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data_load.py
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data_load.py
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import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.dataset import random_split
import torchvision
import torch
import torch.nn as nn
#import torch.nn.functional as F
import torchvision.transforms.functional as F
from torchvision.utils import save_image
import math
import torch.optim as optim
import os
import random
import natsort
import cv2
from PIL import Image, ImageFilter, ImageOps
import pandas
import matplotlib.pyplot as plt
# References
# https://github.com/FunkyKoki/Laplace_Landmark_Localization/blob/e8067bcd90c66227676cc1482e9defc3c9baa659/datasets/datasetsTools.py#L100
# https://tutorials.pytorch.kr/beginner/data_loading_tutorial.html
# https://towardsdatascience.com/face-landmarks-detection-with-pytorch-4b4852f5e9c4
class FaceLandMark_Loader(Dataset):
def __init__(self, root, args, IsAug = True):
super(FaceLandMark_Loader, self).__init__()
print("#### MotionLoader ####")
print("####### load data from {} ######".format(root))
# read list of images
# read list of landmarks
# read list of bbox
self.img_path = os.path.join(root, "img")
self.ldmks_path = os.path.join(root, "ldmks")
self.bbox_leftcorner_coord_path = os.path.join(root, "bbox_leftcorner_coord")
self.img_list = natsort.natsorted(os.listdir(self.img_path))
self.ldmks_list = natsort.natsorted(os.listdir(self.ldmks_path))
self.bbox_list = natsort.natsorted(os.listdir(self.bbox_leftcorner_coord_path))
assert len(self.img_list) == len(self.ldmks_list)
assert len(self.img_list) == len(self.bbox_list)
self.IsAug = IsAug
self.totensor = torchvision.transforms.ToTensor()
self.greyscale = torchvision.transforms.Grayscale(num_output_channels=3)
self.blurrer = torchvision.transforms.GaussianBlur\
(kernel_size=(args.GaussianBlur_kernel_w, args.GaussianBlur_kernel_h), sigma=(args.GaussianBlur_sigma_min, args.GaussianBlur_sigma_max))
self.perspective_transformer = torchvision.transforms.RandomPerspective(distortion_scale=args.perspective_distortion_scale, p=args.perspective_distortion_prob)
self.rotation_max_angle = args.rotation_max_angle
self.noise_std_scale = args.noise_std_scale
self.grayscale_prob = args.grayscale_prob
self.brightness_factor_min = args.brightness_factor_min
self.brightness_factor_max = args.brightness_factor_max
self.contrast_factor_min = args.contrast_factor_min
self.contrast_factor_max = args.contrast_factor_max
#print(self.img_list[:10])
#print(self.ldmks_list[:10])
def __getitem__(self, idx):
img = Image.open(self.img_path + '/' + self.img_list[idx])
ldmks = pandas.read_csv(self.ldmks_path +'/'+self.ldmks_list[idx], header=None, sep=' ')
ldmks = np.asarray(ldmks) # shape : (70, 2), last two row for centers of eyes
bbox_leftcorner = pandas.read_csv(self.bbox_leftcorner_coord_path +'/'+self.bbox_list[idx], header=None, sep=' ')
bbox_leftcorner = np.asarray(bbox_leftcorner) # shape : (2, 1) # x, y
#crop image 256x256 including face info --> how? # crop이 가장 위에 가야지 계산 효율성이 좋을 듯!
