| import os
|
| import os.path
|
| import sys
|
| import torch
|
| import torch.utils.data as data
|
| import cv2
|
| import numpy as np
|
|
|
| class WiderFaceDetection(data.Dataset):
|
| def __init__(self, txt_path, preproc=None):
|
| self.preproc = preproc
|
| self.imgs_path = []
|
| self.words = []
|
| f = open(txt_path,'r')
|
| lines = f.readlines()
|
| isFirst = True
|
| labels = []
|
| for line in lines:
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| line = line.rstrip()
|
| if line.startswith('#'):
|
| if isFirst is True:
|
| isFirst = False
|
| else:
|
| labels_copy = labels.copy()
|
| self.words.append(labels_copy)
|
| labels.clear()
|
| path = line[2:]
|
| path = txt_path.replace('label.txt','images/') + path
|
| self.imgs_path.append(path)
|
| else:
|
| line = line.split(' ')
|
| label = [float(x) for x in line]
|
| labels.append(label)
|
|
|
| self.words.append(labels)
|
|
|
| def __len__(self):
|
| return len(self.imgs_path)
|
|
|
| def __getitem__(self, index):
|
| img = cv2.imread(self.imgs_path[index])
|
| height, width, _ = img.shape
|
|
|
| labels = self.words[index]
|
| annotations = np.zeros((0, 15))
|
| if len(labels) == 0:
|
| return annotations
|
| for idx, label in enumerate(labels):
|
| annotation = np.zeros((1, 15))
|
|
|
| annotation[0, 0] = label[0]
|
| annotation[0, 1] = label[1]
|
| annotation[0, 2] = label[0] + label[2]
|
| annotation[0, 3] = label[1] + label[3]
|
|
|
|
|
| annotation[0, 4] = label[4]
|
| annotation[0, 5] = label[5]
|
| annotation[0, 6] = label[7]
|
| annotation[0, 7] = label[8]
|
| annotation[0, 8] = label[10]
|
| annotation[0, 9] = label[11]
|
| annotation[0, 10] = label[13]
|
| annotation[0, 11] = label[14]
|
| annotation[0, 12] = label[16]
|
| annotation[0, 13] = label[17]
|
| if (annotation[0, 4]<0):
|
| annotation[0, 14] = -1
|
| else:
|
| annotation[0, 14] = 1
|
|
|
| annotations = np.append(annotations, annotation, axis=0)
|
| target = np.array(annotations)
|
| if self.preproc is not None:
|
| img, target = self.preproc(img, target)
|
|
|
| return torch.from_numpy(img), target
|
|
|
| def detection_collate(batch):
|
| """Custom collate fn for dealing with batches of images that have a different
|
| number of associated object annotations (bounding boxes).
|
|
|
| Arguments:
|
| batch: (tuple) A tuple of tensor images and lists of annotations
|
|
|
| Return:
|
| A tuple containing:
|
| 1) (tensor) batch of images stacked on their 0 dim
|
| 2) (list of tensors) annotations for a given image are stacked on 0 dim
|
| """
|
| targets = []
|
| imgs = []
|
| for _, sample in enumerate(batch):
|
| for _, tup in enumerate(sample):
|
| if torch.is_tensor(tup):
|
| imgs.append(tup)
|
| elif isinstance(tup, type(np.empty(0))):
|
| annos = torch.from_numpy(tup).float()
|
| targets.append(annos)
|
|
|
| return (torch.stack(imgs, 0), targets)
|
|
|
