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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)
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