def post_params(): myid = str(int(time.time() * 10000)) + ".jpg" tmp_img_post_path = RESULT_IMG_PATH + myid style_img_url = STYLE_IMG_PATH + request.form['style_img_url'] content_img_url = CONTENT_IMG_PATH + request.form['content_img_url'] alpha = float(request.form['alpha']) content_img = get_img(content_img_url) style_size = int(float(request.form['style_scale']) * 512) keep_colors = False # style_img = get_img_crop(style_img_url, resize=style_size) style_img = get_img_crop(style_img_url) if style_size > 0: style_img = resize_to(style_img, style_size) if keep_colors: style_img = preserve_colors_np(style_img, content_img) # Run the frame through the style network stylized_rgb = wct_model.predict(content_img, style_img, alpha, False, 0.6, False) save_img(tmp_img_post_path, stylized_rgb) return Response(myid, status=200, mimetype='application/json')
def stylize_output(wct_model, content_img, styles): if args.crop_size > 0: styles = [center_crop(style) for style in styles] if args.keep_colors: styles = [preserve_colors_np(style, content_img) for style in styles] # Run the frame through the style network stylized = content_img for _ in range(args.passes): stylized = wct_model.predict(stylized, styles) # Stitch the style + stylized output together, but only if there's one style image if args.concat: # Resize style img to same height as frame style_img_resized = scipy.misc.imresize( styles[0], (stylized.shape[0], stylized.shape[0])) stylized = np.hstack([style_img_resized, stylized]) return stylized
def main(): # Load the WCT model wct_model = WCT(checkpoints=args.checkpoints, relu_targets=args.relu_targets, vgg_path=args.vgg_path, device=args.device, ss_patch_size=args.ss_patch_size, ss_stride=args.ss_stride) # Create needed dirs in_dir = os.path.join(args.tmp_dir, 'input') out_dir = os.path.join(args.tmp_dir, 'sytlized') if not os.path.exists(in_dir): os.makedirs(in_dir) if not os.path.exists(out_dir): os.makedirs(out_dir) in_args = ['ffmpeg', '-i', args.in_path, '%s/frame_%%d.png' % in_dir] subprocess.call(" ".join(in_args), shell=True) base_names = os.listdir(in_dir) in_files = [os.path.join(in_dir, x) for x in base_names] out_files = [os.path.join(out_dir, x) for x in base_names] style_img = get_img(args.style_path) if args.style_size > 0: style_img = resize_to(style_img, args.style_size) if args.crop_size > 0: style_img = center_crop(style_img, args.crop_size) s = time.time() for in_f, out_f in zip(in_files, out_files): print('{} -> {}'.format(in_f, out_f)) content_img = get_img(in_f) if args.keep_colors: style_rgb = preserve_colors_np(style_img, content_img) else: style_rgb = style_img stylized = wct_model.predict(content_img, style_rgb, args.alpha, args.swap5, args.ss_alpha) if args.passes > 1: for _ in range(args.passes - 1): stylized = wct_model.predict(stylized, style_rgb, args.alpha) # Stitch the style + stylized output together, but only if there's one style image if args.concat: # Resize style img to same height as frame style_img_resized = scipy.misc.imresize( style_rgb, (stylized.shape[0], stylized.shape[0])) stylized = np.hstack([style_img_resized, stylized]) save_img(out_f, stylized) fr = 30 out_args = [ 'ffmpeg', '-i', '%s/frame_%%d.png' % out_dir, '-f', 'mp4', '-q:v', '0', '-vcodec', 'mpeg4', '-r', str(fr), args.out_path ] subprocess.call(" ".join(out_args), shell=True) print('Video at: %s' % args.out_path) if args.keep_tmp is False: shutil.rmtree(args.tmp_dir) print('Processed in:', (time.time() - s))
