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data_loader_fg_model.py
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data_loader_fg_model.py
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import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image, ImageDraw
import os
import torchvision
from utils import imshow
class CocoData(Dataset):
"""
Args:
root (string): Root directory where images are downloaded to.
annFile (string): Path to json annotation file.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.ToTensor``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
category_names : name of the categories desired dataset consists
final_img_size : Dataset image size, default: 128
Return:
'image' : 3x128x128
'segmentation mask' : num_catx128x128 --- only one instance for specific category (one instance for each category)
'category' : multiple categories (e.g. zebra, giraffe)
"""
def __init__(self, root, annFile, transform=None, target_transform=None, category_names = None, final_img_size=128):
from pycocotools.coco import COCO
self.root = root
self.coco = COCO(annFile)
self.ids = list(self.coco.imgs.keys())
self.transform = transform
self.target_transform = target_transform
self.final_img_size = final_img_size
self.transform2 = transforms.Compose([
transforms.Scale((final_img_size,final_img_size)),
transforms.ToTensor(),
])
if category_names == None:
self.category = None
self.ids = list(self.coco.imgs.keys())
else:
self.category = self.coco.getCatIds(catNms=category_names) #e.g. [22,25]
self.ids = []
self.cat = []
for x in self.category:
self.ids += self.coco.getImgIds(catIds=x )
self.cat += [x]*len(self.coco.getImgIds(catIds=x )) #e.g. [22,22,...,22]
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.
"""
coco = self.coco
img_id = self.ids[index]
ann_ids = coco.getAnnIds(imgIds=img_id)
target = coco.loadAnns(ann_ids)
path = coco.loadImgs(img_id)[0]['file_name']
valid_instance = False
num_iter = 0
while valid_instance == False and (num_iter < 5):
img = Image.open(os.path.join(self.root, path)).convert('RGB')
img_size = img.size
img_size_x = img_size[0]
img_size_y = img_size[1]
seg_masks = torch.zeros([len(self.category),self.final_img_size,self.final_img_size])
single_fg_mask = torch.zeros([len(self.category),self.final_img_size,self.final_img_size])
instance_types = []
for i in range(len(target)):
instance = target[i]
instance_types.append(instance['category_id'])
idx_list = [i for i in range(len(instance_types)) if (instance_types[i] in self.category and len(target[i]['segmentation'])>=1)]
num_object = len(idx_list)
for i in range(num_object):
idx = idx_list[np.random.choice(len(idx_list),1)[0]]
idx_list.remove(idx)
instance = target[idx]
mask = Image.new('L', (img_size_x, img_size_y))
for j in range(len(instance['segmentation'])):
poly = instance['segmentation'][j]
ImageDraw.Draw(mask).polygon(poly, outline=1, fill=1)
if i==0:
bbox = instance['bbox']
mask_instance_1 = mask
bbox_mask = Image.new('L', (img_size_x, img_size_y))
ImageDraw.Draw(bbox_mask).rectangle([bbox[0],bbox[1],bbox[0]+bbox[2],bbox[1]+bbox[3]], outline=1, fill=1)
bbox_mask= self.transform2(bbox_mask)
if torch.max(bbox_mask) != 0:
bbox_mask = bbox_mask/torch.max(bbox_mask)
mask= self.transform2(mask)
if torch.max(mask) != 0:
mask = mask/torch.max(mask)
seg_masks[self.category.index(instance['category_id']),:,:] += mask
#Single foreground object mask
if i==0:
single_fg_obj_ctg = self.category.index(instance['category_id'])
single_fg_mask[single_fg_obj_ctg,:,:] = mask
if self.transform is not None:
img = self.transform(img)
seg_masks = torch.clamp(seg_masks,0,1)
###bounding-box of the object in resized image
