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139 lines
6.4 KiB
139 lines
6.4 KiB
import os, time, oss2, cv2 |
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from alibabacloud_facebody20191230.client import Client |
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from alibabacloud_facebody20191230.models import DetectFaceRequest |
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from alibabacloud_tea_openapi.models import Config |
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from alibabacloud_tea_util.models import RuntimeOptions |
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def detect_face_feature_points(url): |
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try: |
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detect_face_request = DetectFaceRequest( |
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image_url=url, |
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landmark=True, |
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quality=True, |
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max_face_number=5, |
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pose=True |
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) |
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runtime = RuntimeOptions() |
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result = facebody_client.detect_face_with_options(detect_face_request, runtime) |
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return result.body |
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except Exception as e: |
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print(e) |
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return None |
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def main(): |
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# 定义人脸关键特征点，以降低运算量，提高抖动检测的兼容性 |
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# https://help.aliyun.com/document_detail/151969.html?spm=a2c4g.155519.0.0.742d7d63kadwAt |
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# feature_points = ['chin', 'left_eyebrow', 'right_eyebrow', 'nose_bridge', 'nose_tip', 'left_eye', 'right_eye', 'top_lip', 'bottom_lip'] |
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feature_points = [29, 36, 45, 52, 59, 66, 75, 98] |
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# 在oss上定义图片缩放样式 |
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# https://help.aliyun.com/document_detail/44688.html?spm=a2c4g.11186623.6.1001.6b3c4c3f3ZQZ7z |
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# https://oss-console-img-demo-cn-hangzhou.oss-cn-hangzhou.aliyuncs.com/example.jpg?x-oss-process=image/resize,p_50 |
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# 采用python 获取授权oss上的图片url |
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# https://help.aliyun.com/document_detail/31947.html?spm=a2c4g.11186623.6.1003.6b3c4c3f3ZQZ7z |
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style = 'image/resize,p_50' |
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# 在oss上循环同一次拍照pid上的2张图片，分别获取缩放后的2张图片url |
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prefix = f'zhengliang/photos/photos_1000/' |
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filelist = oss2.ObjectIteratorV2(bucket_client, prefix=prefix, delimiter='/') |
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# for file in filelist: |
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# pid = file.key.split('/')[-2] |
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# url1 = f'zhengliang/photos/photos_1000/{pid}/photo1/103_1.jpg' |
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# url2 = f'zhengliang/photos/photos_1000/{pid}/photo2/103_8.jpg' |
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# sign_url1 = bucket_client.sign_url('GET', url1, 3600, params={'x-oss-process': style}) |
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# sign_url2 = bucket_client.sign_url('GET', url2, 3600, params={'x-oss-process': style}) |
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pid = '45610' |
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url1 = f'zhengliang/photos/photos_1000/{pid}/photo1/103_1.jpg' |
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url2 = f'zhengliang/photos/photos_1000/{pid}/photo2/103_8.jpg' |
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sign_url1 = bucket_client.sign_url('GET', url1, 3600, params={'x-oss-process': style}) |
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sign_url2 = bucket_client.sign_url('GET', url2, 3600, params={'x-oss-process': style}) |
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result1 = detect_face_feature_points(sign_url1) |
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photo1_face_count = result1.data.face_count |
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photo1_feature_points = result1.data.landmarks |
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# result2 = detect_face_feature_points(sign_url2) |
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# photo2_face_count = result2.data.face_count |
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# photo2_feature_points = result2.data.landmarks |
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# 将特征点坐标转换成坐标编号为key，坐标值为value的字典 |
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print(photo1_feature_points) |
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photo1_feature_points_dict = {} |
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face_i = 0 |
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while face_i < photo1_face_count: |