def main(): start = time.time() # Load the WCT model wct_model = WCT(checkpoints=args.checkpoints, relu_targets=args.relu_targets, vgg_path=args.vgg_path, device=args.device, ss_patch_size=args.ss_patch_size, ss_stride=args.ss_stride) # Get content & style full paths if os.path.isdir(args.content_path): content_files = get_files(args.content_path) else: # Single image file content_files = [args.content_path] if os.path.isdir(args.style_path): style_files = get_files(args.style_path) if args.random > 0: style_files = np.random.choice(style_files, args.random) else: # Single image file style_files = [args.style_path] os.makedirs(args.out_path, exist_ok=True) count = 0 # Apply each style to each content image for content_fullpath in content_files: content_prefix, content_ext = os.path.splitext(content_fullpath) content_prefix = os.path.basename( content_prefix) # Extract filename prefix without ext content_img = get_img(content_fullpath) if args.content_size > 0: content_img = resize_to(content_img, args.content_size) for style_fullpath in style_files: style_prefix, _ = os.path.splitext(style_fullpath) style_prefix = os.path.basename( style_prefix) # Extract filename prefix without ext # style_img = get_img_crop(style_fullpath, resize=args.style_size, crop=args.crop_size) # style_img = resize_to(get_img(style_fullpath), content_img.shape[0]) style_img = get_img(style_fullpath) if args.style_size > 0: style_img = resize_to(style_img, args.style_size) if args.crop_size > 0: style_img = center_crop(style_img, args.crop_size) if args.keep_colors: style_img = preserve_colors_np(style_img, content_img) # if args.noise: # Generate textures from noise instead of images # frame_resize = np.random.randint(0, 256, frame_resize.shape, np.uint8) # frame_resize = gaussian_filter(frame_resize, sigma=0.5) # Run the frame through the style network stylized_rgb = wct_model.predict(content_img, style_img, args.alpha, args.swap5, args.ss_alpha, args.adain) if args.passes > 1: for _ in range(args.passes - 1): stylized_rgb = wct_model.predict(stylized_rgb, style_img, args.alpha, args.swap5, args.ss_alpha, args.adain) # Stitch the style + stylized output together, but only if there's one style image if args.concat: # Resize style img to same height as frame style_img_resized = scipy.misc.imresize( style_img, (stylized_rgb.shape[0], stylized_rgb.shape[0])) # margin = np.ones((style_img_resized.shape[0], 10, 3)) * 255 stylized_rgb = np.hstack([style_img_resized, stylized_rgb]) # Format for out filename: {out_path}/{content_prefix}_{style_prefix}.{content_ext} out_f = os.path.join( args.out_path, '{}_{}{}'.format(content_prefix, style_prefix, content_ext)) # out_f = f'{content_prefix}_{style_prefix}.{content_ext}' save_img(out_f, stylized_rgb) count += 1 print("{}: Wrote stylized output image to {}".format(count, out_f)) print("Finished stylizing {} outputs in {}s".format( count, time.time() - start))
def main(): # Load the AdaIN model ada_in = AdaINference(args.checkpoint, args.vgg_path, device=args.device) # Load a panel to control style settings style_window = StyleWindow(args.style_path, args.style_size, args.scale, args.alpha, args.interpolate) # Start the webcam stream cap = WebcamVideoStream(args.video_source, args.width, args.height).start() _, frame = cap.read() # Grab a sample frame to calculate frame size frame_resize = cv2.resize(frame, None, fx=args.scale, fy=args.scale) img_shape = frame_resize.shape # Setup video out writer if args.video_out is not None: fourcc = cv2.VideoWriter_fourcc(*'XVID') if args.concat: out_shape = (img_shape[1]+img_shape[0],img_shape[0]) # Make room for the style img else: out_shape = (img_shape[1],img_shape[0]) print('Video Out Shape:', out_shape) video_writer = cv2.VideoWriter(args.video_out, fourcc, args.fps, out_shape) fps = FPS().start() # Track FPS processing speed keep_colors = args.keep_colors count = 0 while(True): ret, frame = cap.read() if ret is True: frame_resize = cv2.resize(frame, None, fx=style_window.scale, fy=style_window.scale) if args.noise: # Generate textures from noise instead of images frame_resize = np.random.randint(0, 256, frame_resize.shape, np.uint8) frame_resize = gaussian_filter(frame_resize, sigma=0.5) count += 1 print("Frame:",count,"Orig shape:",frame.shape,"New shape",frame_resize.shape) content_rgb = cv2.cvtColor(frame_resize, cv2.COLOR_BGR2RGB) # OpenCV uses BGR, we need RGB if args.random > 0 and count % args.random == 0: style_window.set_style(random=True, style_idx=0) if keep_colors: style_rgb = preserve_colors_np(style_window.style_rgbs[0], content_rgb) else: style_rgb = style_window.style_rgbs[0] if args.interpolate is False: # Run the frame through the style network stylized_rgb = ada_in.predict(content_rgb, style_rgb, style_window.alpha) else: interp_weights = [style_window.interp_weight, 1 - style_window.interp_weight] stylized_rgb = ada_in.predict_interpolate(content_rgb, style_window.style_rgbs, interp_weights, style_window.alpha) # Stitch the style + stylized output together, but only if there's one style image if args.concat and args.interpolate is False: # Resize style img to same height as frame style_rgb_resized = cv2.resize(style_rgb, (stylized_rgb.shape[0], stylized_rgb.shape[0])) stylized_rgb = np.hstack([style_rgb_resized, stylized_rgb]) stylized_bgr = cv2.cvtColor(stylized_rgb, cv2.COLOR_RGB2BGR) if args.video_out is not None: stylized_bgr = cv2.resize(stylized_bgr, out_shape) # Make sure frame matches video size video_writer.write(stylized_bgr) cv2.imshow('AdaIN Style', stylized_bgr) fps.update() key = cv2.waitKey(10) if key & 0xFF == ord('r'): # Load new random style style_window.set_style(random=True, style_idx=0) if args.interpolate: # Load a a second style if interpolating style_window.set_style(random=True, style_idx=1, window='style2') elif key & 0xFF == ord('c'): keep_colors = not keep_colors print("Switching to keep_colors",keep_colors) elif key & 0xFF == ord('q'): # Quit break else: break fps.stop() print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) cap.stop() if args.video_out is not None: video_writer.release() cv2.destroyAllWindows()
def main(): # Load the WCT model wct_model = WCT(checkpoints=args.checkpoints, relu_targets=args.relu_targets, vgg_path=args.vgg_path, device=args.device, ss_patch_size=args.ss_patch_size, ss_stride=args.ss_stride) # Create needed dirs in_dir = os.path.join(args.tmp_dir, 'input') out_dir = os.path.join(args.tmp_dir, 'sytlized') if not os.path.exists(in_dir): os.makedirs(in_dir) if not os.path.exists(out_dir): os.makedirs(out_dir) if os.path.isdir(args.in_path): in_path = get_files(args.in_path) else: # Single image file in_path = [args.in_path] if os.path.isdir(args.style_path): style_files = get_files(args.style_path) else: # Single image file style_files = [args.style_path] print(style_files) import time # time.sleep(999) in_args = [ 'ffmpeg', '-i', args.in_path, '%s/frame_%%d.png' % in_dir ] subprocess.call(" ".join(in_args), shell=True) base_names = os.listdir(in_dir) in_files = [os.path.join(in_dir, x) for x in base_names] out_files = [os.path.join(out_dir, x) for x in base_names] s = time.time() for content_fullpath in in_path: content_prefix, content_ext = os.path.splitext(content_fullpath) content_prefix = os.path.basename(content_prefix) try: for style_fullpath in style_files: style_img = get_img(style_fullpath) if args.style_size > 0: style_img = resize_to(style_img, args.style_size) if args.crop_size > 0: style_img = center_crop(style_img, args.crop_size) style_prefix, _ = os.path.splitext(style_fullpath) style_prefix = os.path.basename(style_prefix) # print("ARRAY: ", style_img) out_v = os.path.join(args.out_path, '{}_{}{}'.format(content_prefix, style_prefix, content_ext)) print("OUT:",out_v) if os.path.isfile(out_v): print("SKIP" , out_v) continue for in_f, out_f in zip(in_files, out_files): print('{} -> {}'.format(in_f, out_f)) content_img = get_img(in_f) if args.keep_colors: style_rgb = preserve_colors_np(style_img, content_img) else: style_rgb = style_img stylized = wct_model.predict(content_img, style_rgb, args.alpha, args.swap5, args.ss_alpha) if args.passes > 1: for _ in range(args.passes-1): stylized = wct_model.predict(stylized, style_rgb, args.alpha) # Stitch the style + stylized output together, but only if there's one style image if args.concat: # Resize style img to same height as frame style_img_resized = scipy.misc.imresize(style_rgb, (stylized.shape[0], stylized.shape[0])) stylized = np.hstack([style_img_resized, stylized]) save_img(out_f, stylized) fr = 30 out_args = [ 'ffmpeg', '-i', '%s/frame_%%d.png' % out_dir, '-f', 'mp4', '-q:v', '0', '-vcodec', 'mpeg4', '-r', str(fr), '"' + out_v + '"' ] print(out_args) subprocess.call(" ".join(out_args), shell=True) print('Video at: %s' % out_v) if args.keep_tmp is True or len(style_files) > 1: continue else: shutil.rmtree(args.tmp_dir) print('Processed in:',(time.time() - s)) print('Processed in:',(time.time() - s)) except Exception as e: print("EXCEPTION: ",e)
def main(): start = time.time() # Load the WCT model wct_model = WCT(checkpoints=args.checkpoints, relu_targets=args.relu_targets, vgg_path=args.vgg_path, device=args.device, ss_patch_size=args.ss_patch_size, ss_stride=args.ss_stride) # Get content & style full paths if os.path.isdir(args.content_path): content_files = get_files(args.content_path) else: # Single image file content_files = [args.content_path] if os.path.isdir(args.style_path): style_files = get_files(args.style_path) if args.random > 0: style_files = np.random.choice(style_files, args.random) else: # Single image file style_files = [args.style_path] os.makedirs(args.out_path, exist_ok=True) count = 0 ### Apply each style to each content image for content_fullpath in content_files: content_prefix, content_ext = os.path.splitext(content_fullpath) content_prefix = os.path.basename(content_prefix) # Extract filename prefix without ext content_img = get_img(content_fullpath) if args.content_size > 0: content_img = resize_to(content_img, args.content_size) for style_fullpath in style_files: style_prefix, _ = os.path.splitext(style_fullpath) style_prefix = os.path.basename(style_prefix) # Extract filename prefix without ext # style_img = get_img_crop(style_fullpath, resize=args.style_size, crop=args.crop_size) # style_img = resize_to(get_img(style_fullpath), content_img.shape[0]) style_img = get_img(style_fullpath) if args.style_size > 0: style_img = resize_to(style_img, args.style_size) if args.crop_size > 0: style_img = center_crop(style_img, args.crop_size) if args.keep_colors: style_img = preserve_colors_np(style_img, content_img) # if args.noise: # Generate textures from noise instead of images # frame_resize = np.random.randint(0, 256, frame_resize.shape, np.uint8) # frame_resize = gaussian_filter(frame_resize, sigma=0.5) # Run the frame through the style network stylized_rgb = wct_model.predict(content_img, style_img, args.alpha, args.swap5, args.ss_alpha, args.adain) if