test effects script

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)
+