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| 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: | |
| 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)) | |
| # bbox | |
| annotation[0, 0] = label[0] # x1 | |
| annotation[0, 1] = label[1] # y1 | |
| annotation[0, 2] = label[0] + label[2] # x2 | |
| annotation[0, 3] = label[1] + label[3] # y2 | |
| # landmarks | |
| annotation[0, 4] = label[4] # l0_x | |
| annotation[0, 5] = label[5] # l0_y | |
| annotation[0, 6] = label[7] # l1_x | |
| annotation[0, 7] = label[8] # l1_y | |
| annotation[0, 8] = label[10] # l2_x | |
| annotation[0, 9] = label[11] # l2_y | |
| annotation[0, 10] = label[13] # l3_x | |
| annotation[0, 11] = label[14] # l3_y | |
| annotation[0, 12] = label[16] # l4_x | |
| annotation[0, 13] = label[17] # l4_y | |
| 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) | |