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)
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)
ws = int(origin_w * scale)
scale_img = cv2.resize(caffe_img, (ws, hs))
from tools import calculateScales, detect_face_12net, NMS, filter_face_24net, filter_face_48net
import cv2
import numpy as np
from MTCNN import create_Kao_Onet, create_Kao_Rnet, create_Kao_Pnet
print('Creating Face Detector....')
print('Creating Face Detector Pnet....')
Pnet = create_Kao_Pnet('./weight/MTCNN/12net.h5')
print('Creating Face Detector Rnet....')
Rnet = create_Kao_Rnet('./weight/MTCNN/24net.h5')
print('Creating Face Detector Onet....')
Onet = create_Kao_Onet('./weight/MTCNN/48net.h5')
print('Face Detector Created')
def detectFace(img, threshold):
caffe_img = (img.copy() - 127.5) / 127.5
origin_h, origin_w, ch = caffe_img.shape
scales = calculateScales(img)
out = []
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)
out.append(ouput)
image_num = len(scales)
rectangles = []
for i in range(image_num):
cls_prob = out[i][0][0][:, :, 1]
# elif crop_img.ndim == 2:
# rgb = cvtColor(crop_img, COLOR_GRAY2BGR)
#temp_image = QImage(crop_img.flatten(), width, height, QImage.Format_RGB888)
#temp_pixmap = QPixmap.fromImage(temp_image)
#self.imgeLabel.setPixmap(temp_pixmap)
cv2.imwrite('data/' + str(self.threadID) + 'test.jpg',
crop_img)
return draw
if __name__ == "__main__":
video_path = 'east.mp4'
Pnet = create_Kao_Pnet(r'12net.h5')
Rnet = create_Kao_Rnet(r'24net.h5')
Onet = create_Kao_Onet(r'48net.h5')
cap = cv2.VideoCapture(video_path)
preTime = 0
lock = threading.Lock()
index = 0
img = cv2.imread('0001.png')
scale_img = cv2.resize(img, (100, 100))
input = scale_img.reshape(1, *scale_img.shape)
Pnet.predict(input)
img = cv2.imread('0001.png')
scale_img = cv2.resize(img, (24, 24))
input = scale_img.reshape(1, *scale_img.shape)
Rnet.predict(input)
img = cv2.imread('0001.png')
scale_img = cv2.resize(img, (48, 48))
input = scale_img.reshape(1, *scale_img.shape)
import cv2
import time
import numpy as np
from MTCNN import create_Kao_Onet
detection_model_path = 'models/haarcascade_frontalface_default.xml'
face_detector = cv2.CascadeClassifier(detection_model_path)
# starting video streaming
cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)
Onet = create_Kao_Onet('models/48net.h5')
while True:
bgr_image = video_capture.read()[1]
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
faces = face_detector.detectMultiScale(gray_image, 1.3, 5)
if len(faces) > 0:
for face_coordinates in faces:
batch_in = []
x, y, w, h = face_coordinates
face_crop = bgr_image[y:(y + h), x:(x + w)]
face_crop = cv2.resize(face_crop, (48, 48))
face_crop = (face_crop - 127.5) / 127.5
batch_in.append(cv2.resize(face_crop, (48, 48)))
cv2.rectangle(bgr_image, (x, y), (x + w, y + h), (255, 255, 255),
2)
batch_in = np.array(batch_in)
start = time.time()
