def predict(input_dir, gpu_num=None): (file_names, inputs) = load_images(input_dir, INPUT_IMAGE_SHAPE, with_normalize=WITH_NORM) network = M2Det(INPUT_IMAGE_SHAPE, BATCH_SIZE, class_num=CLASS_NUM) priors = network.get_prior_boxes() bbox_util = BBoxUtility(CLASS_NUM, priors) if isinstance(gpu_num, int): model = network.get_parallel_model(gpu_num) else: model = network.get_model() # model.summary() print('loading weghts ...') model.load_weights(os.path.join(DIR_MODEL, FILE_MODEL)) print('... loaded') #""" print('predicting ...') preds = model.predict(inputs, BATCH_SIZE) print('... predicted') print('result saveing ...') pred_pbox = preds[0, :, -8:] results = bbox_util.detection_out(preds) image_data = __outputs_to_image_data(inputs, results, file_names) save_images(DIR_OUTPUTS, image_data, file_names, with_unnormalize=WITH_NORM) print('... finish .')
def predict(input_dir, gpu_num=None): h, w, c = INPUT_IMAGE_SHAPE org_h, org_w = h - (PADDING * 2), w - (PADDING * 2) (file_names, inputs) = load_images(input_dir, (org_h, org_w, c)) inputs = np.pad(inputs, [(0, 0), (PADDING, PADDING), (PADDING, PADDING), (0, 0)], 'constant', constant_values=0) network = UNet(INPUT_IMAGE_SHAPE, CLASS_NUM) model = network.model() plot_model(model, to_file='../model_plot.png') model.summary() if isinstance(gpu_num, int): model = multi_gpu_model(model, gpus=gpu_num) model.load_weights(os.path.join(DIR_MODEL, File_MODEL)) print('predicting ...') preds = model.predict(inputs, BATCH_SIZE) print('... predicted') print('output saveing ...') preds = preds[:, PADDING:org_h + PADDING, PADDING:org_w + PADDING, :] save_images(DIR_OUTPUTS, preds, file_names) print('... saved')
def predict(input_dir): (file_names, inputs) = load_images(input_dir, INPUT_IMAGE_SIZE) network = UNet(INPUT_IMAGE_SIZE) model = network.model() model.load_weights(os.path.join(DIR_MODEL, File_MODEL)) preds = model.predict(inputs, BATCH_SIZE) save_images(DIR_OUTPUTS, preds, file_names)
def predict(input_dir, gpu_num=None): (file_names, inputs) = load_images(input_dir, INPUT_IMAGE_SHAPE) network = UNetPP(INPUT_IMAGE_SHAPE, start_filter=START_FILTER, depth=DEPTH, class_num=CLASS_NUM) if isinstance(gpu_num, int): model = network.get_parallel_model(gpu_num) else: model = network.get_model() print('loading weghts ...') model.load_weights(os.path.join(DIR_MODEL, File_MODEL)) print('... loaded') print('predicting ...') preds = model.predict(inputs, BATCH_SIZE) print('... predicted') print('result saveing ...') save_images(DIR_OUTPUTS, preds, file_names) print('... finish .')
def predict(input_dir, gpu_num=None): h, w, c = INPUT_IMAGE_SHAPE org_h, org_w = h - (PADDING * 2), w - (PADDING * 2) (file_names, inputs) = load_images(input_dir, (org_h, org_w, c)) inputs = np.pad(inputs, [(0, 0), (PADDING, PADDING), (PADDING, PADDING), (0, 0)], 'constant', constant_values=0) network = DeepUNet(INPUT_IMAGE_SHAPE, internal_filter=INTERNAL_FILTER, depth=DEPTH, class_num=CLASS_NUM) if isinstance(gpu_num, int): model = network.get_parallel_model(gpu_num) else: model = network.get_model() # model.summary() print('loading weghts ...') model.load_weights(os.path.join(DIR_MODEL, File_MODEL)) print('... loaded') print('predicting ...') preds = model.predict(inputs, BATCH_SIZE) print('... predicted') print('result saveing ...') preds = preds[:, PADDING:org_h+PADDING, PADDING:org_w+PADDING, :] save_images(DIR_OUTPUTS, preds, file_names) print('... finish .')