def show_debug_sample(data, label, predictions, data_dim, label_dim, std=1): img = [] lbl = [] for i in range(len(data)): prediction_image = from_arr_to_label(predictions[i], label_dim) label_image = from_arr_to_label(label[i], label_dim) temp = data[i] * std min_pixel = np.amin(temp) temp = temp + min_pixel data_image = from_arr_to_data(temp, data_dim) data_image.paste(prediction_image, (24, 24), prediction_image) data_image = data_image.resize((128, 128)) img.append(data_image) lbldata_image = from_arr_to_data(temp, data_dim) lbldata_image.paste(label_image, (24, 24), label_image) lbldata_image = lbldata_image.resize((128, 128)) lbl.append(lbldata_image) new_im = Image.new('RGB', (256,len(img)*128)) for j in range(len(img)): new_im.paste(img[j], (0, j*128)) new_im.paste(lbl[j], (128, j*128)) new_im.show()
def show_debug_sample(data, label, predictions, data_dim, label_dim, std=1): img = [] lbl = [] for i in range(len(data)): prediction_image = from_arr_to_label(predictions[i], label_dim) label_image = from_arr_to_label(label[i], label_dim) temp = data[i] * std min_pixel = np.amin(temp) temp = temp + min_pixel data_image = from_arr_to_data(temp, data_dim) data_image.paste(prediction_image, (24, 24), prediction_image) data_image = data_image.resize((128, 128)) img.append(data_image) lbldata_image = from_arr_to_data(temp, data_dim) lbldata_image.paste(label_image, (24, 24), label_image) lbldata_image = lbldata_image.resize((128, 128)) lbl.append(lbldata_image) new_im = Image.new('RGB', (256, len(img) * 128)) for j in range(len(img)): new_im.paste(img[j], (0, j * 128)) new_im.paste(lbl[j], (128, j * 128)) new_im.show()
def debug_input_data(data, label, data_dim, label_dim, delay=0): label_image = from_arr_to_label(label, label_dim) data_image= from_arr_to_data(data, data_dim) data_image.paste(label_image, (24, 24), label_image) data_image = data_image.resize((128, 128)) data_image.show() time.sleep(delay)
def debug_input_data(data, label, data_dim, label_dim, delay=0): label_image = from_arr_to_label(label, label_dim) data_image = from_arr_to_data(data, data_dim) data_image.paste(label_image, (24, 24), label_image) data_image = data_image.resize((128, 128)) data_image.show() time.sleep(delay)
def show_individual_predictions(self, dataset, predictions, std=1): print("Show each individual prediction") images = np.array(dataset[0].eval()) for i in range(images.shape[0]): min_val = np.amin(images[i]) img =from_arr_to_data((images[i]*std + min_val), 64) pred = predictions[i] clip_idx = pred < 0.3 pred[clip_idx] = 0 lab = from_arr_to_label(pred, 16) img.paste(lab, (24, 24), lab) img = img.resize((256, 256)) img.show() user = raw_input('Proceed?') if user == 'no': break