diff options
| -rw-r--r-- | options/base_options.py | 2 | ||||
| -rw-r--r-- | options/dataset_options.py | 17 | ||||
| -rw-r--r-- | test_effects.py | 146 |
3 files changed, 163 insertions, 2 deletions
diff --git a/options/base_options.py b/options/base_options.py index d569928..105292f 100644 --- a/options/base_options.py +++ b/options/base_options.py @@ -48,7 +48,7 @@ class BaseOptions(): def parse(self): if not self.initialized: self.initialize() - self.opt = self.parser.parse_args() + self.opt = self.parser.parse_known_args() self.opt.isTrain = self.isTrain # train or test return self.opt diff --git a/options/dataset_options.py b/options/dataset_options.py index 057b940..5eca374 100644 --- a/options/dataset_options.py +++ b/options/dataset_options.py @@ -59,6 +59,21 @@ class DatasetOptions(BaseOptions): ## IMAGE FILTERS + ### RECURSION + + self.parser.add_argument( + '--recursive', + action='store_true', + help='recurse on previous output' + ) + + self.parser.add_argument( + '--recursive-frac', + default=0.5, + type=float, + help='amount of previous step to use in recursion' + ) + ### GRAYSCALE self.parser.add_argument( @@ -166,5 +181,5 @@ class DatasetOptions(BaseOptions): def parse(self): if not self.initialized: self.initialize() - self.opt = self.parser.parse_args() + self.opt = self.parser.parse_known_args() return self.opt
\ No newline at end of file diff --git a/test_effects.py b/test_effects.py new file mode 100644 index 0000000..7f9430c --- /dev/null +++ b/test_effects.py @@ -0,0 +1,146 @@ +import os +from options.test_options import TestOptions +from options.dataset_options import TestOptions +from data import CreateRecursiveDataLoader +from models import create_model +from util.visualizer import Visualizer +from util.util import mkdirs, crop_image +from util import html +from shutil import move, copyfile +from PIL import Image, ImageOps +from skimage.transform import resize +from scipy.misc import imresize +from shutil import copyfile, rmtree +import numpy as np +import cv2 +import time + +import subprocess +from time import sleep + +blur = 3 +sigma = 0 +canny_lo = 10 +canny_hi = 220 +frac_a = 0.99 +frac_b = 1 - frac_a + +if __name__ == '__main__': + opt = TestOptions().parse() + data_opt = DatasetOptions().parse() + 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 = opt.start_img.split("/")[-1].split(".")[0] + + render_dir = opt.results_dir + opt.name + "/exp:" + opt.experiment + "/" + + 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() + + #copyfile(opt.start_img, render_dir + "frame_00000.png") + + 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() + print('%04d: process image... %s' % (i, img_path)) + ims = visualizer.save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio) + + 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) + + image_pil = Image.fromarray(im, mode='RGB') + image_pil.save(tmp_path) + os.rename(tmp_path, render_path) + + if dataset.name() == 'RecursiveDatasetDataLoader': + if data_opt.recursive and last_im is not None: + tmp_im = im.copy() + + frac_a = data_opt.recursive_frac + frac_b = 1.0 - frac_a + + 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) + + image_pil = Image.fromarray(im, mode='RGB') + im = np.asarray(image_pil).astype('uint8') + #print(im.shape, im.dtype) + + img = im[:, :, ::-1].copy() + + 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) + + 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) + + cv2.imwrite(tmp_path, img) + os.rename(tmp_path, edges_path) + + + webpage.save() + + os.remove(render_dir + "frame_00000.png") + + t = time.time() + t /= 60 + t %= 525600 + video_fn = "{}_{}_{}_mogrify.mp4".format( + opt.name, opt.experiment, + # opt.how_many, frac_a, + # blur, sigma, canny_lo, canny_hi, + int(t)) + + cmd = ("/usr/bin/ffmpeg", "-i", render_dir + "ren_%05d.png", "-y", "-c:v", "libx264", "-vf", "fps=30", "-pix_fmt", "yuv420p", 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) + |