args.passes > 1: for _ in range(args.passes-1): stylized_rgb = wct_model.predict(stylized_rgb, style_img, args.alpha, args.swap5, args.ss_alpha, args.adain) # Stitch the style + stylized output together, but only if there's one style image if args.concat: # Resize style img to same height as frame style_img_resized = scipy.misc.imresize(style_img, (stylized_rgb.shape[0], stylized_rgb.shape[0])) # margin = np.ones((style_img_resized.shape[0], 10, 3)) * 255 stylized_rgb = np.hstack([style_img_resized, stylized_rgb]) # Format for out filename: {out_path}/{content_prefix}_{style_prefix}.{content_ext} out_f = os.path.join(args.out_path, '{}_{}{}'.format(content_prefix, style_prefix, content_ext)) # out_f = f'{content_prefix}_{style_prefix}.{content_ext}' save_img(out_f, stylized_rgb) count += 1 print("{}: Wrote stylized output image to {}".format(count, out_f)) print("Finished stylizing {} outputs in {}s".format(count, time.time() - start))
def main(): # Load the WCT model wct_model = WCT(checkpoints=args.checkpoints, relu_targets=args.relu_targets, vgg_path=args.vgg_path, device=args.device) # Load a panel to control style settings style_window = StyleWindow(args.style_path, args.style_size, args.crop_size, args.scale, args.alpha) # Start the webcam stream cap = WebcamVideoStream(args.video_source, args.width, args.height).start() _, frame = cap.read() # Grab a sample frame to calculate frame size frame_resize = cv2.resize(frame, None, fx=args.scale, fy=args.scale) img_shape = frame_resize.shape # Setup video out writer if args.video_out is not None: fourcc = cv2.VideoWriter_fourcc(*'XVID') if args.concat: out_shape = (img_shape[1] + img_shape[0], img_shape[0] ) # Make room for the style img else: out_shape = (img_shape[1], img_shape[0]) print('Video Out Shape:', out_shape) video_writer = cv2.VideoWriter(args.video_out, fourcc, args.fps, out_shape) fps = FPS().start() # Track FPS processing speed keep_colors = args.keep_colors count = 0 while (True): ret, frame = cap.read() if ret is True: frame_resize = cv2.resize(frame, None, fx=style_window.scale, fy=style_window.scale) if args.noise: # Generate textures from noise instead of images frame_resize = np.random.randint(0, 256, frame_resize.shape, np.uint8) count += 1 print("Frame:", count, "Orig shape:", frame.shape, "New shape", frame_resize.shape) content_rgb = cv2.cvtColor( frame_resize, cv2.COLOR_BGR2RGB) # OpenCV uses BGR, we need RGB if args.random > 0 and count % args.random == 0: style_window.set_style(random=True) if keep_colors: style_rgb = preserve_colors_np(style_window.style_rgb, content_rgb) else: style_rgb = style_window.style_rgb # Run the frame through the style network stylized_rgb = wct_model.predict(content_rgb, style_rgb, style_window.alpha) if args.passes > 1: for i in range(args.passes - 1): stylized_rgb = wct_model.predict(stylized_rgb, style_rgb, style_window.alpha) # Stitch the style + stylized output together, but only if there's one style image if args.concat: # Resize style img to same height as frame style_rgb_resized = cv2.resize( style_rgb, (stylized_rgb.shape[0], stylized_rgb.shape[0])) stylized_rgb = np.hstack([style_rgb_resized, stylized_rgb]) stylized_bgr = cv2.cvtColor(stylized_rgb, cv2.COLOR_RGB2BGR) if args.video_out is not None: stylized_bgr = cv2.resize( stylized_bgr, out_shape) # Make sure frame matches video size video_writer.write(stylized_bgr) cv2.imshow('WCT Universal Style Transfer', stylized_bgr) fps.update() key = cv2.waitKey(10) if key & 0xFF == ord('r'): # Load new random style style_window.set_style(random=True) elif key & 0xFF == ord('c'): # Toggle color preservation keep_colors = not keep_colors print('Switching to keep_colors', keep_colors) elif key & 0xFF == ord('s'): # Save stylized frame out_f = "{}.png".format(time.time()) save_img(out_f, stylized_rgb) print('Saved image to', out_f) elif key & 0xFF == ord('q'): # Quit gracefully break else: break fps.stop() print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) cap.stop() if args.video_out is not None: video_writer.release() cv2.destroyAllWindows()
