mtcnn代码实现

MTCNN（Multi-task Cascaded Convolutional Networks）是一种用于人脸检测的深度神经网络。MTCNN使用了一个三级级联的CNN进行人脸检测。以下是MTCNN的代码实现。

首先，需要导入必要的库和模型：

import cv2
import numpy as np
from keras.models import load_model

PNet = load_model('PNet.h5')
RNet = load_model('RNet.h5')
ONet = load_model('ONet.h5')

接下来，定义一个函数来进行人脸检测：

def detect_faces(image):
    img = image.copy()
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    height, width = img.shape[:2]
    
    # 图像预处理
    img_resized = cv2.resize(img, (int(width/2), int(height/2)))
    img_resized = (img_resized - 127.5) / 128.0
    img_resized = np.expand_dims(img_resized, axis=0)
    
    # PNet 预测
    threshold = 0.7
    scale_factor = 0.709
    scales = []
    bounding_boxes = []
    while min(img_resized.shape[:2]) > 12:
        scales.append(np.array([img_resized.shape[1]/width, img_resized.shape[0]/height]))
        output = PNet.predict(img_resized)
        cls_prob = output[0][:,:,1]
        bbox_pred = output[1]
        indices = np.where(cls_prob > threshold)
        indices = np.stack(indices, axis=1)
        if indices.shape[0] == 0:
            img_resized = cv2.resize(img_resized, (int(img_resized.shape[1]*scale_factor), int(img_resized.shape[0]*scale_factor)))
            continue
        for index in indices:
            xmin = index[1] * 2
            ymin = index[0] * 2
            xmax = index[1] * 2 + 12
            ymax = index[0] * 2 + 12
            score = cls_prob[index[0], index[1]]
            offset = bbox_pred[index[0], index[1]]
            bounding_boxes.append([xmin, ymin, xmax, ymax, score, offset])
        
        img_resized = cv2.resize(img_resized, (int(img_resized.shape[1]*scale_factor), int(img_resized.shape[0]*scale_factor)))
    
    # NMS 处理
    bounding_boxes = np.array(bounding_boxes)
    keep = nms(bounding_boxes[:, :5], 0.5)
    bounding_boxes = bounding_boxes[keep]
    
    # RNet 预测
    threshold = 0.7
    scales = []
    for b in bounding_boxes:
        w = b[2] - b[0]
        h = b[3] - b[1]
        size = max(w, h)
        img_sub = img[b[1]:b[3], b[0]:b[2]]
        img_resized = cv2.resize(img_sub, (24, 24))
        img_resized = (img_resized - 127.5) / 128.0
        img_resized = np.expand_dims(img_resized, axis=0)
        scales.append(size/24)
        output = RNet.predict(img_resized)
        cls_prob = output[0][:,1]
        bbox_pred = output[1]
        if cls_prob > threshold:
            score = cls_prob
            offset = bbox_pred
            xmin = int(b[0] + offset[0]*w)
            ymin = int(b[1] + offset[1]*h)
            xmax = int(b[2] + offset[2]*w)
            ymax = int(b[3] + offset[3]*h)
            bounding_boxes.append([xmin, ymin, xmax, ymax, score])
    
    # NMS 处理
    bounding_boxes = np.array(bounding_boxes)
    keep = nms(bounding_boxes[:, :5], 0.7, 'iom')
    bounding_boxes = bounding_boxes[keep]
    
    # ONet 预测
    threshold = 0.7
    faces = []
    for b in bounding_boxes:
        w = b[2] - b[0]
        h = b[3] - b[1]
        size = max(w, h)
        img_sub = img[b[1]:b[3], b[0]:b[2]]
        img_resized = cv2.resize(img_sub, (48, 48))
        img_resized = (img_resized - 127.5) / 128.0
        img_resized = np.expand_dims(img_resized, axis=0)
        output = ONet.predict(img_resized)
        cls_prob = output[0][:,1]
        bbox_pred = output[1]
        landmark_pred = output[2]
        if cls_prob > threshold:
            score = cls_prob
            offset = bbox_pred
            landmark = landmark_pred
            xmin = int(b[0] + offset[0]*w)
            ymin = int(b[1] + offset[1]*h)
            xmax = int(b[2] + offset[2]*w)
            ymax = int(b[3] + offset[3]*h)
            x1 = xmin + landmark[0]*w
            y1 = ymin + landmark[1]*h
            x2 = xmin + landmark[2]*w
            y2 = ymin + landmark[3]*h
            x3 = xmin + landmark[4]*w
            y3 = ymin + landmark[5]*h
            x4 = xmin + landmark[6]*w
            y4 = ymin + landmark[7]*h
            faces.append([xmin, ymin, xmax, ymax, score, x1, y1, x2, y2, x3, y3, x4, y4])
    
    return faces

其中，nms函数实现了非极大值抑制：

def nms(dets, thresh, method='union'):
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    scores = dets[:, 4]
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    order = scores.argsort()[::-1]
    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])
        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        if method == 'union':
            ovr = inter / (areas[i] + areas[order[1:]] - inter)
        elif method == 'min':
            ovr = inter / np.minimum(areas[i], areas[order[1:]])
        else:
            print('Unknown nms method!')
        inds = np.where(ovr <= thresh)[0]
        order = order[inds + 1]
    return keep

最后，可以使用以下代码来进行人脸检测：

image = cv2.imread('test.jpg')
faces = detect_faces(image)
for face in faces:
    xmin, ymin, xmax, ymax = face[:4]
    cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2)
    for i in range(5, 13, 2):
        x, y = int(face[i]), int(face[i+1])
        cv2.circle(image, (x, y), 2, (0, 0, 255), -1)
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()