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load_data.py
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load_data.py
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from torch.utils.data import Dataset
import torch.utils.data as torchdata
import torch
import numpy as np
import os
import json
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import utils
dataset_dir = "./data"
class OMC_CPM(Dataset):
"""
Dataset for OMC
"""
def __init__(self, is_training=True):
super(OMC_CPM, self).__init__()
self.is_training = is_training
if self.is_training:
dir = os.path.join(dataset_dir, 'train_annotation.json')
else:
dir = os.path.join(dataset_dir, 'val_annotation.json')
with open(dir) as f:
dic = json.load(f)
self.feature_list = [item for item in dic['data']]
def __getitem__(self, index):
features = self.feature_list[index]
input_shape = (256,256)
hmap_shape = (32,32)
if(self.is_training==True):
img_folder_dir = os.path.join(dataset_dir, 'train')
else:
img_folder_dir = os.path.join(dataset_dir, 'val')
img_dir = os.path.join(img_folder_dir, features['file'])
img = mpimg.imread(img_dir)
# generate crop image
#print(img)
img_crop, pts_crop, cen_crop = utils.crop(img, features)
pts_crop = np.array(pts_crop)
cen_crop = np.array(cen_crop)
train_img = np.transpose(img_crop, (2,0,1))/255.0
train_heatmaps = utils.gen_hmaps(np.zeros(hmap_shape), pts_crop/8)
train_heatmaps = np.transpose(train_heatmaps, (2,0,1))
train_centermap = utils.gen_cmap(np.zeros(input_shape), cen_crop)
train_centermap = np.expand_dims(train_centermap, axis=0)
return train_img, train_heatmaps, train_centermap
def __len__(self):
return len(self.feature_list)
def collate_fn(self, batch):
imgs, heatmaps, centermap = list(zip(*batch))
imgs = np.stack(imgs, axis=0)
heatmaps = np.stack(heatmaps, axis=0)
centermap = np.stack(centermap, axis=0)
return imgs, heatmaps, centermap
class OMC_HRNET(Dataset):
"""
Dataset for OMC using hrnet
"""
def __init__(self, is_training=True):
super(OMC_HRNET, self).__init__()
self.is_training = is_training
if self.is_training:
dir = os.path.join(dataset_dir, 'train_annotation.json')
else:
dir = os.path.join(dataset_dir, 'val_annotation.json')
with open(dir) as f:
dic = json.load(f)
self.feature_list = [item for item in dic['data']]
def __getitem__(self, index):
features = self.feature_list[index]
input_shape = (256,256)
hmap_shape = (64,64)
if(self.is_training==True):
img_folder_dir = os.path.join(dataset_dir, 'train')
else:
img_folder_dir = os.path.join(dataset_dir, 'val')
img_dir = os.path.join(img_folder_dir, features['file'])
img = mpimg.imread(img_dir)
# generate crop image
#print(img)
img_crop, pts_crop, _ = utils.crop(img, features)
pts_crop = np.array(pts_crop)
train_img = np.transpose(img_crop, (2,0,1))/255.0
train_heatmaps = utils.gen_hmaps(np.zeros(hmap_shape), pts_crop/4)
train_heatmaps = np.transpose(train_heatmaps, (2,0,1))
return train_img, train_heatmaps
def __len__(self):
return len(self.feature_list)
def collate_fn(self, batch):
imgs, heatmaps = list(zip(*batch))
imgs = np.stack(imgs, axis=0)
heatmaps = np.stack(heatmaps, axis=0)
return imgs, heatmaps
class OMC_CPM_UNet(Dataset):
"""
Dataset for OMC
"""
def __init__(self, is_training=True):
super(OMC_CPM_UNet, self).__init__()
self.is_training = is_training
if self.is_training:
dir = os.path.join(dataset_dir, 'train_annotation.json')
else:
dir = os.path.join(dataset_dir, 'val_annotation.json')
with open(dir) as f:
dic = json.load(f)
self.feature_list = [item for item in dic['data']]
def __getitem__(self, index):
features = self.feature_list[index]
input_shape = (256, 256)
if(self.is_training==True):
img_folder_dir = os.path.join(dataset_dir, 'train')
else:
img_folder_dir = os.path.join(dataset_dir, 'val')
img_dir = os.path.join(img_folder_dir, features['file'])
img = mpimg.imread(img_dir)
# generate crop image
#print(img)
img_crop, pts_crop, cen_crop = utils.crop(img, features)
pts_crop = np.array(pts_crop)
cen_crop = np.array(cen_crop)
train_img = np.transpose(img_crop, (2,0,1))/255.0
# for net.py, use input_shape
train_heatmaps = utils.gen_hmaps(np.zeros(input_shape), pts_crop)
