diff options
| -rw-r--r-- | options/dataset_options.py | 28 | ||||
| -rw-r--r-- | test-mogrify-recursive.sh | 194 | ||||
| -rw-r--r-- | test-mogrify.py | 225 |
3 files changed, 246 insertions, 201 deletions
diff --git a/options/dataset_options.py b/options/dataset_options.py index 0040540..530a3cf 100644 --- a/options/dataset_options.py +++ b/options/dataset_options.py @@ -78,11 +78,37 @@ class DatasetOptions(BaseOptions): self.parser.add_argument( '--recursive-frac', - default=0.5, + default=0.3, type=float, help='amount of previous step to use in recursion' ) + self.parser.add_argument( + '--sequence', + action='store_true', + help='recurse guided by image sequence' + ) + + self.parser.add_argument( + '--sequence-frac', + default=0.2, + type=float, + help='amount of sequence image to use in recursion' + ) + + self.parser.add_argument( + '--recurse-roll', + default=0, + type=int, + help='in px, roll mixed recursed image 1px to reduce vertical feedback' + ) + + self.parser.add_argument( + '--recurse-roll-axis', + default=1, + type=float, + help='axis of roll' + ) ### GRAYSCALE self.parser.add_argument( diff --git a/test-mogrify-recursive.sh b/test-mogrify-recursive.sh index 7455c6f..e60d8bd 100644 --- a/test-mogrify-recursive.sh +++ b/test-mogrify-recursive.sh @@ -4,124 +4,112 @@ tag='spin' dataset='/home/lens/Desktop/woods_videos/processed/dataset/B/' how_many=200 -python test-mogrify.py \ - --dataroot "$dataset/train/" \ - --start_img "$dataset/test/frame_00003.png" \ - --experiment "${tag}_dark" \ - --name pcfade \ - --loadSize 256 --fineSize 256 --how_many $how_many \ - --model test --which_model_netG unet_256 \ - --which_direction AtoB --dataset_mode recursive \ - --recursive --clahe --posterize --blur \ - --recursive-frac $recursive_frac \ - --norm batch - # python test-mogrify.py \ # --dataroot "$dataset/train/" \ # --start_img "$dataset/test/frame_00003.png" \ -# --experiment "${tag}_dark_reverse" \ -# --name pcfade \ -# --loadSize 256 --fineSize 256 --how_many $how_many \ -# --model test --which_model_netG unet_256 \ -# --which_direction BtoA --dataset_mode recursive \ -# --recursive --clahe --posterize --blur \ -# --recursive-frac $recursive_frac \ -# --norm batch - -# python test-mogrify.py \ -# --dataroot "$dataset/train/" \ -# --start_img "$dataset/test/frame_01003.png" \ -# --experiment "${tag}_1003" \ +# --experiment "${tag}_dark" \ # --name pcfade \ # --loadSize 256 --fineSize 256 --how_many $how_many \ # --model test --which_model_netG unet_256 \ # --which_direction AtoB --dataset_mode recursive \ -# --recursive --clahe --posterize --blur \ +# --recursive --clahe --posterize \ # --recursive-frac $recursive_frac \ # --norm batch -# python test-mogrify.py \ -# --dataroot "$dataset/train/" \ -# --start_img "$dataset/test/frame_01003.png" \ -# --experiment "${tag}_1003_0_1frac" \ -# --name pcfade \ -# --loadSize 256 --fineSize 256 --how_many $how_many \ -# --model test --which_model_netG unet_256 \ -# --which_direction AtoB --dataset_mode recursive \ -# --recursive --clahe --posterize --blur \ -# --recursive-frac 0.1 \ -# --norm batch +python test-mogrify.py \ + --dataroot "$dataset/train/" \ + --start_img "$dataset/test/frame_00003.png" \ + --experiment "${tag}_dark_reverse" \ + --name pcfade \ + --loadSize 256 --fineSize 256 --how_many $how_many \ + --model test --which_model_netG unet_256 \ + --which_direction BtoA --dataset_mode recursive \ + --recursive --clahe --posterize \ + --recursive-frac $recursive_frac \ + --norm batch -# python test-mogrify.py \ -# --dataroot "$dataset/train/" \ -# --start_img "$dataset/test/frame_01003.png" \ -# --experiment "${tag}_1003_0_8frac" \ -# --name pcfade \ -# --loadSize 256 --fineSize 256 --how_many $how_many \ -# --model test --which_model_netG unet_256 \ -# --which_direction AtoB --dataset_mode recursive \ -# --recursive --clahe --posterize --blur \ -# --recursive-frac 0.8 \ -# --norm batch +python test-mogrify.py \ + --dataroot "$dataset/train/" \ + --start_img "$dataset/test/frame_01003.png" \ + --experiment "${tag}_1003" \ + --name pcfade \ + --loadSize 256 --fineSize 256 --how_many $how_many \ + --model test --which_model_netG unet_256 \ + --which_direction AtoB --dataset_mode recursive \ + --recursive --clahe --posterize \ + --recursive-frac $recursive_frac \ + --norm batch -# python test-mogrify.py \ -# --dataroot "$dataset/train/" \ -# --start_img "$dataset/test/frame_05003.png" \ -# --experiment "${tag}_5003" \ -# --name pcfade \ -# --loadSize 256 --fineSize 256 --how_many $how_many \ -# --model test --which_model_netG unet_256 \ -# --which_direction AtoB --dataset_mode recursive \ -# --recursive --clahe --posterize --blur \ -# --recursive-frac $recursive_frac \ -# --norm batch +python test-mogrify.py \ + --dataroot "$dataset/train/" \ + --start_img "$dataset/test/frame_01003.png" \ + --experiment "${tag}_1003_0_1frac" \ + --name pcfade \ + --loadSize 256 --fineSize 256 --how_many $how_many \ + --model test --which_model_netG unet_256 \ + --which_direction AtoB --dataset_mode recursive \ + --recursive --clahe --posterize \ + --recursive-frac 0.1 \ + --norm batch -# python test-mogrify.py \ -# --dataroot "$dataset/train/" \ -# --start_img "$dataset/train/frame_05005.png" \ -# --experiment "${tag}_5005_rec_clahe_poster_blur" \ -# --name pcfade \ -# --loadSize 256 --fineSize 256 --how_many $how_many \ -# --model test --which_model_netG unet_256 \ -# --which_direction AtoB --dataset_mode recursive \ -# --recursive --clahe --posterize --blur \ -# --recursive-frac $recursive_frac \ -# --norm batch +python test-mogrify.py \ + --dataroot "$dataset/train/" \ + --start_img "$dataset/test/frame_01003.png" \ + --experiment "${tag}_1003_0_8frac" \ + --name pcfade \ + --loadSize 256 --fineSize 256 --how_many $how_many \ + --model test --which_model_netG unet_256 \ + --which_direction AtoB --dataset_mode recursive \ + --recursive --clahe --posterize \ + --recursive-frac 0.8 \ + --norm batch +python test-mogrify.py \ + --dataroot "$dataset/train/" \ + --start_img "$dataset/test/frame_05003.png" \ + --experiment "${tag}_5003" \ + --name pcfade \ + --loadSize 256 --fineSize 256 --how_many $how_many \ + --model test --which_model_netG unet_256 \ + --which_direction AtoB --dataset_mode recursive \ + --recursive --clahe --posterize \ + --recursive-frac $recursive_frac \ + --norm batch -# python test-mogrify.py \ -# --dataroot "$dataset/train/" \ -# --start_img "$dataset/train/frame_05005.png" \ -# --experiment "${tag}_5005_rec_poster_blur" \ -# --name pcfade \ -# --loadSize 256 --fineSize 256 --how_many $how_many \ -# --model test --which_model_netG unet_256 \ -# --which_direction AtoB --dataset_mode recursive \ -# --recursive --posterize --blur \ -# --recursive-frac $recursive_frac \ -# --norm batch -# python test-mogrify.py \ -# --dataroot "$dataset/train/" \ -# --start_img "$dataset/train/frame_05005.png" \ -# --experiment "${tag}_5005_rec_blur" \ -# --name pcfade \ -# --loadSize 256 --fineSize 256 --how_many $how_many \ -# --model test --which_model_netG unet_256 \ -# --which_direction AtoB --dataset_mode recursive \ -# --recursive --blur \ -# --recursive-frac $recursive_frac \ -# --norm batch +python test-mogrify.py \ + --dataroot "$dataset/train/" \ + --start_img "$dataset/train/frame_05005.png" \ + --experiment "${tag}_5005_rec_poster_blur" \ + --name pcfade \ + --loadSize 256 --fineSize 256 --how_many $how_many \ + --model test --which_model_netG unet_256 \ + --which_direction AtoB --dataset_mode recursive \ + --recursive --posterize --blur \ + --recursive-frac $recursive_frac \ + --norm batch -# python test-mogrify.py \ -# --dataroot "$dataset/train/" \ -# --start_img "$dataset/test/frame_10003.png" \ -# --experiment "${tag}_10003" \ -# --name pcfade \ -# --loadSize 256 --fineSize 256 --how_many $how_many \ -# --model test --which_model_netG unet_256 \ -# --which_direction AtoB --dataset_mode recursive \ -# --recursive --clahe --posterize --blur \ -# --recursive-frac $recursive_frac \ -# --norm batch +python test-mogrify.py \ + --dataroot "$dataset/train/" \ + --start_img "$dataset/train/frame_05005.png" \ + --experiment "${tag}_5005_rec_blur" \ + --name pcfade \ + --loadSize 256 --fineSize 256 --how_many $how_many \ + --model test --which_model_netG unet_256 \ + --which_direction AtoB --dataset_mode recursive \ + --recursive --blur \ + --recursive-frac $recursive_frac \ + --norm batch + +python test-mogrify.py \ + --dataroot "$dataset/train/" \ + --start_img "$dataset/test/frame_10003.png" \ + --experiment "${tag}_10003" \ + --name pcfade \ + --loadSize 256 --fineSize 256 --how_many $how_many \ + --model test --which_model_netG unet_256 \ + --which_direction AtoB --dataset_mode recursive \ + --recursive --clahe --posterize --blur \ + --recursive-frac $recursive_frac \ + --norm batch diff --git a/test-mogrify.py b/test-mogrify.py index 12e042e..392b520 100644 --- a/test-mogrify.py +++ b/test-mogrify.py @@ -20,129 +20,160 @@ import subprocess from time import sleep if __name__ == '__main__': - opt = TestOptions().parse() - data_opt = DatasetOptions().parse(opt.unknown) - opt.nThreads = 1 # test code only supports nThreads = 1 - opt.batchSize = 1 # test code only supports batchSize = 1 - opt.serial_batches = True # no shuffle - opt.no_flip = True # no flip - opt.experiment = data_opt.experiment # opt.start_img.split("/")[-1].split(".")[0] + opt = TestOptions().parse() + data_opt = DatasetOptions().parse(opt.unknown) + opt.nThreads = 1 # test code only supports nThreads = 1 + opt.batchSize = 1 # test code only supports batchSize = 1 + opt.serial_batches = True # no shuffle + opt.no_flip = True # no flip + opt.experiment = data_opt.experiment # opt.start_img.split("/")[-1].split(".")[0] - d = datetime.now() - tag = "{}_{}_{}".format( - opt.name, data_opt.experiment, - d.strftime('%Y%m%d%H%M')) + d = datetime.now() + tag = "{}_{}_{}".format( + opt.name, data_opt.experiment, + d.strftime('%Y%m%d%H%M')) - opt.tag = tag # = "pcfade___201805150250" + opt.tag = tag # = "pcfade___201805150250" - opt.render_dir = render_dir = opt.results_dir + opt.name + "/" + tag + "/" + opt.render_dir = render_dir = opt.results_dir + opt.name + "/" + tag + "/" + A_offset = 0 + A_im = None + A_dir = None - print("create render_dir: {}".format(render_dir)) - if os.path.exists(render_dir): - rmtree(render_dir) - mkdirs(render_dir) + print("create render_dir: {}".format(render_dir)) + if os.path.exists(render_dir): + rmtree(render_dir) + mkdirs(render_dir) - # cmd = ("convert", opt.start_img, '-canny', '0x1+10%+30%', render_dir + "frame_00000.png") - # process = subprocess.Popen(cmd, stdout=subprocess.PIPE) - # output, error = process.communicate() + # cmd = ("convert", opt.start_img, '-canny', '0x1+10%+30%', render_dir + "frame_00000.png") + # process = subprocess.Popen(cmd, stdout=subprocess.PIPE) + # output, error = process.communicate() - copyfile(opt.start_img, render_dir + "frame_00000.png") + def