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import os.path
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import cv2
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from insightface.app import FaceAnalysis
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import shutil
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# 准备好模型
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def prepare_model():
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# 默认的模型目录是否存在
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directory = 'C:/Users/Administrator/.insightface/models/buffalo_l'
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if not os.path.exists(directory): # 不存在就创建
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os.makedirs(directory)
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# 模型文件是不是存在
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files = ['1k3d68.onnx', '2d106det.onnx', 'det_10g.onnx', 'genderage.onnx', 'w600k_r50.onnx']
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# 云存储的前缀
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prefix = r'https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/insightface/'
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# windows版本的wget
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# https://eternallybored.org/misc/wget/
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source_file = './Tools/wget.exe'
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target_file = "c:/Windows/System32/wget.exe"
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if not os.path.exists(target_file):
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if os.path.exists(source_file):
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shutil.copy(source_file, target_file)
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elif os.path.exists('.' + source_file):
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shutil.copy('.' + source_file, target_file)
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# 下载缺失的模型文件
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for file in files:
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if not os.path.exists(directory + '/' + file):
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os.system('wget ' + prefix + file + ' -P ' + directory)
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# 获取图片中人脸有哪些，返回人脸个数
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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_size: 检测模型图片大小
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# det_thresh = 0.50 配置的是人脸检测的阈值
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def get_faces_count(v_image_path, v_ctx_id=0, v_det_thresh=0.5, v_det_size=(640, 640)):
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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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return len(v_faces)
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except Exception as err:
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print(err)
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return 0
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if __name__ == '__main__':
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# 准备好模型
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prepare_model()
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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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# 0
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image_path = r'C:\Users\Administrator\Desktop\Upload\Cat.png'
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# 1
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# image_path = r'C:\Users\Administrator\Desktop\Upload\d825cc1e-6518-afd5-1b02-ed88a7a72806.jpg'
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# 2
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# image_path=r'C:\Users\Administrator\Desktop\Upload\ff781209-7368-bb99-4b0c-02c1406519d5.jpg'
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# 获取人脸个数信息
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face_count = get_faces_count(v_image_path=image_path)
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print(face_count)
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