_result_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
