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carpreprocess.py
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carpreprocess.py
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# -*- coding: utf-8 -*-
import tarfile
import scipy.io
import numpy as np
import os
import cv2 as cv
import shutil
import random
from console_progressbar import ProgressBar
def ensure_folder(folder):
if not os.path.exists(folder):
os.makedirs(folder)
def save_train_data(fnames, labels, bboxes):
src_folder = 'cars_train'
num_samples = len(fnames)
train_split = 0.8
num_train = int(round(num_samples * train_split))
train_indexes = random.sample(range(num_samples), num_train)
pb = ProgressBar(total=100, prefix='Save train data', suffix='', decimals=3, length=50, fill='=')
for i in range(num_samples):
fname = fnames[i]
(x1, y1, x2, y2) = bboxes[i]
src_path = os.path.join(src_folder, fname)
src_image = cv.imread(src_path)
height, width = src_image.shape[:2]
# margins of 16 pixels
margin = 16
x1 = max(0, x1 - margin)
y1 = max(0, y1 - margin)
x2 = min(x2 + margin, width)
y2 = min(y2 + margin, height)
# print("{} -> {}".format(fname, label))
pb.print_progress_bar((i + 1) * 100 / num_samples)
dst_folder = 'data/train'
dst_path = os.path.join(dst_folder, fname)
crop_image = src_image[y1:y2, x1:x2]
dst_img = cv.resize(src=crop_image, dsize=(img_height, img_width))
cv.imwrite(dst_path, dst_img)
def save_test_data(fnames, bboxes):
src_folder = 'cars_test'
dst_folder = 'data/test'
num_samples = len(fnames)
pb = ProgressBar(total=100, prefix='Save test data', suffix='', decimals=3, length=50, fill='=')
for i in range(num_samples):
fname = fnames[i]
(x1, y1, x2, y2) = bboxes[i]
src_path = os.path.join(src_folder, fname)
src_image = cv.imread(src_path)
height, width = src_image.shape[:2]
# margins of 16 pixels
margin = 16
x1 = max(0, x1 - margin)
y1 = max(0, y1 - margin)
x2 = min(x2 + margin, width)
y2 = min(y2 + margin, height)
# print(fname)
pb.print_progress_bar((i + 1) * 100 / num_samples)
dst_path = os.path.join(dst_folder, fname)
crop_image = src_image[y1:y2, x1:x2]
dst_img = cv.resize(src=crop_image, dsize=(img_height, img_width))
cv.imwrite(dst_path, dst_img)
def process_train_data():
print("Processing train data...")
cars_annos = scipy.io.loadmat('devkit/cars_train_annos')
annotations = cars_annos['annotations']
annotations = np.transpose(annotations)
fnames = []
class_ids = []
bboxes = []
labels = []
for annotation in annotations:
bbox_x1 = annotation[0][0][0][0]
bbox_y1 = annotation[0][1][0][0]
bbox_x2 = annotation[0][2][0][0]
bbox_y2 = annotation[0][3][0][0]
class_id = annotation[0][4][0][0]
labels.append('%04d' % (class_id,))
fname = annotation[0][5][0]
bboxes.append((bbox_x1, bbox_y1, bbox_x2, bbox_y2))
class_ids.append(class_id)
fnames.append(fname)
labels_count = np.unique(class_ids).shape[0]
print(np.unique(class_ids))
print('The number of different cars is %d' % labels_count)
save_train_data(fnames, labels, bboxes)
def process_test_data():
print("Processing test data...")
cars_annos = scipy.io.loadmat('devkit/cars_test_annos')
annotations = cars_annos['annotations']
annotations = np.transpose(annotations)
fnames = []
bboxes = []
for annotation in annotations:
bbox_x1 = annotation[0][0][0][0]
bbox_y1 = annotation[0][1][0][0]
bbox_x2 = annotation[0][2][0][0]
bbox_y2 = annotation[0][3][0][0]
fname = annotation[0][4][0]
bboxes.append((bbox_x1, bbox_y1, bbox_x2, bbox_y2))
fnames.append(fname)
save_test_data(fnames, bboxes)
def rename2(collection):
os.getcwd()
for i,filename in enumerate(os.listdir(collection)):
os.rename("data/test/" + filename, "data/train/" + str(i)+"t"+ ".jpg")
def rename1(collection):
os.getcwd()
for i,filename in enumerate(os.listdir(collection)):
os.rename("data/train/" + filename, "data/train/" + str(i)+ ".jpg")
def renameNew(collection):
os.getcwd()
for i,filename in enumerate(os.listdir(collection)):
os.rename("data/train/" + filename, "data/train/" + str(i)+ "train"+".jpg")
if __name__ == '__main__':
# parameters
img_width, img_height = 224, 224
print('Extracting cars_train.tgz...')
if not os.path.exists('cars_train'):
with tarfile.open('cars_train.tgz', "r:gz") as tar:
tar.extractall()
print('Extracting cars_test.tgz...')
if not os.path.exists('cars_test'):
with tarfile.open('cars_test.tgz', "r:gz") as tar:
tar.extractall()
print('Extracting car_devkit.tgz...')
if not os.path.exists('devkit'):
with tarfile.open('car_devkit.tgz', "r:gz") as tar:
tar.extractall()
cars_meta = scipy.io.loadmat('devkit/cars_meta')
class_names = cars_meta['class_names'] # shape=(1, 196)
class_names = np.transpose(class_names)
print('class_names.shape: ' + str(class_names.shape))
print('Sample class_name: [{}]'.format(class_names[8][0][0]))
ensure_folder('data/train')
ensure_folder('data/test')
process_train_data()
process_test_data()
rename1("data/train")
rename2("data/test")
renameNew("data/train")
# clean up
shutil.rmtree('cars_train')
shutil.rmtree('cars_test')
# shutil.rmtree('devkit')