new scripts using canny

1 files changed, 111 insertions, 0 deletions

diff --git a/canny.py b/canny.py
new file mode 100644
index 0000000..b8dc6e8
--- /dev/null
+++ b/canny.py
@@ -0,0 +1,111 @@
+import os
+from options.test_options import TestOptions
+from data import CreateRecursiveDataLoader
+from models import create_model
+from util.visualizer import Visualizer
+from util.util import mkdirs
+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 time as time
+import numpy as np
+import cv2
+
+import subprocess
+from time import sleep
+
+if __name__ == '__main__':
+    opt = TestOptions().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()
+
+    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)
+        if dataset.name() == 'RecursiveDatasetDataLoader':
+            # print(visuals.keys())
+            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)
+            # s = 256
+            # p = 8
+            # im = imresize(im, (s-p, s-p), interp='bicubic')
+            # image_pil = Image.fromarray(im)
+            # image_pil = ImageOps.expand(image_pil, p)
+            # image_pil.save(save_path)
+            # copyfile(save_path, final_path)
+            #if last_im is not None:
+            #    frac_a = 999/1000
+            #    frac_b = 1/1000
+            #    tmp_im = im.copy()
+            #    array_a = np.multiply(im, frac_a)
+            #    array_b = np.multiply(last_im, frac_b)
+            #    # im = np.add(array_a, array_b).astype('int8')
+            #    # print(im.shape, im.dtype)
+            #    last_im = np.roll(tmp_im, 1, axis=1)
+            #else:
+            #    last_im = im.copy()
+            #    print(im.shape, im.dtype)
+            image_pil = Image.fromarray(im, mode='RGB')
+            image_pil.save(tmp_path)
+            os.rename(tmp_path, render_path)
+
+            cmd = ("convert", render_path, '-canny', '0x1+10%+30%', tmp_path)
+            process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
+            output, error = process.communicate()
+
+            os.rename(tmp_path, edges_path)
+
+    webpage.save()
+
+    os.remove(render_dir + "frame_00000.png")
+
+    t = time.time()
+    t /= 60
+    t %= 525600
+    video_fn = opt.name + "_" + opt.experiment + "_canny_" + str(opt.how_many) + "_" + str(int(t)) + ".mp4"
+
+    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)