train_heatmaps = np.transpose(train_heatmaps, (2,0,1))
train_centermap = utils.gen_cmap(np.zeros(input_shape), cen_crop)
train_centermap = np.expand_dims(train_centermap, axis=0)
return train_img, train_heatmaps, train_centermap
def __len__(self):
return len(self.feature_list)
def collate_fn(self, batch):
imgs, heatmaps, centermap = list(zip(*batch))
imgs = np.stack(imgs, axis=0)
heatmaps = np.stack(heatmaps, axis=0)
centermap = np.stack(centermap, axis=0)
return imgs, heatmaps, centermap
class test_OMC(Dataset):
"""
Dataset for OMC
"""
def __init__(self, is_training=False):
super(test_OMC, self).__init__()
dir = os.path.join(dataset_dir, 'test_prediction.json')
with open(dir) as f:
dic = json.load(f)
self.feature_list = [item for item in dic['data']]
def __getitem__(self, index):
features = self.feature_list[index]
input_shape = (256, 256)
img_folder_dir = os.path.join(dataset_dir, 'test')
img_dir = os.path.join(img_folder_dir, features['file'])
img = mpimg.imread(img_dir)
# generate crop image
#print(img)
img_crop, cen_crop, bb = utils.crop_test(img, features)
cen_crop = np.array(cen_crop)
test_img = np.transpose(img_crop, (2,0,1))/255.0
test_centermap = utils.gen_cmap(np.zeros(input_shape), cen_crop)
test_centermap = np.expand_dims(test_centermap, axis=0)
return test_img, test_centermap, bb
def __len__(self):
return len(self.feature_list)
def collate_fn(self, batch):
imgs, centermap, bb = list(zip(*batch))
imgs = np.stack(imgs, axis=0)
centermap = np.stack(centermap, axis=0)
bb = np.stack(bb, axis=0)
return imgs, centermap, bb
class test_OMC_HRNET(Dataset):
"""
Dataset for OMC
"""
def __init__(self, is_training=False):
super(test_OMC_HRNET, self).__init__()
dir = os.path.join(dataset_dir, 'test_prediction.json')
with open(dir) as f:
dic = json.load(f)
self.feature_list = [item for item in dic['data']]
def __getitem__(self, index):
features = self.feature_list[index]
img_folder_dir = os.path.join(dataset_dir, 'test')
img_dir = os.path.join(img_folder_dir, features['file'])
img = mpimg.imread(img_dir)
# generate crop image
#print(img)
img_crop, _, bb = utils.crop_test(img, features)
test_img = np.transpose(img_crop, (2,0,1))/255.0
return test_img, bb
def __len__(self):
return len(self.feature_list)
def collate_fn(self, batch):
imgs, bb = list(zip(*batch))
imgs = np.stack(imgs, axis=0)
bb = np.stack(bb, axis=0)
return imgs, bb
class sanity_check_OMC(Dataset):
"""
Dataset for OMC
"""
def __init__(self, is_training=False):
super(sanity_check_OMC, self).__init__()
dir = os.path.join(dataset_dir, 'val_annotation.json')
with open(dir) as f:
dic = json.load(f)
self.feature_list = [item for item in dic['data']]
def __getitem__(self, index):
features = self.feature_list[index]
input_shape = (256, 256)
img_folder_dir = os.path.join(dataset_dir, 'val')
img_dir = os.path.join(img_folder_dir, features['file'])
img = mpimg.imread(img_dir)
# generate crop image
#print(img)
img_crop, cen_crop, landmarks, bb = utils.crop_check(img, features)
cen_crop = np.array(cen_crop)
test_img = np.transpose(img_crop, (2,0,1)) / 255.0
test_centermap = utils.gen_cmap(np.zeros(input_shape), cen_crop)
test_centermap = np.expand_dims(test_centermap, axis=0)
return test_img, test_centermap, bb, landmarks
def __len__(self):
return len(self.feature_list)
def collate_fn(self, batch):
imgs, centermap, bb, landmarks = list(zip(*batch))
imgs = np.stack(imgs, axis=0)
centermap = np.stack(centermap, axis=0)
bb = np.stack(bb, axis=0)
landmarks = np.stack(landmarks, axis=0)
return imgs, centermap, bb, landmarks
def main():
#plt.ion()
omc = OMC_CPM(is_training=True)
dataloader = torchdata.DataLoader(omc, batch_size=1, shuffle=True, collate_fn=omc.collate_fn)
for i, (img, heatmap, centermap) in enumerate(dataloader):
#print(img_crop[0].shape)
#imgutils.show_stack_joints(img_crop[0], pts_crop[0], cen_crop[0], num_fig=2*i+1)
#imgutils.show_stack_joints(img[0], pts[0], cen[0], num_fig=2*i+2)
utils.show_heatmaps(img[0].transpose(1,2,0), heatmap[0].transpose(1,2,0))
#plt.pause(5)
if i == 0:
break
#plt.ioff()
if __name__ == "__main__":
main()