crop_img = img.crop((bbox_leftcorner[0][0], bbox_leftcorner[0][1], bbox_leftcorner[0][0]+ 256, bbox_leftcorner[0][1] + 256))
crop_ladmks = self._landmark_processing4crop(ldmks, bbox_leftcorner)
crop_img = self.totensor(crop_img)
if self.IsAug == True:
crop_img = F.adjust_brightness(crop_img, brightness_factor = random.uniform(self.brightness_factor_min,self.brightness_factor_max)) # brightness_factor 0(black) ~ 2(white)
crop_img = F.adjust_contrast(crop_img, contrast_factor = random.uniform(self.contrast_factor_min,self.contrast_factor_max)) # contrast_factor 0(solid gray) ~ 2
is_gray = random.randint(0,self.grayscale_prob) # 25% conduct gray scale
if is_gray == 1:
crop_img = self.greyscale(crop_img)
else:
crop_img = crop_img
#Add Noise
crop_img = crop_img + torch.randn_like(crop_img) * self.noise_std_scale
#Blur
crop_img = self.blurrer(crop_img)
#perspective_warp
crop_img, crop_ladmks = self._perspective_warp(crop_img , crop_ladmks)
angle = random.randint(0, self.rotation_max_angle)
crop_img = F.rotate(crop_img, angle)
crop_ladmks = self._rotate(crop_ladmks, angle = angle)
return np.array(img), ldmks, crop_img, torch.Tensor(crop_ladmks), bbox_leftcorner
#return np.array(img), ldmks, crop_img, crop_ladmks, bbox_leftcorner # for test module(here)
def __len__(self):
return len(self.img_list)
def _landmark_processing4crop(self, ldmks, bbox_leftcorner):
#make ldmks point corresponding to cropped image
#ldmks = ldmks - [bbox_leftcorner[0][1] ,bbox_leftcorner[0][0]] # landmarks = landmarks - [left, top]
ldmks = ldmks - [bbox_leftcorner[0][0] ,bbox_leftcorner[0][1]]
return ldmks
def _gray_scaling(self, crop_img):
'''
input : PIL_img
return 3-channel gray img
'''
crop_img_rgb_to_grayscale = ImageOps.grayscale(crop_img)
crop_img_rgb_to_grayscale = np.expand_dims(np.array(crop_img_rgb_to_grayscale), axis = -1)
crop_img_rgb_to_grayscale = np.concatenate((crop_img_rgb_to_grayscale, crop_img_rgb_to_grayscale, crop_img_rgb_to_grayscale), axis = 2)
return crop_img_rgb_to_grayscale
def _rotate(self, crop_ladmks, angle = 0, imgWidth =256, imgHeight =256):
'''
input : array
ouput : array
conduct rotate
'''
#rotated_img = crop_img.rotate(angle)
#crop_img = np.array(crop_img)
rad = math.radians(angle)
c, s = np.cos(rad), np.sin(rad)
rot_mat = np.array(((c,-s), (s, c)))
center = np.array([imgWidth / 2.0, imgHeight / 2.0], dtype=np.float32)
#center -> 0 , rotate , zero center -> original center
#rotated_img = cv2.warpAffine(crop_img, cv2.getRotationMatrix2D(center, angle, 1.0), (imgWidth, imgHeight))
rotated_ladmks = np.matmul(crop_ladmks - center, rot_mat) + center
return rotated_ladmks
def _perspective_warp(self, crop_img, crop_ladmks, imgWidth =256, imgHeight =256):
'''
input : array
ouput : array
conduct perspective_warp
'''
#print(crop_img.shape)
_, height, width = crop_img.shape
#[x, y]
topLeft = [0,0]
topRight = [width -1, 0]
bottomRight = [width -1, height - 1]
bottomLeft = [0, height - 1]
origin_pts = [topLeft, topRight, bottomRight, bottomLeft]
change_range = 30
trans_topLeft = [0 + random.randint(0, change_range), 0 + random.randint(0, change_range)]
trans_topRight = [width - 1 - random.randint(0, change_range), 0 + random.randint(0, change_range)]
trans_bottomRight = [width - 1 - random.randint(0, change_range), height - 1 - random.randint(0, change_range)]
trans_bottomLeft = [0 + random.randint(0, change_range), height - 1 - random.randint(0, change_range)]
