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import PIL #version 1.2.0
import torch
import os
import torchvision.transforms.functional as F
import numpy as np
import random
def intersect(boxes1, boxes2):
'''
Find intersection of every box combination between two sets of box
boxes1: bounding boxes 1, a tensor of dimensions (n1, 4)
boxes2: bounding boxes 2, a tensor of dimensions (n2, 4)
Out: Intersection each of boxes1 with respect to each of boxes2,
a tensor of dimensions (n1, n2)
'''
n1 = boxes1.size(0)
n2 = boxes2.size(0)
max_xy = torch.min(boxes1[:, 2:].unsqueeze(1).expand(n1, n2, 2),
boxes2[:, 2:].unsqueeze(0).expand(n1, n2, 2))
min_xy = torch.max(boxes1[:, :2].unsqueeze(1).expand(n1, n2, 2),
boxes2[:, :2].unsqueeze(0).expand(n1, n2, 2))
inter = torch.clamp(max_xy - min_xy , min=0) # (n1, n2, 2)
return inter[:, :, 0] * inter[:, :, 1] #(n1, n2)
def find_IoU(boxes1, boxes2):
'''
Find IoU between every boxes set of boxes
boxes1: a tensor of dimensions (n1, 4) (left, top, right , bottom)
boxes2: a tensor of dimensions (n2, 4)
Out: IoU each of boxes1 with respect to each of boxes2, a tensor of
dimensions (n1, n2)
Formula:
(box1 ∩ box2) / (box1 u box2) = (box1 ∩ box2) / (area(box1) + area(box2) - (box1 ∩ box2 ))
'''
inter = intersect(boxes1, boxes2)
area_boxes1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1])
area_boxes2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1])
area_boxes1 = area_boxes1.unsqueeze(1).expand_as(inter) #(n1, n2)
area_boxes2 = area_boxes2.unsqueeze(0).expand_as(inter) #(n1, n2)
union = (area_boxes1 + area_boxes2 - inter)
return inter / union
def random_crop(image, boxes, labels, difficulties=None):
'''
image: A PIL image
boxes: Bounding boxes, a tensor of dimensions (#objects, 4)
labels: labels of object, a tensor of dimensions (#objects)
difficulties: difficulties of detect object, a tensor of dimensions (#objects)
Out: cropped image , new boxes, new labels, new difficulties
'''
if type(image) == PIL.Image.Image:
image = F.to_tensor(image)
original_h = image.size(1)
original_w = image.size(2)
while True:
mode = random.choice([0.1, 0.3, 0.5, 0.9, None])
if mode is None:
return F.to_pil_image(image), boxes, labels, difficulties
new_image = image
new_boxes = boxes
new_difficulties = difficulties
new_labels = labels
for _ in range(50):
# Crop dimensions: [0.3, 1] of original dimensions
new_h = random.uniform(0.3*original_h, original_h)
new_w = random.uniform(0.3*original_w, original_w)
# Aspect ratio constraint b/t .5 & 2
if new_h/new_w < 0.5 or new_h/new_w > 2:
continue
#Crop coordinate
left = random.uniform(0, original_w - new_w)
right = left + new_w
top = random.uniform(0, original_h - new_h)
bottom = top + new_h
crop = torch.FloatTensor([int(left), int(top), int(right), int(bottom)])
# Calculate IoU between the crop and the bounding boxes
overlap = find_IoU(crop.unsqueeze(0), boxes) #(1, #objects)
overlap = overlap.squeeze(0)
# If not a single bounding box has a IoU of greater than the minimum, try again
if overlap.shape[0] == 0:
continue
if overlap.max().item() < mode:
continue
#Crop
new_image = image[:, int(top):int(bottom), int(left):int(right)] #(3, new_h, new_w)
#Center of bounding boxes
center_bb = (boxes[:, :2] + boxes[:, 2:])/2.0
#Find bounding box has been had center in crop
center_in_crop = (center_bb[:, 0] >left) * (center_bb[:, 0] < right
) *(center_bb[:, 1] > top) * (center_bb[:, 1] < bottom) #( #objects)
if not center_in_crop.any():
continue
#take matching bounding box
new_boxes = boxes[center_in_crop, :]
#take matching labels
new_labels = labels[center_in_crop]
#take matching difficulities
if difficulties is not None:
new_difficulties = difficulties[center_in_crop]
else:
new_difficulties = None
#Use the box left and top corner or the crop's
new_boxes[:, :2] = torch.max(new_boxes[:, :2], crop[:2])
#adjust to crop
new_boxes[:, :2] -= crop[:2]
new_boxes[:, 2:] = torch.min(new_boxes[:, 2:],crop[2:])
#adjust to crop
new_boxes[:, 2:] -= crop[:2]
return F.to_pil_image(new_image), new_boxes, new_labels, new_difficulties |