load_first_frame(): + if data_opt.just_copy: + copyfile(opt.start_img, render_dir + "frame_00000.png") + else: + A_im = Image.open(opt.start_img).convert('RGB') + A = process_image(im) + cv2.imwrite(render_dir + "frame_00000.png", img) - i_offset = 0 numz = re.findall(r'\d+', os.path.basename(opt.start_img)) if len(numz) > 0: - i_offset = int(numz[0]) + A_offset = int(numz[0]) + if A_offset: + print ">> starting offset: {}".format(A_offset) + A_dir = opt.start_img.replace(numz[0], "{:05d}") - data_loader = CreateRecursiveDataLoader(opt) - dataset = data_loader.load_data() - ds = dataset.dataset - model = create_model(opt) - visualizer = Visualizer(opt) - # create website - # web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch)) - # webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch)) - # test - last_im = None - for i, data in enumerate(data_loader): - if i >= opt.how_many: - break - model.set_input(data) - model.test() - visuals = model.get_current_visuals() - img_path = model.get_image_paths() + def process_image(im): + img = im[:, :, ::-1].copy() - if (i % 20) == 0: - print('%04d: process image... %s' % (i, img_path)) - # ims = visualizer.save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio) + if data_opt.clahe is True: + lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) + l, a, b = cv2.split(lab) + clahe = cv2.createCLAHE(clipLimit=data_opt.clip_limit, tileGridSize=(8,8)) + l = clahe.apply(l) + limg = cv2.merge((l,a,b)) + img = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR) - im = visuals['fake_B'] - tmp_path = render_dir + "frame_{:05d}_tmp.png".format(i+1) - edges_path = render_dir + "frame_{:05d}.png".format(i+1) - render_path = render_dir + "ren_{:05d}.png".format(i+1) + if data_opt.posterize is True: + img = cv2.pyrMeanShiftFiltering(img, data_opt.spatial_window, data_opt.color_window) + if data_opt.grayscale is True: + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + if data_opt.blur is True: + img = cv2.GaussianBlur(img, (data_opt.blur_radius, data_opt.blur_radius), data_opt.blur_sigma) + if data_opt.canny is True: + img = cv2.Canny(img, data_opt.canny_lo, data_opt.canny_hi) - image_pil = Image.fromarray(im, mode='RGB') - image_pil.save(tmp_path) - os.rename(tmp_path, render_path) + load_first_frame() - if dataset.name() == 'RecursiveDatasetDataLoader': - if data_opt.recursive and last_im is not None: - tmp_im = im.copy() + data_loader = CreateRecursiveDataLoader(opt) + dataset = data_loader.load_data() + ds = dataset.dataset + model = create_model(opt) + visualizer = Visualizer(opt) + # create website + # web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch)) + # webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch)) + # test + last_im = None + for i, data in enumerate(data_loader): + if i >= opt.how_many: + break + model.set_input(data) + model.test() + visuals = model.get_current_visuals() + img_path = model.get_image_paths() - frac_a = data_opt.recursive_frac - frac_b = 1.0 - frac_a + if (i % 20) == 0: + print('%04d: process image... %s' % (i, img_path)) + # ims = visualizer.save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio) - array_a = np.multiply(im.astype('float64'), frac_a) - array_b = np.multiply(last_im.astype('float64'), frac_b) - im = np.add(array_a, array_b).astype('uint8') - # print(im.shape, im.dtype) - # last_im = np.roll(tmp_im, 1, axis=1) - else: - last_im = im.copy().astype('uint8') - tmp_im = im.copy().astype('uint8') - #print(im.shape, im.dtype) + im = visuals['fake_B'] + tmp_path = render_dir + "frame_{:05d}_tmp.png".format(i+1) + edges_path = render_dir + "frame_{:05d}.png".format(i+1) + render_path = render_dir + "ren_{:05d}.png".format(i+1) + sequence_path = A_dir.format(A_offset+i+1) + # A_offset + image_pil = Image.fromarray(im, mode='RGB') + image_pil.save(tmp_path) + os.rename(tmp_path, render_path) - image_pil = Image.fromarray(im, mode='RGB') - im = np.asarray(image_pil).astype('uint8') - #print(im.shape, im.dtype) + if dataset.name() == 'RecursiveDatasetDataLoader': + if data_opt.recursive and last_im is not None: + tmp_im = im.copy() - img = im[:, :, ::-1].copy() + if data_opt.sequence: + A_im = Image.open(sequence_path).convert('RGB') + frac_a = data_opt.recursive_frac + frac_b = data_opt.sequence_frac + frac_c = 1.0 - frac_a - frac_b + array_a = np.multiply(last_im.astype('float64'), frac_a) + array_b = np.multiply(A_im.astype('float64'), frac_b) + array_c = np.multiply(im.astype('float64'), frac_c) + comb_ab = np.add(array_a, array_b) + comb_abc = np.add(array_ab, array_c) + im = comb_abc.astype('uint8') - if data_opt.clahe is True: - lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) - l, a, b = cv2.split(lab) - clahe = cv2.createCLAHE(clipLimit=data_opt.clip_limit, tileGridSize=(8,8)) - l = clahe.apply(l) - limg = cv2.merge((l,a,b)) - img = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR) + else: + frac_a = data_opt.recursive_frac + frac_b = 1.0 - frac_a + array_a = np.multiply(last_im.astype('float64'), frac_a) + array_b = np.multiply(im.astype('float64'), frac_b) + im = np.add(array_a, array_b).astype('uint8') - if data_opt.posterize is True: - img = cv2.pyrMeanShiftFiltering(img, data_opt.spatial_window, data_opt.color_window) - if data_opt.grayscale is True: - img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - if data_opt.blur is True: - img = cv2.GaussianBlur(img, (data_opt.blur_radius, data_opt.blur_radius), data_opt.blur_sigma) - if data_opt.canny is True: - img = cv2.Canny(img, data_opt.canny_lo, data_opt.canny_hi) + if data_opt.recurse_roll != 0: + last_im = np.roll(tmp_im, data_opt.recurse_roll, axis=data_opt.recurse_roll_axis) - cv2.imwrite(tmp_path, img) - os.rename(tmp_path, edges_path) + else: + last_im = im.copy().astype('uint8') + tmp_im = im.copy().astype('uint8') + #print(im.shape, im.dtype) - # webpage.save() + image_pil = Image.fromarray(im, mode='RGB') + im = np.asarray(image_pil).astype('uint8') + #print(im.shape, im.dtype) - # os.remove(render_dir + "frame_00000.png") + img = process_image(im) - print(opt.render_dir) - video_fn = tag + "_mogrify.mp4" + cv2.imwrite(tmp_path, img) + os.rename(tmp_path, edges_path) - cmd = ("ffmpeg", "-i", render_dir + "ren_%05d.png", "-y", "-c:v", "libx264", "-vf", "fps=30", "-pix_fmt", "yuv420p", "-s", "456x256", render_dir + video_fn) - process = subprocess.Popen(cmd, stdout=subprocess.PIPE) - output, error = process.communicate() + # webpage.save() - print("________") + # os.remove(render_dir + "frame_00000.png") - cmd = ("scp", render_dir + video_fn, "jules@asdf.us:asdf/neural/") - process = subprocess.Popen(cmd, stdout=subprocess.PIPE) - output, error = process.communicate() + print(opt.render_dir) + video_fn = tag + "_mogrify.mp4" - print("https://asdf.us/neural/" + video_fn) + cmd = ("ffmpeg", "-i", render_dir + "ren_%05d.png", "-y", "-c:v", "libx264", "-vf", "fps=30", "-pix_fmt", "yuv420p", "-s", "456x256", render_dir + video_fn) + process = subprocess.Popen(cmd, stdout=subprocess.PIPE) + output, error = process.communicate() + + print("________") + + cmd = ("scp", render_dir + video_fn, "jules@asdf.us:asdf/neural/") + process = subprocess.Popen(cmd, stdout=subprocess.PIPE) + output, error = process.communicate() + + print("https://asdf.us/neural/" + video_fn) |