config['image_size'],
BATCH_SIZE,
args.num_examples,
mode='test')
try:
ssd = create_ssd(NUM_CLASSES, args.arch, args.pretrained_type,
args.checkpoint_dir, args.checkpoint_path)
except Exception as e:
print(e)
print('The program is exiting...')
sys.exit()
os.makedirs('outputs/images', exist_ok=True)
os.makedirs('outputs/detects', exist_ok=True)
visualizer = ImageVisualizer(info['idx_to_name'],
save_dir='outputs/images')
for i, (filename, imgs, gt_confs, gt_locs) in enumerate(
tqdm(batch_generator,
total=info['length'],
desc='Testing...',
unit='images')):
boxes, classes, scores = predict(imgs, default_boxes)
filename = filename.numpy()[0].decode()
original_image = Image.open(
os.path.join(info['image_dir'], '{}.jpg'.format(filename)))
boxes *= original_image.size * 2
visualizer.save_image(original_image, boxes, classes,
'{}.jpg'.format(filename))
log_file = os.path.join('outputs/detects', '{}.txt')
new_size=config['image_size'],
batch_size=BATCH_SIZE,
num_batches=args.num_examples,
mode='test')
try:
ssd = create_ssd(NUM_CLASSES, args.arch, args.pretrained_type,
args.checkpoint_dir, args.checkpoint_path)
except Exception as e:
print(e)
print('The program is exiting...')
sys.exit()
os.makedirs('./find_area_results/PNGImages', exist_ok=True)
os.makedirs('./find_area_results/detects', exist_ok=True)
visualizer = ImageVisualizer(info['idx_to_name'],
save_dir='./find_area_results/PNGImages')
for i, (filename, imgs) in enumerate(
tqdm(batch_generator,
total=info['length'],
desc='Testing...',
unit='images')):
boxes, classes, scores = predict(imgs, default_boxes)
filename = filename.numpy()[0].decode()
original_image = Image.open(
os.path.join(info['image_dir'], '{}.jpg'.format(filename)))
boxes *= original_image.size * 2
visualizer.save_image(original_image, boxes, classes,
'{}.jpg'.format(filename))
return dataloader, info
if __name__ == '__main__':
default_boxes = torch.rand(8732, 4)
dataloader, info = create_dataloader('./data/VOCdevkit', 1, default_boxes)
data = next(iter(dataloader))
idx_to_name = info['idx_to_name']
# Confirm data shapes
print([x.shape for x in data])
img, boxes, labels = data
# Eliminate the batch dimension
img = img.squeeze(0)
boxes = boxes.squeeze(0)
labels = labels.squeeze(0)
# Reverse image processing step
img = img.permute(1, 2, 0).contiguous().numpy()
img += [123, 117, 104]
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# Scale boxes back to image size
boxes = boxes.numpy() * 300
labels = labels.numpy()
visualizer = ImageVisualizer(idx_to_name)
visualizer.save_image(img, boxes, labels, 'test.jpg')
default_boxes = generate_default_boxes(config)
batch_generator, info = create_batch_generator(
args.data_dir, args.data_year, default_boxes, config['image_size'],
BATCH_SIZE, args.num_examples)
try:
ssd = create_ssd(NUM_CLASSES, args.arch, args.pretrained_type,
args.checkpoint_dir, args.checkpoint_path)
except Exception as e:
print(e)
print('The program is exiting...')
sys.exit()
visualizer = ImageVisualizer(info['idx_to_name'], save_dir='images')
for i, (imgs, gt_confs, gt_locs) in enumerate(
tqdm(batch_generator,
total=info['length'],
desc='Testing...',
unit='images')):
img = imgs[0].numpy() + 1.0
img *= 127
img = img.astype(np.uint8)
boxes, classes, scores = predict(imgs, default_boxes)
boxes *= img.shape[0]
visualizer.save_image(img, boxes, classes, '{}.jpg'.format(i))
return img, all_boxes, all_scores, all_names
if __name__ == '__main__':
with open('config.yaml', 'r') as f:
cfg = yaml.load(f)
if args.arch == 'ssd300':
config = cfg['SSD300']
else:
config = cfg['SSD512']
default_boxes = generate_default_boxes(config)
dataloader, info = create_dataloader(args.data_dir, args.batch_size,
config['image_size'], default_boxes,
False, args.num_examples)
ssd = create_ssd(NUM_CLASSES, args.arch, 'ssd', args.pretrained_path)
ssd.to(device)
ssd.eval()
visualizer = ImageVisualizer(info['idx_to_name'],
save_dir=args.save_image_dir)
for i, data in enumerate(dataloader):
img, boxes, scores, names = test(ssd, data, default_boxes)
img = reconstruct_image(img)
img_name = 'image_{}.jpg'.format(i)
visualizer.save_image(img, boxes, names, img_name)