import numpy as np import os import tensorflow as tf from six.moves import range from PIL import Image import argparse import hyper_parameters as hparams from model_common_test import DiffPastingV3, VirtualSketchingModel from utils import reset_graph, load_checkpoint, update_hyperparams, draw, \ save_seq_data, image_pasting_v3_testing, draw_strokes from dataset_utils import load_dataset_testing os.environ['CUDA_VISIBLE_DEVICES'] = '0' def move_cursor_to_undrawn(current_pos_list, input_image_, patch_size, move_min_dist, move_max_dist, trial_times=20): """ :param current_pos_list: (select_times, 1, 2), [0.0, 1.0) :param input_image_: (1, image_size, image_size, 3), [0-stroke, 1-BG] :return: new_cursor_pos: (select_times, 1, 2), [0.0, 1.0) """ def crop_patch(image, center, image_size, crop_size): x0 = center[0] - crop_size // 2 x1 = x0 + crop_size y0 = center[1] - crop_size // 2 y1 = y0 + crop_size x0 = max(0, min(x0, image_size)) y0 = max(0, min(y0, image_size)) x1 = max(0, min(x1, image_size)) y1 = max(0, min(y1, image_size)) patch = image[y0:y1, x0:x1] return patch def isvalid_cursor(input_img, cursor, raster_size, image_size): # input_img: (image_size, image_size, 3), [0.0-BG, 1.0-stroke] cursor_large = cursor * float(image_size) cursor_large = np.round(cursor_large).astype(np.int32) input_crop_patch = crop_patch(input_img, cursor_large, image_size, raster_size) if np.sum(input_crop_patch) > 0.0: return True else: return False def randomly_move_cursor(cursor_position, img_size, min_dist_p, max_dist_p): # cursor_position: (2), [0.0, 1.0) cursor_pos_large = cursor_position * img_size min_dist = int(min_dist_p / 2.0 * img_size) max_dist = int(max_dist_p / 2.0 * img_size) rand_cursor_offset = np.random.randint(min_dist, max_dist, size=cursor_pos_large.shape) rand_cursor_offset_sign = np.random.randint(0, 1 + 1, size=cursor_pos_large.shape) rand_cursor_offset_sign[rand_cursor_offset_sign == 0] = -1 rand_cursor_offset = rand_cursor_offset * rand_cursor_offset_sign new_cursor_pos_large = cursor_pos_large + rand_cursor_offset new_cursor_pos_large = np.minimum(np.maximum(new_cursor_pos_large, 0), img_size - 1) # (2), large-level new_cursor_pos = new_cursor_pos_large.astype(np.float32) / float(img_size) return new_cursor_pos input_image = 1.0 - input_image_[0] # (image_size, image_size, 3), [0-BG, 1-stroke] img_size = input_image.shape[0] new_cursor_pos = [] for cursor_i in range(current_pos_list.shape[0]): curr_cursor = current_pos_list[cursor_i][0] for trial_i in range(trial_times): new_cursor = randomly_move_cursor(curr_cursor, img_size, move_min_dist, move_max_dist) # (2), [0.0, 1.0) if isvalid_cursor(input_image, new_cursor, patch_size, img_size) or trial_i == trial_times - 1: new_cursor_pos.append(new_cursor) break assert len(new_cursor_pos) == current_pos_list.shape[0] new_cursor_pos = np.expand_dims(np.stack(new_cursor_pos, axis=0), axis=1) # (select_times, 1, 2), [0.0, 1.0) return new_cursor_pos def sample(sess, model, input_photos, init_cursor, image_size, init_len, seq_lens, state_dependent, pasting_func, round_stop_state_num, min_dist_p, max_dist_p): """Samples a sequence from a pre-trained model.""" select_times = 1 curr_canvas = np.zeros(dtype=np.float32, shape=(select_times, image_size, image_size)) # [0.0-BG, 1.0-stroke] initial_state = sess.run(model.initial_state) params_list = [[] for _ in range(select_times)] state_raw_list = [[] for _ in range(select_times)] state_soft_list = [[] for _ in range(select_times)] window_size_list = [[] for _ in range(select_times)] round_cursor_list = [] round_length_real_list = [] input_photos_tiles = np.tile(input_photos, (select_times, 1, 1, 1)) for cursor_i, seq_len in enumerate(seq_lens): if cursor_i == 0: cursor_pos = np.squeeze(init_cursor, axis=0) # (select_times, 1, 2) else: cursor_pos = move_cursor_to_undrawn(cursor_pos, input_photos, model.hps.raster_size, min_dist_p, max_dist_p) # (select_times, 1, 2) round_cursor_list.append(cursor_pos) prev_state = initial_state prev_width = np.stack([model.hps.min_width for _ in range(select_times)], axis=0) prev_scaling = np.ones((select_times), dtype=np.float32) # (N) prev_window_size = np.ones((select_times), dtype=np.float32) * model.hps.raster_size # (N) continuous_one_state_num = 0 for i in range(seq_len): if not state_dependent and i % init_len == 0: prev_state = initial_state curr_window_size = prev_scaling * prev_window_size # (N) curr_window_size = np.maximum(curr_window_size, model.hps.min_window_size) curr_window_size = np.minimum(curr_window_size, image_size) feed = { model.initial_state: prev_state, model.input_photo: input_photos_tiles, model.curr_canvas_hard: curr_canvas.copy(), model.cursor_position: cursor_pos, model.image_size: image_size, model.init_width: prev_width, model.init_scaling: prev_scaling, model.init_window_size: prev_window_size, } o_other_params_list, o_pen_list, o_pred_params_list, next_state_list = \ sess.run([model.other_params, model.pen_ras, model.pred_params, model.final_state], feed_dict=feed) # o_other_params: (N, 6), o_pen: (N, 2), pred_params: (N, 1, 7), next_state: (N, 1024) # o_other_params: [tanh*2, sigmoid*2, tanh*2, sigmoid*2] idx_eos_list = np.argmax(o_pen_list, axis=1) # (N) output_i = 0 idx_eos = idx_eos_list[output_i] eos = [0, 0] eos[idx_eos] = 1 other_params = o_other_params_list[output_i].tolist() # (6) params_list[output_i].append([eos[1]] + other_params) state_raw_list[output_i].append(o_pen_list[output_i][1]) state_soft_list[output_i].append(o_pred_params_list[output_i, 0, 0]) window_size_list[output_i].append(curr_window_size[output_i]) # draw the stroke and add to the canvas x1y1, x2y2, width2 = o_other_params_list[output_i, 0:2], o_other_params_list[output_i, 2:4], \ o_other_params_list[output_i, 4] x0y0 = np.zeros_like(x2y2) # (2), [-1.0, 1.0] x0y0 = np.divide(np.add(x0y0, 1.0), 2.0) # (2), [0.0, 1.0] x2y2 = np.divide(np.add(x2y2, 1.0), 2.0) # (2), [0.0, 1.0] widths = np.stack([prev_width[output_i], width2], axis=0) # (2) o_other_params_proc = np.concatenate([x0y0, x1y1, x2y2, widths], axis=-1).tolist() # (8) if idx_eos == 0: f = o_other_params_proc + [1.0, 1.0] pred_stroke_img = draw(f) # (raster_size, raster_size), [0.0-stroke, 1.0-BG] pred_stroke_img_large = image_pasting_v3_testing(1.0 - pred_stroke_img, cursor_pos[output_i, 0], image_size, curr_window_size[output_i], pasting_func, sess) # [0.0-BG, 1.0-stroke] curr_canvas[output_i] += pred_stroke_img_large # [0.0-BG, 1.0-stroke] continuous_one_state_num = 0 else: continuous_one_state_num += 1 curr_canvas = np.clip(curr_canvas, 0.0, 1.0) next_width = o_other_params_list[:, 4] # (N) next_scaling = o_other_params_list[:, 5] next_window_size = next_scaling * curr_window_size # (N) next_window_size = np.maximum(next_window_size, model.hps.min_window_size) next_window_size = np.minimum(next_window_size, image_size) prev_state = next_state_list prev_width = next_width * curr_window_size / next_window_size # (N,) prev_scaling = next_scaling # (N) prev_window_size = curr_window_size # update cursor_pos based on hps.cursor_type new_cursor_offsets = o_other_params_list[:, 2:4] * ( np.expand_dims(curr_window_size, axis=-1) / 2.0) # (N, 2), patch-level new_cursor_offset_next = new_cursor_offsets # important!!! new_cursor_offset_next = np.concatenate([new_cursor_offset_next[:, 1:2], new_cursor_offset_next[:, 0:1]], axis=-1) cursor_pos_large = cursor_pos * float(image_size) stroke_position_next = cursor_pos_large[:, 0, :] + new_cursor_offset_next # (N, 2), large-level if model.hps.cursor_type == 'next': cursor_pos_large = stroke_position_next # (N, 2), large-level else: raise Exception('Unknown cursor_type') cursor_pos_large = np.minimum(np.maximum(cursor_pos_large, 0.0), float(image_size - 1)) # (N, 2), large-level cursor_pos_large = np.expand_dims(cursor_pos_large, axis=1) # (N, 1, 2) cursor_pos = cursor_pos_large / float(image_size) if continuous_one_state_num >= round_stop_state_num or i == seq_len - 1: round_length_real_list.append(i + 1) break return params_list, state_raw_list, state_soft_list, curr_canvas, window_size_list, \ round_cursor_list, round_length_real_list def main_testing(test_image_base_dir, test_dataset, test_image_name, sampling_base_dir, model_base_dir, model_name, sampling_num, min_dist_p, max_dist_p, longer_infer_lens, round_stop_state_num, draw_seq=False, draw_order=False, state_dependent=True): model_params_default = hparams.get_default_hparams_rough() model_params = update_hyperparams(model_params_default, model_base_dir, model_name, infer_dataset=test_dataset) [test_set, eval_hps_model, sample_hps_model] = \ load_dataset_testing(test_image_base_dir, test_dataset, test_image_name, model_params) test_image_raw_name = test_image_name[:test_image_name.find('.')] model_dir = os.path.join(model_base_dir, model_name) reset_graph() sampling_model = VirtualSketchingModel(sample_hps_model) # differentiable pasting graph paste_v3_func = DiffPastingV3(sample_hps_model.raster_size) tfconfig = tf.ConfigProto() tfconfig.gpu_options.allow_growth = True sess = tf.InteractiveSession(config=tfconfig) sess.run(tf.global_variables_initializer()) # loads the weights from checkpoint into our model snapshot_step = load_checkpoint(sess, model_dir, gen_model_pretrain=True) print('snapshot_step', snapshot_step) sampling_dir = os.path.join(sampling_base_dir, test_dataset + '__' + model_name) os.makedirs(sampling_dir, exist_ok=True) for sampling_i in range(sampling_num): input_photos, init_cursors, test_image_size = test_set.get_test_image() # input_photos: (1, image_size, image_size, 3), [0-stroke, 1-BG] # init_cursors: (N, 1, 2), in size [0.0, 1.0) print() print(test_image_name, ', image_size:', test_image_size, ', sampling_i:', sampling_i) print('Processing ...') if init_cursors.ndim == 3: init_cursors = np.expand_dims(init_cursors, axis=0) input_photos = input_photos[0:1, :, :, :] ori_img = (input_photos.copy()[0] * 255.0).astype(np.uint8) ori_img_png = Image.fromarray(ori_img, 'RGB') ori_img_png.save(os.path.join(sampling_dir, test_image_raw_name + '_input.png'), 'PNG') # decoding for