first commit

README.md

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+# 人脸检测识别&&活体识别
+
+## 1. 运行环境
+
+`conda create -n py39 python=3.9`
+
+`conda activate py39`
+
+运行所需依赖库
+`pip install tensorflow==2.11.0 scipy scikit-learn==1.0.2 numpy imutils opencv-python gradio`
+
+## 2. 文件
+
++ **models:** 包含各模型权重文件
++ **pickles:** 包含不同模型对应类别编码以及人脸识别中SVM分类器模型
++ **infer.py** 包含封装后的推理模型，包括人脸识别和活体识别的推理
++ **app.py** 包含gradio创建的web服务
+
+## 3. 运行
+
+`python main.py`

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+from infer import Face_inference
+
+
+# 当设置置信度阈值较大时，人脸检测返回值为空，此时直接返回原图
+def inference(image,thresh,model_type):
+    global name
+    if image is None:
+        yield image
+    if model_type == "人脸识别":
+        if m_model.face_detect(image,thresh,is_show=True) is None:
+            yield image
+        else:
+            name,prob,res_frame = m_model.face_detect(image,thresh,is_show=True)
+            yield res_frame
+    else:
+        if m_model.live_detect(image,name,thresh,is_show=True) is None:
+            yield image
+        else:
+            label,prob,res_frame = m_model.live_detect(image,name,thresh,is_show=True)
+            yield res_frame
+
+
+model_name = gr.Radio(["人脸识别","活体检测"],value="人脸识别",label="Model",info="选择需要的模型")
+threshold = gr.Slider(0.001,1,value=0.1,label="threshold",info="设置置信度阈值")
+inputs_webcam = gr.Image(sources=["webcam"],streaming=True)
+outputs_image = gr.Image(type="numpy",label="结果图片")
+
+if __name__ == "__main__":
+    m_model = Face_inference()
+    name = ""
+    demo = gr.Interface(inference,inputs=[inputs_webcam,threshold,model_name],outputs=outputs_image,live=True,title="检测")
+    demo.queue().launch(max_threads=8)

