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import cv2
import torch
from model import U2NET
from torch.autograd import Variable
import numpy as np
from glob import glob
import os
def detect_single_face(face_cascade,img):
# Convert into grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
if(len(faces)==0):
print("Warming: no face detection, the portrait u2net will run on the whole image!")
return None
# filter to keep the largest face
wh = 0
idx = 0
for i in range(0,len(faces)):
(x,y,w,h) = faces[i]
if(wh<w*h):
idx = i
wh = w*h
return faces[idx]
# crop, pad and resize face region to 512x512 resolution
def crop_face(img, face):
# no face detected, return the whole image and the inference will run on the whole image
if(face is None):
return img
(x, y, w, h) = face
height,width = img.shape[0:2]
# crop the face with a bigger bbox
hmw = h - w
# hpad = int(h/2)+1
# wpad = int(w/2)+1
l,r,t,b = 0,0,0,0
lpad = int(float(w)*0.4)
left = x-lpad
if(left<0):
l = lpad-x
left = 0
rpad = int(float(w)*0.4)
right = x+w+rpad
if(right>width):
r = right-width
right = width
tpad = int(float(h)*0.6)
top = y - tpad
if(top<0):
t = tpad-y
top = 0
bpad = int(float(h)*0.2)
bottom = y+h+bpad
if(bottom>height):
b = bottom-height
bottom = height
im_face = img[top:bottom,left:right]
if(len(im_face.shape)==2):
im_face = np.repeat(im_face[:,:,np.newaxis],(1,1,3))
im_face = np.pad(im_face,((t,b),(l,r),(0,0)),mode='constant',constant_values=((255,255),(255,255),(255,255)))
# pad to achieve image with square shape for avoding face deformation after resizing
hf,wf = im_face.shape[0:2]
if(hf-2>wf):
wfp = int((hf-wf)/2)
im_face = np.pad(im_face,((0,0),(wfp,wfp),(0,0)),mode='constant',constant_values=((255,255),(255,255),(255,255)))
elif(wf-2>hf):
hfp = int((wf-hf)/2)
im_face = np.pad(im_face,((hfp,hfp),(0,0),(0,0)),mode='constant',constant_values=((255,255),(255,255),(255,255)))
# resize to have 512x512 resolution
im_face = cv2.resize(im_face, (512,512), interpolation = cv2.INTER_AREA)
return im_face
def normPRED(d):
ma = torch.max(d)
mi = torch.min(d)
dn = (d-mi)/(ma-mi)
return dn
def inference(net,input):
# normalize the input
tmpImg = np.zeros((input.shape[0],input.shape[1],3))
input = input/np.max(input)
tmpImg[:,:,0] = (input[:,:,2]-0.406)/0.225
tmpImg[:,:,1] = (input[:,:,1]-0.456)/0.224
tmpImg[:,:,2] = (input[:,:,0]-0.485)/0.229
# convert BGR to RGB
tmpImg = tmpImg.transpose((2, 0, 1))
tmpImg = tmpImg[np.newaxis,:,:,:]
tmpImg = torch.from_numpy(tmpImg)
# convert numpy array to torch tensor
tmpImg = tmpImg.type(torch.FloatTensor)
if torch.cuda.is_available():
tmpImg = Variable(tmpImg.cuda())
else:
tmpImg = Variable(tmpImg)
# inference
d1,d2,d3,d4,d5,d6,d7= net(tmpImg)
# normalization
pred = 1.0 - d1[:,0,:,:]
pred = normPRED(pred)
# convert torch tensor to numpy array
pred = pred.squeeze()
pred = pred.cpu().data.numpy()
del d1,d2,d3,d4,d5,d6,d7
return pred
def main():
# get the image path list for inference
im_list = glob('./test_data/test_portrait_images/your_portrait_im/*')
print("Number of images: ",len(im_list))
# indicate the output directory
out_dir = './test_data/test_portrait_images/your_portrait_results'
if(not os.path.exists(out_dir)):
os.mkdir(out_dir)
# Load the cascade face detection model
face_cascade = cv2.CascadeClassifier('./saved_models/face_detection_cv2/haarcascade_frontalface_default.xml')
# u2net_portrait path
model_dir = './saved_models/u2net_portrait/u2net_portrait.pth'
# load u2net_portrait model
net = U2NET(3,1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# do the inference one-by-one
for i in range(0,len(im_list)):
print("--------------------------")
print("inferencing ", i, "/", len(im_list), im_list[i])
# load each image
img = cv2.imread(im_list[i])
height,width = img.shape[0:2]
face = detect_single_face(face_cascade,img)
im_face = crop_face(img, face)
im_portrait = inference(net,im_face)
# save the output
cv2.imwrite(out_dir+"/"+im_list[i].split('/')[-1][0:-4]+'.png',(im_portrait*255).astype(np.uint8))
if __name__ == '__main__':
main()
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