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DragGan / stylegan_human /alignment.py
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# Copyright (c) SenseTime Research. All rights reserved.
import os
import argparse
import numpy as np
import torch
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
from utils.ImagesDataset import ImagesDataset
import cv2
import time
import copy
import imutils
# for openpose body keypoint detector : # (src:https://github.com/Hzzone/pytorch-openpose)
from openpose.src import util
from openpose.src.body import Body
# for paddlepaddle human segmentation : #(src: https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.5/contrib/PP-HumanSeg/)
from PP_HumanSeg.deploy.infer import Predictor as PP_HumenSeg_Predictor
import math
def angle_between_points(p0,p1,p2):
if p0[1]==-1 or p1[1]==-1 or p2[1]==-1:
return -1
a = (p1[0]-p0[0])**2 + (p1[1]-p0[1])**2
b = (p1[0]-p2[0])**2 + (p1[1]-p2[1])**2
c = (p2[0]-p0[0])**2 + (p2[1]-p0[1])**2
if a * b == 0:
return -1
return math.acos((a+b-c) / math.sqrt(4*a*b)) * 180 / math.pi
def crop_img_with_padding(img, keypoints, rect):
person_xmin,person_xmax, ymin, ymax= rect
img_h,img_w,_ = img.shape ## find body center using keypoints
middle_shoulder_x = keypoints[1][0]
middle_hip_x = (keypoints[8][0] + keypoints[11][0]) // 2
mid_x = (middle_hip_x + middle_shoulder_x) // 2
mid_y = (ymin + ymax) // 2
## find which side (l or r) is further than center x, use the further side
if abs(mid_x-person_xmin) > abs(person_xmax-mid_x): #left further
xmin = person_xmin
xmax = mid_x + (mid_x-person_xmin)
else:
############### may be negtive
### in this case, the script won't output any image, leave the case like this
### since we don't want to pad human body
xmin = mid_x - (person_xmax-mid_x)
xmax = person_xmax
w = xmax - xmin
h = ymax - ymin
## pad rectangle to w:h = 1:2 ## calculate desired border length
if h / w >= 2: #pad horizontally
target_w = h // 2
xmin_prime = int(mid_x - target_w / 2)
xmax_prime = int(mid_x + target_w / 2)
if xmin_prime < 0:
pad_left = abs(xmin_prime)# - xmin
xmin = 0
else:
pad_left = 0
xmin = xmin_prime
if xmax_prime > img_w:
pad_right = xmax_prime - img_w
xmax = img_w
else:
pad_right = 0
xmax = xmax_prime
cropped_img = img[int(ymin):int(ymax), int(xmin):int(xmax)]
im_pad = cv2.copyMakeBorder(cropped_img, 0, 0, int(pad_left), int(pad_right), cv2.BORDER_REPLICATE)
else: #pad vertically
target_h = w * 2
ymin_prime = mid_y - (target_h / 2)
ymax_prime = mid_y + (target_h / 2)
if ymin_prime < 0:
pad_up = abs(ymin_prime)# - ymin
ymin = 0
else:
pad_up = 0
ymin = ymin_prime
if ymax_prime > img_h:
pad_down = ymax_prime - img_h
ymax = img_h
else:
pad_down = 0
ymax = ymax_prime
print(ymin,ymax, xmin,xmax, img.shape)
cropped_img = img[int(ymin):int(ymax), int(xmin):int(xmax)]
im_pad = cv2.copyMakeBorder(cropped_img, int(pad_up), int(pad_down), 0,
0, cv2.BORDER_REPLICATE)
result = cv2.resize(im_pad,(512,1024),interpolation = cv2.INTER_AREA)
return result
def run(args):
os.makedirs(args.output_folder, exist_ok=True)
dataset = ImagesDataset(args.image_folder, transforms.Compose([transforms.ToTensor()]))
dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
body_estimation = Body('openpose/model/body_pose_model.pth')
total = len(dataloader)
print('Num of dataloader : ', total)
os.makedirs(f'{args.output_folder}', exist_ok=True)
# os.makedirs(f'{args.output_folder}/middle_result', exist_ok=True)
## initialzide HumenSeg
human_seg_args = {}
human_seg_args['cfg'] = 'PP_HumanSeg/export_model/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax/deploy.yaml'
human_seg_args['input_shape'] = [1024,512]
human_seg_args['save_dir'] = args.output_folder
human_seg_args['soft_predict'] = False
human_seg_args['use_gpu'] = True
human_seg_args['test_speed'] = False
human_seg_args['use_optic_flow'] = False
human_seg_args['add_argmax'] = True
human_seg_args= argparse.Namespace(**human_seg_args)
human_seg = PP_HumenSeg_Predictor(human_seg_args)
from tqdm import tqdm
for fname, image in tqdm(dataloader):
# try:
## tensor to numpy image
fname = fname[0]
print(f'Processing \'{fname}\'.')
