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aug_contrast.py
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aug_contrast.py
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# import libraries
from PIL import Image, ImageDraw
import PIL
import torch
import os
import xml.etree.ElementTree as ET
import torchvision.transforms.functional as F
import numpy as np
import random
import cv2
import matplotlib.pyplot as plt
# classes of the dataset
voc_labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
# map classes to its corresponding number
label_map = {k: v+1 for v, k in enumerate(voc_labels)}
#Inverse mapping
rev_label_map = {v: k for k, v in label_map.items()}
#Colormap for bounding box
# number of classes (34)
CLASSES = 34
distinct_colors = ["#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)])
for i in range(CLASSES)]
label_color_map = {k: distinct_colors[i] for i, k in enumerate(label_map.keys())}
def parse_annot(annotation_path):
tree = ET.parse(annotation_path)
root = tree.getroot()
boxes = list()
labels = list()
difficulties = list()
for object in root.iter("object"):
difficult = int(object.find("difficult").text == "1")
label = object.find("name").text.upper().strip()
if label not in label_map:
print("{0} not in label map.".format(label))
assert label in label_map
bbox = object.find("bndbox")
xmin = int(bbox.find("xmin").text)
ymin = int(bbox.find("ymin").text)
xmax = int(bbox.find("xmax").text)
ymax = int(bbox.find("ymax").text)
boxes.append([xmin, ymin, xmax, ymax])
labels.append(label_map[label])
difficulties.append(difficult)
return {"boxes": boxes, "labels": labels, "difficulties": difficulties}
def draw_PIL_image(image, boxes, labels):
'''
Draw PIL image
image: A PIL image
labels: A tensor of dimensions (#objects,)
boxes: A tensor of dimensions (#objects, 4)
'''
if type(image) != PIL.Image.Image:
image = F.to_pil_image(image)
new_image = image.copy()
labels = labels.tolist()
print("Labels: ",labels)
draw = ImageDraw.Draw(new_image)
boxes = boxes.tolist()
print("boxes: ", boxes)
for i in range(len(boxes)):
draw.rectangle(xy= boxes[i], outline= label_color_map[rev_label_map[labels[i]]])
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = (box[0] + box[1]) / 2.0
y = (box[2] + box[3]) / 2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x * dw
w = w * dw
y = y * dh
h = h * dh
return (x, y, w, h)
def convert_annotation(image_id):
if not os.path.exists('./carin_LP_labels/%s.xml' % (image_id)):
return
in_file = open('./carin_LP_labels/%s.xml' % (image_id))
out_file = open('./aug_carin_LP_labels/%s_contrast.txt' % (image_id), 'w')
tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls not in voc_labels:
continue
cls_id = voc_labels.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text))
print(b)
bb = convert((w, h), b)
# print(bb)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
def Adjust_contrast(image):
# adjust contrast of the image by 2 and 5
return F.adjust_contrast(image,2)
for image in os.listdir('./carin_LP/'):
# It needs to be modified according to your picture . For example, the name of the picture is 123.456.jpg, There will be mistakes here . In general , If the picture format is fixed , If it's all jpg, It would be image_id=image[:-4] Just deal with it . All in all , There's a lot going on , Take matters into one's own hands , ha-ha !
image_id = image.split('.jpg')[0]
image = Image.open("./carin_LP/" + image_id +".jpg", mode="r")
image = image.convert("RGB")
objects = parse_annot("./carin_LP_labels/" + image_id + ".xml")
boxes = torch.FloatTensor(objects['boxes'])
labels = torch.LongTensor(objects['labels'])
new_image = Adjust_contrast(image)
# print(new_image)
new_image.save("./aug_carin_LP/" +image_id + "_contrast.png")
draw_PIL_image(new_image, boxes, labels)
convert_annotation(image_id)