infer.py

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+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.models import load_model
+import numpy as np
+import pickle
+import cv2
+import time
+import os
+
+
+def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
+    dim = None
+    (h, w) = image.shape[:2]
+    # 如果高和宽为None则直接返回
+    if width is None and height is None:
+        return image
+    # 检查宽是否是None
+    if width is None:
+        # 计算高度的比例并并按照比例计算宽度
+        r = height / float(h)
+        dim = (int(w * r), height)
+    # 高为None
+    else:
+        # 计算宽度比例，并计算高度
+        r = width / float(w)
+        dim = (width, int(h * r))
+    resized = cv2.resize(image, dim, interpolation=inter)
+    # return the resized image
+    return resized
+
+# 构建脸部/活体识别检测器
+class Face_inference():
+    def __init__(self):
+        self.face_detector_path = "./models/face_detector/"
+        self.recognize_embed_path = "./models/nn4.small2.v1.t7"
+        self.liveness_model_path = "./models/liveness.model"
+        self.recognize_pickle = "./pickles/face_label/recognizer.pickle"
+        self.recognize_class_pickle = "./pickles/face_label/le.pickle"
+        self.liveness_class_pickle = "./pickles/liveness/le.pickle"
+        
+        # 人脸检测
+        self.face_detector = cv2.dnn.readNetFromCaffe(
+            self.face_detector_path + "deploy.prototxt",
+            self.face_detector_path + "res10_300x300_ssd_iter_140000.caffemodel"
+        )
+
+        # 人脸embedding
+        self.face_embed_model = cv2.dnn.readNetFromTorch(self.recognize_embed_path)
+
+        # embedding SVM 分类
+        with open(self.recognize_pickle,"rb") as f:
+            self.face_recog_model = pickle.loads(f.read())
+         
+        # 活体分类
+        self.liveness_model = load_model(self.liveness_model_path)
+
+        # 对应类别标签
+
+        # 人脸类别
+        with open(self.recognize_class_pickle,"rb") as f:
+            self.face_class = pickle.loads(f.read()).classes_
+        
+        # 活体类别
+        with open(self.liveness_class_pickle,"rb") as f:
+            self.liveness_class = pickle.loads(f.read()).classes_
+
+    def face_detect(self,img,conf_threshold = 0.1,is_show = False):
+        if img is None:
+            return img
+        frame = resize(img,width = 600)
+        h,w = frame.shape[:2]
+        blob = cv2.dnn.blobFromImage(
+            cv2.resize(frame,(300,300)),1.0,(300,300),
+            (104.0,177.0,123.0),swapRB = False,crop = False
+        )
+
+        self.face_detector.setInput(blob)
+        detections = self.face_detector.forward()
+
+        for i in range(detections.shape[2]):
+            conf = detections[0,0,i,2]
+            if conf > conf_threshold:
+                iou = detections[0,0,i,3:7] * np.array([w,h,w,h])
+                (start_x,start_y,end_x,end_y) = iou.astype('int')
+                face = frame[start_y:end_y,start_x:end_x]
+
+                fh,fw = face.shape[:2]
+                if fh < 20 or fw < 20:
+                    continue
+                face_blob = cv2.dnn.blobFromImage(
+                    face,1.0/255.,(96,96),
+                    (0,0,0),swapRB = True , crop = False
+                )
+
+                self.face_embed_model.setInput(face_blob)
+                vec = self.face_embed_model.forward()
+
+                preds = self.face_recog_model.predict_proba(vec)[0]
+                pred_index = np.argmax(preds)
+                pred_class,pred_prob = self.face_class[pred_index],preds[pred_index]
+                pred_class = pred_class if pred_prob > conf_threshold else "err"
+
+                if is_show:
+                    text = f"{pred_class}: {pred_prob*100:.2f}%"
+                    cv2.rectangle(frame,(start_x,start_y),(end_x,end_y),
+                                  (0,0,255,2))
+                    y = start_y - 10 if start_y - 10 > 10 else start_y + 10
+                    cv2.putText(frame,text,(start_x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
+
+                    return pred_class,pred_prob,frame
+                else:
+                    return pred_class,pred_prob
+                
+
+        
+    def live_detect(self,img,name = "",conf_threshold = 0.1,is_show = False):
+        if img is None:
+            return img
+        frame = resize(img,width=600)
+        h,w = frame.shape[:2]
+        blob = cv2.dnn.blobFromImage(
+            cv2.resize(frame,(300,300)),1.0,(300,300),
+            (104.0,177.0,123.0),swapRB = False,crop = False
+        )
+
+        self.face_detector.setInput(blob)
+        detections = self.face_detector.forward()
+                
+        for i in range(detections.shape[2]):
+            conf = detections[0,0,i,2]
+            if conf > conf_threshold:
+                iou = detections[0,0,i,3:7] * np.array([w,h,w,h])
+                (start_x,start_y,end_x,end_y) = iou.astype('int')
+                face = frame[start_y:end_y,start_x:end_x]
+
+                face = cv2.resize(face,(32,32))
+                face = face.astype('float')/255.
+                face = img_to_array(face)
+                face = np.expand_dims(face,axis = 0)
+
+                preds = self.liveness_model(face)[0]
+                index = np.argmax(preds)
+                pred_class,pred_prob = self.liveness_class[index],preds[index]
+
+                if is_show:
+                    text = f"{name} , {pred_class}: {pred_prob*100:.4f}%"
+                    cv2.rectangle(frame,(start_x,start_y),(end_x,end_y),
+                                  (0,0,255,2))
+                    y = start_y - 10 if start_y - 10 > 10 else start_y + 10
+                    cv2.putText(frame,text,(start_x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
+
+                    return pred_class,pred_prob,frame
+                else:
+                    return pred_class,pred_prob
+
+
+if __name__ == "__main__":
+    model = Face_inference()
+    img = cv2.imread("R.jpg")
+    name,prob,res_frame = model.face_detect(img,is_show=True)
+    cv2.imshow("face",res_frame)
+    label,prob,res_frame = model.live_detect(img,name,is_show=True)
+    cv2.imshow("live",res_frame)
+    cv2.waitKey(0)