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points = {} |
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index = 0 |
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i = 0 |
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while i < len(photo1_feature_points): |
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if i == 105: |
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i = 0 # 105是下一张脸的第一个特征点 |
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photo1_feature_points_dict[face_i] = points |
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face_i = face_i + 1 |
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print(i) |
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points[index] = {'x': photo1_feature_points[face_i * face_feature_points_count + i], 'y': photo1_feature_points[face_i * face_feature_points_count + i + 1]} |
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i = i + 2 |
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index = index + 1 |
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print(photo1_feature_points_dict) |
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index = 0 |
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i = 0 |
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while i < len(photo1_feature_points): |
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photo1_feature_points_dict[index] = {'x': photo1_feature_points[i], 'y': photo1_feature_points[i + 1]} |
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i = i + 2 |
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index = index + 1 |
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photo2_feature_points_dict = {} |
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index = 0 |
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i = 0 |
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while i < len(photo2_feature_points): |
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photo2_feature_points_dict[index] = {'x': photo2_feature_points[i], 'y': photo2_feature_points[i + 1]} |
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i = i + 2 |
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index = index + 1 |
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localphoto1 = '/data/datasets/photos/{pid}/photo1/103_1.jpg' |
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localphoto2 = '/data/datasets/photos/{pid}/photo2/103_8.jpg' |
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# 用opencv在localphoto中画出特征点位置 |
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photo1 = cv2.imread(localphoto1) |
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photo2 = cv2.imread(localphoto2) |
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for feature_point in feature_points: |
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cv2.circle(photo1, (photo1_feature_points_dict[feature_point]['x'], photo1_feature_points_dict[feature_point]['y']), 2, (0, 0, 255), -1) |
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cv2.circle(photo2, (photo2_feature_points_dict[feature_point]['x'], photo2_feature_points_dict[feature_point]['y']), 2, (0, 0, 255), -1) |
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cv2.imwrite(f'/data/datasets/photos/{pid}/photo1/103_1.point.jpg', photo1) |
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cv2.imwrite(f'/data/datasets/photos/{pid}/photo2/103_8.point.jpg', photo2) |
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# 采用阿里云的105个人脸特征点检测api，获取2张图片的人脸特征点信息 |
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# https://help.aliyun.com/document_detail/155645.html?spm=a2c4g.11186623.6.1002.6b3c4c3f3ZQZ7z |
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# 过滤人脸特征点信息中的关键特征点，计算2张图片中每个人脸关键特征点的坐标差值，以判断是否抖动 |
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# 将每次拍照的两张图片每个人脸中的关键特征点的距离保存到数据库中，以便后续分析，每条记录的pid为拍照的pid，face_id为人脸id，feature_point为关键特征点，distance为距离，最大距离为max_distance，最小距离为min_distance，平均距离为avg_distance，抖动阈值为jitter_threshold，抖动标志为jitter_flag，抖动标志为0表示不抖动，抖动标志为1表示抖动 |
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# 在原始图片1上画出人脸关键特征点，叠加到原始图片2上，生成透明重叠图片，以便观察图片是否抖动 |
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# 通过steamit图表画出每次拍照的两张图片每个人脸中的关键特征点的距离变化曲线，同时可以点击打开生成的透明重叠抖动观察图片 |
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if __name__ == '__main__': |
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# 定义阿里云oss的配置参数 |
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access_key_id = 'LTAI5tSReWm8hz7dSYxxth8f' |
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access_key_secret = '8ywTDF9upPAtvgXtLKALY2iMYHIxdS' |
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facebody_endpoint = 'facebody.cn-shanghai.aliyuncs.com' |
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endpoint = 'oss-cn-shanghai.aliyuncs.com' |
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bucket_name = 'suwa3d-test' |
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face_feature_points_count = 105 |
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bucket_client = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, bucket_name) |
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facebody_client = Client(Config( |
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access_key_id=access_key_id, |
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access_key_secret=access_key_secret, |
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endpoint=facebody_endpoint, |
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region_id='cn-shanghai' |
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)) |
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main() |