sampling strokes_raw_out_list, states_raw_out_list, states_soft_out_list, pred_imgs_out, \ window_size_out_list, round_new_cursors, round_new_lengths = sample( sess, sampling_model, input_photos, init_cursors, test_image_size, eval_hps_model.max_seq_len, longer_infer_lens, state_dependent, paste_v3_func, round_stop_state_num, min_dist_p, max_dist_p) # pred_imgs_out: (N, H, W), [0.0-BG, 1.0-stroke] print('## round_lengths:', len(round_new_lengths), ':', round_new_lengths) output_i = 0 strokes_raw_out = np.stack(strokes_raw_out_list[output_i], axis=0) states_raw_out = states_raw_out_list[output_i] states_soft_out = states_soft_out_list[output_i] window_size_out = window_size_out_list[output_i] multi_cursors = [init_cursors[0, output_i, 0]] for c_i in range(len(round_new_cursors)): best_cursor = round_new_cursors[c_i][output_i, 0] # (2) multi_cursors.append(best_cursor) assert len(multi_cursors) == len(round_new_lengths) print('strokes_raw_out', strokes_raw_out.shape) clean_states_soft_out = np.array(states_soft_out) # (N) flag_list = strokes_raw_out[:, 0].astype(np.int32) # (N) drawing_len = len(flag_list) - np.sum(flag_list) assert drawing_len >= 0 # print(' flag raw\t soft\t x1\t\t y1\t\t x2\t\t y2\t\t r2\t\t s2') for i in range(strokes_raw_out.shape[0]): flag, x1, y1, x2, y2, r2, s2 = strokes_raw_out[i] win_size = window_size_out[i] out_format = '#%d: %d | %.4f, %.4f, %.4f, %.4f, %.4f, %.4f, %.4f, %.4f' out_values = (i, flag, states_raw_out[i], clean_states_soft_out[i], x1, y1, x2, y2, r2, s2) out_log = out_format % out_values # print(out_log) print('Saving results ...') save_seq_data(sampling_dir, test_image_raw_name + '_' + str(sampling_i), strokes_raw_out, multi_cursors, test_image_size, round_new_lengths, eval_hps_model.min_width) draw_strokes(strokes_raw_out, sampling_dir, test_image_raw_name + '_' + str(sampling_i) + '_pred.png', ori_img, test_image_size, multi_cursors, round_new_lengths, eval_hps_model.min_width, eval_hps_model.cursor_type, sample_hps_model.raster_size, sample_hps_model.min_window_size, sess, pasting_func=paste_v3_func, save_seq=draw_seq, draw_order=draw_order) def main(model_name, test_image_name, sampling_num): test_dataset = 'rough_sketches' test_image_base_dir = 'sample_inputs' sampling_base_dir = 'outputs/sampling' model_base_dir = 'outputs/snapshot' state_dependent = False longer_infer_lens = [128 for _ in range(10)] round_stop_state_num = 12 min_dist_p = 0.3 max_dist_p = 0.9 draw_seq = False draw_color_order = True # set numpy output to something sensible np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True) main_testing(test_image_base_dir, test_dataset, test_image_name, sampling_base_dir, model_base_dir, model_name, sampling_num, min_dist_p=min_dist_p, max_dist_p=max_dist_p, draw_seq=draw_seq, draw_order=draw_color_order, state_dependent=state_dependent, longer_infer_lens=longer_infer_lens, round_stop_state_num=round_stop_state_num) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--input', '-i', type=str, default='', help="The test image name.") parser.add_argument('--model', '-m', type=str, default='pretrain_rough_sketches', help="The trained model.") parser.add_argument('--sample', '-s', type=int, default=1, help="The number of outputs.") args = parser.parse_args() assert args.input != '' assert args.sample > 0 main(args.model, args.input, args.sample)