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import cv2
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from PIL import Image
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from insightface.app import FaceAnalysis
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import os
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# 获取图片中人脸有哪些，返回人脸信息
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# ctx_id: 如果有多块GPU，那么ctx_id=0表示使用第一块显卡,如果是负数则是使用CPU执行预测
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# det_thresh: 配置的是人脸检测的阈值
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# det_size: 检测模型图片大小
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# det_thresh = 0.50
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def get_faces(v_image_path, v_ctx_id, v_det_thresh, v_det_size, v_image_path_flag=None):
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try:
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app.prepare(ctx_id=v_ctx_id, det_thresh=v_det_thresh, det_size=v_det_size)
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img = cv2.imread(v_image_path) # 检测图片中人脸的个数
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v_faces = app.get(img)
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# 在图片上标识出人脸区域
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if v_image_path_flag is not None and len(v_faces) > 0:
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rimg = app.draw_on(img, v_faces)
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cv2.imwrite(v_image_path_flag, rimg)
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return v_faces
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except Exception as err:
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return None
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# 获取面积最大的人脸
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def get_first_face(v_faces, output_path):
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# 计算每个面部的面积
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face_areas = [(face['bbox'][2] - face['bbox'][0]) * (face['bbox'][3] - face['bbox'][1]) for face in v_faces]
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# 根据面积降序排序面部
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sorted_faces = [face for _, face in sorted(zip(face_areas, v_faces), reverse=True)]
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# 输出排序后的面部数组
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bbox = sorted_faces[0]['bbox'] # 最大的人脸
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# 把一个图片的这个区域截取出来，另存在一个图片文件
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img = Image.open(image_path)
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# 将图像转换为RGB模式
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img_rgb = img.convert('RGB')
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# 截取矩形区域
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cropped_image = img_rgb.crop(bbox)
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# 保存截取的区域到新的图片文件
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cropped_image.save(output_path)
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# 关闭图像
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cropped_image.close()
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# 关闭图像
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img_rgb.close()
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if __name__ == '__main__':
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# 声明 FaceAnalysis
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app = FaceAnalysis(allowed_modules=['detection'], providers=['CUDAExecutionProvider', 'CPUExecutionProvider'],
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download=False)
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# 遍历目录下所有文件
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directory = r'C:\Users\Administrator\Desktop\Upload'
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# 对于多个人脸，打标记框
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flag_directory = r'C:\Users\Administrator\Desktop\Flag'
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# 第一个人脸
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first_directory = r'C:\Users\Administrator\Desktop\FirstFace'
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# 不存在就创建
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if not os.path.exists(flag_directory):
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os.makedirs(flag_directory)
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if not os.path.exists(first_directory):
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os.makedirs(first_directory)
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with os.scandir(directory) as entries: # 使用os.scandir()获取目录条目
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for entry in entries:
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if entry.is_file(): # 检查是否是文件
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image_path = os.path.join(directory, entry.name)
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# 打标记框的图片
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v_image_path_flag = os.path.join(flag_directory, entry.name)
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# 第一个人脸图片
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v_first_face_path = os.path.join(first_directory, entry.name)
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# 获取人脸信息
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faces = get_faces(v_image_path=image_path, v_ctx_id=0, v_det_thresh=0.2, v_det_size=(640, 640),
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v_image_path_flag=v_image_path_flag)
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if faces is not None and len(faces) > 0:
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# 获取第一个人脸图片,只截取第一个人脸的方法是不可以的
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get_first_face(faces, output_path=v_first_face_path)
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else:
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print("文件名称" + entry.name + ",人脸数量:" + str(len(faces)))
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