test

1 files changed, 128 insertions, 97 deletions

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)