transform_pts = [trans_topLeft, trans_topRight, trans_bottomRight, trans_bottomLeft]
#print("origin_pts: ", origin_pts)
#print("transform_pts: ", transform_pts)
crop_img = F.perspective(crop_img, origin_pts, transform_pts)
mtrx = cv2.getPerspectiveTransform(np.float32(origin_pts), np.float32(transform_pts))
#print("mtrx.transpose():", mtrx[:2, :].shape)
crop_ladmks = np.concatenate((crop_ladmks, np.ones((70,1))), axis = 1)
#print("crop_ladmks.shape: ", crop_ladmks.shape)
crop_ladmks = np.matmul(crop_ladmks, mtrx[:, :].transpose()) # =--> x, y, w(projection vector)
crop_ladmks[:, 0] = crop_ladmks[:, 0] / crop_ladmks[:, 2]
crop_ladmks[:, 1] = crop_ladmks[:, 1] / crop_ladmks[:, 2]
#print("new crop_ladmks.shape: ", crop_ladmks.shape)
return crop_img, crop_ladmks[:,:2]
def get_dataloader(args, IsSuffle = True, num_workers = 16, IsAug =True, train_val_ratio = 0.80):
dataset = FaceLandMark_Loader(args.datasetPath, args, IsAug = IsAug)
print("# of dataset:", len(dataset))
train_dataset, valid_dataset = random_split(dataset, [int(len(dataset) * train_val_ratio), len(dataset)-int(len(dataset) * train_val_ratio)])
print("# of train dataset:", len(train_dataset))
print("# of valid dataset:", len(valid_dataset))
train_dataloader = DataLoader(train_dataset, batch_size=args.batchSize, shuffle=IsSuffle, drop_last=True, num_workers = num_workers)
valid_dataloader = DataLoader(valid_dataset, batch_size=args.batchSize, shuffle=IsSuffle, drop_last=True, num_workers = num_workers)
return train_dataloader, valid_dataloader
def get_test_dataloader(dataroot, IsSuffle = True, num_workers = 0):
dataset = FaceLandMark_Loader(dataroot)
print("# of dataset:", len(dataset))
test_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=True, drop_last=True, num_workers = num_workers)
return test_dataloader
if __name__ == '__main__':
root = "/data2/MS-FaceSynthetic"
#print_all_augmented_images(root)
dataset = FaceLandMark_Loader(root = "/data2/MS-FaceSynthetic")
data_loader = DataLoader(dataset, batch_size=4, shuffle=True, drop_last=True)
for idx, item in enumerate(data_loader):
img_GT, landmark_GT, crop_img, crop_ladmks, bbox_leftcorner = item
crop_img = crop_img.cuda()
crop_ladmks = crop_ladmks.cuda()
#crop_ladmks = crop_ladmks.to(device, dtype=torch.float)
print("img_GT.shape: ", img_GT.shape)
print("landmark_GT.shape: ", landmark_GT.shape)
print("crop_img.shape: ", crop_img.shape)
print("crop_ladmks.shape: ", crop_ladmks.shape)
print("bbox_leftcorner.shape: ", bbox_leftcorner.shape)
#crop_img_result = crop_img[0]cpu().numpy()
# Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
import utils
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--name', type=str)
parser.add_argument('--datasetPath', type=str, default="/data2/MS-FaceSynthetic")
parser.add_argument('--saveDir', type=str, default='/personal/GiHoonKim/face_ldmk_detection')
args = parser.parse_args()
saveUtils = utils.saveData(args)
crop_ladmks = crop_ladmks.cpu()
saveUtils.save_visualization(crop_img, crop_ladmks, crop_ladmks, 0)
crop_img_result = crop_img[0].mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
print("crop_img_result.shape: ", crop_img_result.shape)
plt.imshow(crop_img_result)
plt.scatter(crop_ladmks[0, :, 0], crop_ladmks[0, :, 1], s=10, marker='.', c='g')
plt.savefig('data_load_sample_test.png')
break
print("############ Done ############")