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mtcnn.py
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from builtins import *
import cv2
import numpy as np
import onnx
import onnxruntime
import utils
class mtcnn(object):
def __init__(self):
#-----------------------------#
# mtcnn的第一段
# 粗略获取人脸框
# 输出bbox位置和是否有人脸
#-----------------------------#
self.Pnet = onnxruntime.InferenceSession('model_data/pnet.onnx')
#-----------------------------#
# mtcnn的第二段
# 精修框
#-----------------------------#
self.Rnet = onnxruntime.InferenceSession('model_data/rnet.onnx')
#-----------------------------#
# mtcnn的第三段
# 精修框并获得五个点
#-----------------------------#
self.Onet = onnxruntime.InferenceSession('model_data/onet.onnx')
self.Pnet_inputs = ['input_1']
self.Pnet_outputs = ['conv4-1', 'conv4-2']
self.Rnet_inputs = ['input_1']
self.Rnet_outputs = ['conv5-1', 'conv5-2']
self.Onet_inputs = ['input_1']
self.Onet_outputs = ['conv6-1', 'conv6-2', 'conv6-3']
def detectFace(self, img, threshold):
#-----------------------------#
# 归一化
#-----------------------------#
copy_img = (img.copy() - 127.5) / 127.5
origin_h, origin_w, _ = copy_img.shape
print("orgin image's shape is: ", origin_h, origin_w)
#-----------------------------#
# 计算原始输入图像
# 每一次缩放的比例
#-----------------------------#
scales = utils.calculateScales(img)
out = []
#-----------------------------#
# 粗略计算人脸框
# pnet部分
#-----------------------------#
for scale in scales:
hs = int(origin_h * scale)
ws = int(origin_w * scale)
scale_img = cv2.resize(copy_img, (ws, hs))
inputs = np.expand_dims(scale_img, 0).astype(np.float32)
# print('inputs shape is: ', inputs.shape)
output = self.Pnet.run([self.Pnet_outputs[0], self.Pnet_outputs[1]],
{self.Pnet_inputs[0]: inputs})
# print(output[0].shape)
# print(output[1].shape)
output = [output[0][0], output[1][0]]
out.append(output)
# print(out)
rectangles = []
#----------------------------------------------------------#
# 在这个地方我们对图像金字塔的预测结果进行循环
# 取出每张图片的种类预测和回归预测结果
#----------------------------------------------------------#
for i in range(len(scales)):
#------------------------------------------------------------------#
# 为了方便理解，这里和视频上看到的不太一样
# 因为我们在上面对图像金字塔循环的时候就把batch_size维度给去掉了
#------------------------------------------------------------------#
cls_prob = out[i][0][:, :, 1]
roi = out[i][1]
#--------------------------------------------#
# 取出每个缩放后图片的高宽
#--------------------------------------------#
out_h, out_w = cls_prob.shape
out_side = max(out_h, out_w)
#--------------------------------------------#
# 解码的过程
#--------------------------------------------#
rectangle = utils.detect_face_12net(cls_prob, roi, out_side, 1 / scales[i], origin_w, origin_h, threshold[0])
rectangles.extend(rectangle)
#-----------------------------------------#
# 进行非极大抑制
#-----------------------------------------#
rectangles = np.array(utils.NMS(rectangles, 0.7))
if len(rectangles) == 0:
return rectangles
#-----------------------------------------#
# 稍微精确计算人脸框
# Rnet部分
#-----------------------------------------#
predict_24_batch = []
for rectangle in rectangles:
#--------------------------------------------#
# 利用获取到的粗略坐标，在原图上进行截取
#--------------------------------------------#
crop_img = copy_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
#--------------------------------------------#
# 将截取到的图片进行resize，调整成24x24的大小
#--------------------------------------------#
scale_img = cv2.resize(crop_img, (24, 24))
predict_24_batch.append(scale_img)
cls_prob, roi_prob = self.Rnet.run([self.Rnet_outputs[0], self.Rnet_outputs[1]],
{self.Rnet_inputs[0]: np.array(predict_24_batch).astype(np.float32)})
# print("cls_prob: ", cls_prob.shape)
# print("roi_prob: ", roi_prob.shape)
#------------------------------------------#
# 解码的过程
#------------------------------------------#
rectangles = utils.filter_face_24net(cls_prob, roi_prob, rectangles, origin_w, origin_h, threshold[1])
# print(rectangles)
if len(rectangles) == 0:
return rectangles
#-----------------------------#
# 计算人脸框
# onet部分
#-----------------------------#
predict_batch = []
for rectangle in rectangles:
#------------------------------------------#
# 利用获取到的粗略坐标，在原图上进行截取
#------------------------------------------#
crop_img = copy_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
#-----------------------------------------------#
# 将截取到的图片进行resize，调整成48x48的大小
#-----------------------------------------------#
scale_img = cv2.resize(crop_img, (48, 48))
predict_batch.append(scale_img)
# print(predict_batch)
cls_prob, roi_prob, pts_prob = self.Onet.run([self.Onet_outputs[0], self.Onet_outputs[1], self.Onet_outputs[2]],
{self.Onet_inputs[0]: np.array(predict_batch).astype(np.float32)})
#-----------------------------#
# 解码的过程
#-----------------------------#
rectangles = utils.filter_face_48net(cls_prob, roi_prob, pts_prob, rectangles, origin_w, origin_h, threshold[2])
return rectangles
if __name__ == '__main__':
model = mtcnn()
#-----------------------------#
# 设置检测门限
#-----------------------------#
threshold = [0.5, 0.6, 0.7]
#-----------------------------#
# 读取图片
#-----------------------------#
img = cv2.imread('img/test.jpg')
temp_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#-----------------------------#
# 将图片传入并检测
#-----------------------------#
rectangles = model.detectFace(temp_img, threshold)
draw = img.copy()
for rectangle in rectangles:
W = int(rectangle[2]) - int(rectangle[0])
H = int(rectangle[3]) - int(rectangle[1])
cv2.rectangle(draw, (int(rectangle[0]), int(rectangle[1])),
(int(rectangle[2]), int(rectangle[3])), (0, 0, 255), 2)
for i in range(5, 15, 2):
cv2.circle(draw, (int(rectangle[i+0]), int(rectangle[i+1])), 1, (255, 0, 0), 4)
cv2.imwrite('img/out.jpg', draw)
cv2.imshow('test', draw)
c = cv2.waitKey(0)