image = (image.permute(0, 2, 3, 1) * 255).clamp(0, 255)
image = image.squeeze(0).numpy() # --> tensor to numpy, (H,W,C)
# avoid super high res img
if image.shape[0] >= 2000: # height ### for shein image
ratio = image.shape[0]/1200 #height
dim = (int(image.shape[1]/ratio),1200)#(width, height)
image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
## create segmentation
# mybg = cv2.imread('mybg.png')
comb, segmentation, bg, ori_img = human_seg.run(image,None) #mybg)
# cv2.imwrite('comb.png',comb) # [0,255]
# cv2.imwrite('alpha.png',segmentation*255) # segmentation [0,1] --> [0.255]
# cv2.imwrite('bg.png',bg) #[0,255]
# cv2.imwrite('ori_img.png',ori_img) # [0,255]
masks_np = (segmentation* 255)# .byte().cpu().numpy() #1024,512,1
mask0_np = masks_np[:,:,0].astype(np.uint8)#[0, :, :]
contours = cv2.findContours(mask0_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(contours)
c = max(cnts, key=cv2.contourArea)
extTop = tuple(c[c[:, :, 1].argmin()][0])
extBot = tuple(c[c[:, :, 1].argmax()][0])
extBot = list(extBot)
extTop = list(extTop)
pad_range = int((extBot[1]-extTop[1])*0.05)
if (int(extTop[1])<=5 and int(extTop[1])>0) and (comb.shape[0]>int(extBot[1]) and int(extBot[1])>=comb.shape[0]-5): #seg mask already reaches to the edge
#pad with pure white, top 100 px, bottom 100 px
comb= cv2.copyMakeBorder(comb,pad_range+5,pad_range+5,0,0,cv2.BORDER_CONSTANT,value=[255,255,255])
elif int(extTop[1])<=0 or int(extBot[1])>=comb.shape[0]:
print('PAD: body out of boundary', fname) #should not happened
return {}
else:
comb = cv2.copyMakeBorder(comb, pad_range+5, pad_range+5, 0, 0, cv2.BORDER_REPLICATE) #105 instead of 100: give some extra space
extBot[1] = extBot[1] + pad_range+5
extTop[1] = extTop[1] + pad_range+5
extLeft = tuple(c[c[:, :, 0].argmin()][0])
extRight = tuple(c[c[:, :, 0].argmax()][0])
extLeft = list(extLeft)
extRight = list(extRight)
person_ymin = int(extTop[1])-pad_range # 100
person_ymax = int(extBot[1])+pad_range # 100 #height
if person_ymin<0 or person_ymax>comb.shape[0]: # out of range
return {}
person_xmin = int(extLeft[0])
person_xmax = int(extRight[0])
rect = [person_xmin,person_xmax,person_ymin, person_ymax]
# recimg = copy.deepcopy(comb)
# cv2.rectangle(recimg,(person_xmin,person_ymin),(person_xmax,person_ymax),(0,255,0),2)
# cv2.imwrite(f'{args.output_folder}/middle_result/{fname}_rec.png',recimg)
## detect keypoints
keypoints, subset = body_estimation(comb)
# print(keypoints, subset, len(subset))
if len(subset) != 1 or (len(subset)==1 and subset[0][-1]<15):
print(f'Processing \'{fname}\'. Please import image contains one person only. Also can check segmentation mask. ')
continue
# canvas = copy.deepcopy(comb)
# canvas = util.draw_bodypose(canvas, keypoints, subset, show_number=True)
# cv2.imwrite(f'{args.output_folder}/middle_result/{fname}_keypoints.png',canvas)
comb = crop_img_with_padding(comb, keypoints, rect)
cv2.imwrite(f'{args.output_folder}/{fname}.png', comb)
print(f' -- Finished processing \'{fname}\'. --')
# except:
# print(f'Processing \'{fname}\'. Not satisfied the alignment strategy.')
if __name__ == '__main__':
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
t1 = time.time()
arg_formatter = argparse.ArgumentDefaultsHelpFormatter
description = 'StyleGAN-Human data process'
parser = argparse.ArgumentParser(formatter_class=arg_formatter,
description=description)
parser.add_argument('--image-folder', type=str, dest='image_folder')
parser.add_argument('--output-folder', dest='output_folder', default='results', type=str)
# parser.add_argument('--cfg', dest='cfg for segmentation', default='PP_HumanSeg/export_model/ppseg_lite_portrait_398x224_with_softmax/deploy.yaml', type=str)
print('parsing arguments')
cmd_args = parser.parse_args()
run(cmd_args)
print('total time elapsed: ', str(time.time() - t1))