test scripts

1 files changed, 0 insertions, 146 deletions

diff --git a/test_effects.py b/test_effects.py
deleted file mode 100644
index 7f9430c..0000000
--- a/test_effects.py
+++ /dev/null
@@ -1,146 +0,0 @@
-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)
-