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test_batch_detections.py
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test_batch_detections.py
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# Face alignment and crop demo
# Uses MTCNN, FaceBoxes or Retinaface as a face detector;
# Support different backbones, include PFLD, MobileFaceNet, MobileNet;
# Retinaface+MobileFaceNet gives the best peformance
# Cunjian Chen (ccunjian@gmail.com), Feb. 2021
from __future__ import division
import argparse
import torch
import os
import cv2
import numpy as np
from common.utils import BBox,drawLandmark,drawLandmark_multiple
from models.basenet import MobileNet_GDConv
from models.pfld_compressed import PFLDInference
from models.mobilefacenet import MobileFaceNet
from FaceBoxes import FaceBoxes
from Retinaface import Retinaface
from PIL import Image
import matplotlib.pyplot as plt
from MTCNN import detect_faces
import glob
import time
from utils.align_trans import get_reference_facial_points, warp_and_crop_face
parser = argparse.ArgumentParser(description='PyTorch face landmark')
# Datasets
parser.add_argument('--backbone', default='MobileFaceNet', type=str,
help='choose which backbone network to use: MobileNet, PFLD, MobileFaceNet')
parser.add_argument('--detector', default='Retinaface', type=str,
help='choose which face detector to use: MTCNN, FaceBoxes, Retinaface')
args = parser.parse_args()
mean = np.asarray([ 0.485, 0.456, 0.406 ])
std = np.asarray([ 0.229, 0.224, 0.225 ])
crop_size= 112
scale = crop_size / 112.
reference = get_reference_facial_points(default_square = True) * scale
if torch.cuda.is_available():
map_location=lambda storage, loc: storage.cuda()
else:
map_location='cpu'
def load_model():
if args.backbone=='MobileNet':
model = MobileNet_GDConv(136)
model = torch.nn.DataParallel(model)
# download model from https://drive.google.com/file/d/1Le5UdpMkKOTRr1sTp4lwkw8263sbgdSe/view?usp=sharing
checkpoint = torch.load('checkpoint/mobilenet_224_model_best_gdconv_external.pth.tar', map_location=map_location)
print('Use MobileNet as backbone')
elif args.backbone=='PFLD':
model = PFLDInference()
# download from https://drive.google.com/file/d/1gjgtm6qaBQJ_EY7lQfQj3EuMJCVg9lVu/view?usp=sharing
checkpoint = torch.load('checkpoint/pfld_model_best.pth.tar', map_location=map_location)
print('Use PFLD as backbone')
# download from https://drive.google.com/file/d/1T8J73UTcB25BEJ_ObAJczCkyGKW5VaeY/view?usp=sharing
elif args.backbone=='MobileFaceNet':
model = MobileFaceNet([112, 112],136)
checkpoint = torch.load('checkpoint/mobilefacenet_model_best.pth.tar', map_location=map_location)
print('Use MobileFaceNet as backbone')
else:
print('Error: not suppored backbone')
model.load_state_dict(checkpoint['state_dict'])
return model
if __name__ == '__main__':
if args.backbone=='MobileNet':
out_size = 224
else:
out_size = 112
model = load_model()
model = model.eval()
filenames=glob.glob("samples/12--Group/*.jpg")
for imgname in filenames:
print(imgname)
img = cv2.imread(imgname)
org_img = Image.open(imgname)
height,width,_=img.shape
if args.detector=='MTCNN':
# perform face detection using MTCNN
image = Image.open(imgname)
faces, landmarks = detect_faces(image)
elif args.detector=='FaceBoxes':
face_boxes = FaceBoxes()
faces = face_boxes(img)
elif args.detector=='Retinaface':
retinaface=Retinaface.Retinaface()
faces = retinaface(img)
else:
print('Error: not suppored detector')
ratio=0
if len(faces)==0:
print('NO face is detected!')
continue
for k, face in enumerate(faces):
if face[4]<0.9: # remove low confidence detection
continue
x1=face[0]
y1=face[1]
x2=face[2]
y2=face[3]
w = x2 - x1 + 1
h = y2 - y1 + 1
size = int(min([w, h])*1.2)
cx = x1 + w//2
cy = y1 + h//2
x1 = cx - size//2
x2 = x1 + size
y1 = cy - size//2
y2 = y1 + size
dx = max(0, -x1)
dy = max(0, -y1)
x1 = max(0, x1)
y1 = max(0, y1)
edx = max(0, x2 - width)
edy = max(0, y2 - height)
x2 = min(width, x2)
y2 = min(height, y2)
new_bbox = list(map(int, [x1, x2, y1, y2]))
new_bbox = BBox(new_bbox)
cropped=img[new_bbox.top:new_bbox.bottom,new_bbox.left:new_bbox.right]
if (dx > 0 or dy > 0 or edx > 0 or edy > 0):
cropped = cv2.copyMakeBorder(cropped, int(dy), int(edy), int(dx), int(edx), cv2.BORDER_CONSTANT, 0)
cropped_face = cv2.resize(cropped, (out_size, out_size))
if cropped_face.shape[0]<=0 or cropped_face.shape[1]<=0:
continue
test_face = cropped_face.copy()
test_face = test_face/255.0
if args.backbone=='MobileNet':
test_face = (test_face-mean)/std
test_face = test_face.transpose((2, 0, 1))
test_face = test_face.reshape((1,) + test_face.shape)
input = torch.from_numpy(test_face).float()
input= torch.autograd.Variable(input)
start = time.time()
if args.backbone=='MobileFaceNet':
landmark = model(input)[0].cpu().data.numpy()
else:
landmark = model(input).cpu().data.numpy()
end = time.time()
print('Time: {:.6f}s.'.format(end - start))
landmark = landmark.reshape(-1,2)
landmark = new_bbox.reprojectLandmark(landmark)
img = drawLandmark_multiple(img, new_bbox, landmark)
# crop and aligned the face
lefteye_x=0
lefteye_y=0
for i in range(36,42):
lefteye_x+=landmark[i][0]
lefteye_y+=landmark[i][1]
lefteye_x=lefteye_x/6
lefteye_y=lefteye_y/6
lefteye=[lefteye_x,lefteye_y]
righteye_x=0
righteye_y=0
for i in range(42,48):
righteye_x+=landmark[i][0]
righteye_y+=landmark[i][1]
righteye_x=righteye_x/6
righteye_y=righteye_y/6
righteye=[righteye_x,righteye_y]
nose=landmark[33]
leftmouth=landmark[48]
rightmouth=landmark[54]
facial5points=[righteye,lefteye,nose,rightmouth,leftmouth]
warped_face = warp_and_crop_face(np.array(org_img), facial5points, reference, crop_size=(crop_size, crop_size))
img_warped = Image.fromarray(warped_face)
# save the aligned and cropped faces
img_warped.save(os.path.join('results_aligned', os.path.basename(imgname)[:-4]+'_'+str(k)+'.png'))
#img = drawLandmark_multiple(img, new_bbox, facial5points) # plot and show 5 points
# save the landmark detections
cv2.imwrite(os.path.join('results',os.path.basename(imgname)),img)