def loadNet(self):
global Pnet, Rnet, Onet
Pnet = create_Kao_Pnet(r'12net.h5')
Rnet = create_Kao_Rnet(r'24net.h5')
Onet = create_Kao_Onet(r'48net.h5') # will not work. caffe and TF incompatible
img = cv2.imread('data/0001.png')
scale_img = cv2.resize(img, (100, 100))
input = scale_img.reshape(1, *scale_img.shape)
Pnet.predict(input)
img = cv2.imread('data/0001.png')
scale_img = cv2.resize(img, (24, 24))
input = scale_img.reshape(1, *scale_img.shape)
Rnet.predict(input)
img = cv2.imread('data/0001.png')
scale_img = cv2.resize(img, (48, 48))
input = scale_img.reshape(1, *scale_img.shape)
Onet.predict(input)
return Pnet, Rnet, Onet
import sys
import tools_matrix as tools
import cv2
import numpy as np
import matplotlib.pyplot as plt
import time
from MTCNN import create_Kao_Onet, create_Kao_Rnet, create_Kao_Pnet
Pnet = create_Kao_Pnet(r'12net.h5')
Rnet = create_Kao_Rnet(r'24net.h5')
Onet = create_Kao_Onet(r'48net.h5') # will not work. caffe and TF incompatible
def detectFace(img, threshold):
caffe_img = (img.copy() - 127.5) / 127.5
origin_h, origin_w, ch = caffe_img.shape
scales = tools.calculateScales(img)
out = []
t0 = time.time()
# del scales[:4]
for scale in scales:
hs = int(origin_h * scale)
ws = int(origin_w * scale)
scale_img = cv2.resize(caffe_img, (ws, hs))
input = scale_img.reshape(1, *scale_img.shape)
ouput = Pnet.predict(
input
) # .transpose(0,2,1,3) should add, but seems after process is wrong then.
out.append(ouput)
#Ref :https://github.com/xiangrufan/keras-mtcnn
import tools
import os
import cv2
import numpy as np
import time
from MTCNN import create_Kao_Onet, create_Kao_Rnet, create_Kao_Pnet
#The threshold can be modified for custom usage
#Example: For the original high-accuracy usage :threshold = [0.6,0.6,0.7]
threshold = [0.4, 0.4, 0.5]
DIR_PATH = 'path_to_imagedir/image_dir/'
Pnet = create_Kao_Pnet('./weight/12net.h5')
Rnet = create_Kao_Rnet('./weight/24net.h5')
Onet = create_Kao_Onet(
'./weight/48net.h5') # will not work. caffe and TF incompatible
def detectFace(img, threshold):
caffe_img = (img.copy() - 127.5) / 127.5
origin_h, origin_w, ch = caffe_img.shape
scales = tools.calculateScales(img)
out = []
t0 = time.time()
# del scales[:4]
for scale in scales:
hs = int(origin_h * scale)