if bbox[2]>bbox[3]:
dx = 0
dy = (bbox[2]-bbox[3]) /2
else:
dx = (bbox[3]-bbox[2]) /2
dy = 0
x1 = max(0,bbox[0]-dx)
y1 = max(0,bbox[1]-dy)
x2 = min(img_size_x,bbox[0]+bbox[2]+dx)
y2 = min(img_size_y,bbox[1]+bbox[3]+dy)
mask_instance_1 = mask_instance_1.crop((int(x1), int(y1), int(x2), int(y2) ))
mask_instance_1 = self.transform2(mask_instance_1)
if torch.max(mask_instance_1) != 0:
mask_instance_1 = mask_instance_1/torch.max(mask_instance_1)
mask_instance = torch.zeros([len(self.category),self.final_img_size,self.final_img_size])
mask_instance[single_fg_obj_ctg,:,:] = mask_instance_1
x_scale = self.final_img_size/img_size_x
y_scale = self.final_img_size/img_size_y
x1, x2 = x1*x_scale, x2*x_scale
y1, y2 = y1*y_scale, y2*y_scale
#bbox_scaled = [y1,y2,x1,x2]
bbox_scaled = [int(y1),int(y2),int(x1),int(x2)]
num_iter += 1
if (bbox_scaled[1]>bbox_scaled[0]) and (bbox_scaled[3]>bbox_scaled[2]):
valid_instance = True
if not (bbox_scaled[1]>bbox_scaled[0]):
if bbox_scaled[1]<self.final_img_size:
bbox_scaled[1] += 1
else:
bbox_scaled[0] -= 1
if not (bbox_scaled[3]>bbox_scaled[2]):
if bbox_scaled[3]<self.final_img_size:
bbox_scaled[3] += 1
else:
bbox_scaled[2] -= 1
sample = {'image': img, 'seg_mask': seg_masks,'single_fg_mask': single_fg_mask, 'mask_instance':mask_instance, 'bbox':bbox_scaled, 'cat': self.cat[index], 'num_object':num_object}
return sample
def __len__(self):
return len(self.ids)
def discard_small(self, min_area, max_area=1):
temp = []
for img_id in self.ids:
ann_ids = self.coco.getAnnIds(imgIds=img_id)
target = self.coco.loadAnns(ann_ids)
instance_types = []
valid_mask = False
path = self.coco.loadImgs(img_id)[0]['file_name']
img = Image.open(os.path.join(self.root, path))
img_size = img.size
img_size_x = img_size[0]
img_size_y = img_size[1]
total_fg_area = 0
total_fg_area_relevant = 0
for i in range(len(target)):
instance = target[i]
total_fg_area += instance['area']
instance_types.append(instance['category_id'])
if instance['category_id'] in self.category and len(instance['segmentation'])>=1:
total_fg_area_relevant += instance['area']
valid_mask = True
if (instance['category_id'] in self.category) and (type(instance['segmentation']) is not list):
valid_mask = False
break
if valid_mask and total_fg_area_relevant/(img_size_x*img_size_y) > min_area and total_fg_area/(img_size_x*img_size_y) < max_area:
temp.append(img_id)
print(str(len(self.ids)) + '-->' + str(len(temp)))
self.ids = temp
def discard_bad_examples(self, path):
file_list = open(path, "r")
bad_examples = file_list.readlines()
for i in range(len(bad_examples)):
bad_examples[i] = int(bad_examples[i][:-1])
temp = []
for img_id in self.ids:
if not (img_id in bad_examples):
temp.append(img_id)
print(str(len(self.ids)) + '-->' + str(len(temp)))
self.ids = temp
print('Bad examples are left out!')
#-------------------------Example-----------------------------------------
if __name__ == '__main__':
transform = transforms.Compose([transforms.Resize((128,128)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
dataset = CocoData(root = 'C:/Users/motur/coco/images/train2017',
annFile = 'C:/Users/motur/coco/annotations/instances_train2017.json',
category_names = ['giraffe'],
transform=transform)
dataset.discard_small(0.03)
train_loader = DataLoader(dataset, batch_size=1, shuffle=True)
print('Number of samples: ', len(dataset))
for num_iter, sample_batched in enumerate(train_loader,0):
image= sample_batched['image'][0]
imshow(torchvision.utils.make_grid(image))
plt.pause(0.001)
mask= sample_batched['seg_mask'][0]
fg_mask= sample_batched['single_fg_mask'][0]
imshow(torchvision.utils.make_grid(mask[0]))
plt.pause(0.001)