def predict(frozen_model, training_cnf, predict_dir, image_size, output_path,
num_classes, gpu_memory_fraction):
cnf = util.load_module(training_cnf).cnf
standardizer = cnf['standardizer']
graph = util.load_frozen_graph(frozen_model)
preprocessor = convert_preprocessor(image_size)
predictor = SegmentPredictor(graph, standardizer, preprocessor)
# images = data.get_image_files(predict_dir)
image_names = [
filename.strip() for filename in os.listdir(predict_dir)
if filename.endswith('.jpg')
]
hist = np.zeros((num_classes, num_classes))
for image_filename in image_names:
final_prediction_map = predictor.predict(
os.path.join(predict_dir, image_filename))
final_prediction_map = final_prediction_map.transpose(0, 2,
1).squeeze()
gt_name = os.path.join(predict_dir,
image_filename[:-4] + '_final_mask' + '.png')
gt = convert(gt_name, image_size)
gt = np.asarray(gt)
gt = convert_labels(gt, image_size, image_size)
hist += compute_hist(gt, final_prediction_map, num_classes=num_classes)
iou = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
meaniou = np.nanmean(iou)
print('Mean IOU %5.5f' % meaniou)
def predict(frozen_model, training_cnf, image_path, image_size, output_path, gpu_memory_fraction):
cnf = util.load_module(training_cnf).cnf
standardizer = cnf['standardizer']
graph = util.load_frozen_graph(frozen_model)
preprocessor = convert_preprocessor(448)
predictor = SegmentPredictor(graph, standardizer, preprocessor)
final_prediction_map = predictor.predict(image_path)
final_prediction_map = final_prediction_map.transpose(0, 2, 1).squeeze()
image = data.load_image(image_path, preprocessor=preprocessor)
img = image.transpose(2, 1, 0)
img = Image.fromarray(img.astype('uint8'), 'RGB')
img.save('/tmp/test.png')
image_filename = image_path.split('/')[-1]
plot_masks('/tmp/test.png', final_prediction_map, output_path)
"""
def predict(frozen_model, training_cnf, predict_dir, image_size, output_path,
num_classes, gpu_memory_fraction):
cnf = util.load_module(training_cnf).cnf
standardizer = cnf['standardizer']
graph = util.load_frozen_graph(frozen_model)
preprocessor = convert_preprocessor(image_size)
predictor = SegmentPredictor(graph, standardizer, preprocessor)
# images = data.get_image_files(predict_dir)
image_names = [
filename.strip() for filename in os.listdir(predict_dir)
if filename.endswith('.jpg')
]
iou = IOU()
per_class_iou = iou.per_class_iou(predictor, predict_dir, image_size)
meaniou = iou.meaniou(predictor, predict_dir, image_size)
print(per_class_iou)
print('Mean IOU %5.5f' % meaniou)
