def predict_fba_folder(model, args):
save_dir = args.output_dir
dataset_test = PredDataset(args.image_dir, args.trimap_dir)
gen = iter(dataset_test)
for item_dict in gen:
image_np = item_dict['image']
trimap_np = item_dict['trimap']
fg, bg, alpha = pred(image_np, trimap_np, model)
cv2.imwrite(os.path.join(save_dir, item_dict['name'][:-4] + '_fg.png'), fg[:, :, ::-1] * 255)
cv2.imwrite(os.path.join(save_dir, item_dict['name'][:-4] + '_bg.png'), bg[:, :, ::-1] * 255)
cv2.imwrite(os.path.join(save_dir, item_dict['name'][:-4] + '_alpha.png'), alpha * 255)
def detect(checkpoint, pred_on_path, output_path, threshold=0.5, visualize=False, red_label='sick'):
device = torch.device(type='cuda') if torch.cuda.is_available() else torch.device(type='cpu')
if os.path.exists(output_path):
shutil.rmtree(output_path)
os.makedirs(output_path)
logger.info('inside ' + str(pred_on_path) + ': ' + str(os.listdir(pred_on_path)))
dataset_val = PredDataset(pred_on_path=pred_on_path,
transform=transforms.Compose([Normalizer(), Resizer(min_side=608)])) #TODO make resize an input param
logger.info('dataset prepared')
dataloader_val = DataLoader(dataset_val, num_workers=0, collate_fn=collater, batch_sampler=None)
logger.info('data loader initialized')
labels = checkpoint['labels']
logger.info('labels are: ' + str(labels))
num_classes = len(labels)
configs = deepcopy(checkpoint['training_configs'])
configs.update(checkpoint['hp_values'])
logger.info('initializing object_detection model')
model = retinanet(depth=checkpoint['depth'], num_classes=num_classes, scales=configs['anchor_scales'], ratios=configs['anchor_ratios']) #TODO: make depth an input parameter
logger.info('loading weights')
model.load_state_dict(checkpoint['model'])
model = model.to(device=device)
logger.info('model to device: ' + str(device))
model.eval()
unnormalize = UnNormalizer()
def draw_caption(image, box, caption):
b = np.array(box).astype(int)
cv2.putText(image, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2)
cv2.putText(image, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)
inference_times = []
for idx, data in enumerate(dataloader_val):
scale = data['scale'][0]
with torch.no_grad():
st = time.time()
scores, classification, transformed_anchors = model(data['img'].to(device=device).float())
elapsed_time = time.time() - st
print('Elapsed time: {}'.format(elapsed_time))
inference_times.append(elapsed_time)
idxs = np.where(scores.cpu() > threshold)[0]
if visualize:
img = np.array(255 * unnormalize(data['img'][0, :, :, :])).copy()
img[img < 0] = 0
img[img > 255] = 255
img = np.transpose(img, (1, 2, 0))
img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB)
detections_list = []
for j in range(idxs.shape[0]):
bbox = transformed_anchors[idxs[j], :]
if visualize:
x1 = int(bbox[0])
y1 = int(bbox[1])
x2 = int(bbox[2])
y2 = int(bbox[3])
label_idx = int(classification[idxs[j]])
label_name = labels[label_idx]
score = scores[idxs[j]].item()
if visualize:
draw_caption(img, (x1, y1, x2, y2), label_name)
if red_label in label_name:
cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 0, 255), thickness=2)
else:
cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 255, 0), thickness=2)
print(label_name)
# un resize for eval against gt
bbox /= scale
bbox.round()
x1 = int(bbox[0])
y1 = int(bbox[1])
x2 = int(bbox[2])
y2 = int(bbox[3])
detections_list.append([label_name, str(score), str(x1), str(y1), str(x2), str(y2)])
img_name = dataset_val.image_names[idx].split('/')[-1]
i_name = img_name.split('.')[0]
filename = i_name + '.txt'
filepathname = os.path.join(output_path, filename)
with open(filepathname, 'w', encoding='utf8') as f:
for single_det_list in detections_list:
for i, x in enumerate(single_det_list):
f.write(str(x))
f.write(' ')
f.write('\n')
if visualize:
save_to_path = os.path.join(output_path, img_name)
cv2.imwrite(save_to_path, img)
cv2.waitKey(0)
print('average inference time per image: ', np.mean(inference_times))
return output_path