def main(): # start = time.time() # Load the WCT model wct_model = WCT(checkpoints=args.checkpoints, relu_targets=args.relu_targets, vgg_path=args.vgg_path, device=args.device, ss_patch_size=args.ss_patch_size, ss_stride=args.ss_stride) # Get content & style full paths if os.path.isdir(args.content_path): content_files = get_files(args.content_path) else: # Single image file content_files = [args.content_path] if os.path.isdir(args.style_path): style_files = get_files(args.style_path) if args.random > 0: style_files = np.random.choice(style_files, args.random) else: # Single image file style_files = [args.style_path] os.makedirs(args.out_path, exist_ok=True) thetotal = len(content_files) * len(style_files) count = 1 ### Apply each style to each content image for content_fullpath in content_files: content_prefix, content_ext = os.path.splitext(content_fullpath) content_prefix = os.path.basename( content_prefix) # Extract filename prefix without ext content_img = get_img(content_fullpath) if args.content_size > 0: content_img = resize_to(content_img, args.content_size) for style_fullpath in style_files: style_prefix, _ = os.path.splitext(style_fullpath) style_prefix = os.path.basename( style_prefix) # Extract filename prefix without ext if args.mask: mask_prefix_, _ = os.path.splitext(args.mask) mask_prefix = os.path.basename(mask_prefix_) if args.keep_colors: style_prefix = "KPT_" + style_prefix if args.concat: style_prefix = "CON_" + style_prefix if args.adain: style_prefix = "ADA_" + style_prefix if args.swap5: style_prefix = "SWP_" + style_prefix if args.mask: style_prefix = "MSK_" + mask_prefix + '_' + style_prefix if args.remaster: style_prefix = style_prefix + '_REMASTERED' out_f = os.path.join( args.out_path, '{}_{}{}'.format(content_prefix, style_prefix, content_ext)) if os.path.isfile(out_f): print("SKIP", out_f) count += 1 continue # style_img = get_img_crop(style_fullpath, resize=args.style_size, crop=args.crop_size) # style_img = resize_to(get_img(style_fullpath), content_img.shape[0]) style_img = get_img(style_fullpath) if style_img == ("IMAGE IS BROKEN"): continue if args.style_size > 0: style_img = resize_to(style_img, args.style_size) if args.crop_size > 0: style_img = center_crop(style_img, args.crop_size) if args.keep_colors: style_img = preserve_colors_np(style_img, content_img) # if args.noise: # Generate textures from noise instead of images # frame_resize = np.random.randint(0, 256, frame_resize.shape, np.uint8) # frame_resize = gaussian_filter(frame_resize, sigma=0.5) # Run the frame through the style network stylized_rgb = wct_model.predict(content_img, style_img, args.alpha, args.swap5, args.ss_alpha, args.adain) if args.passes > 1: for _ in range(args.passes - 1): stylized_rgb = wct_model.predict(stylized_rgb, style_img, args.alpha, args.swap5, args.ss_alpha, args.adain) if args.mask: import cv2 cv2.imwrite('./tmp.png', stylized_rgb) stylized_rgb = cv2.imread('./tmp.png') mask = cv2.imread(args.mask, cv2.IMREAD_GRAYSCALE) # from scipy.misc import bytescale # mask = bytescale(mask) # mask = scipy.ndimage.imread(args.mask,flatten=True,mode='L') height, width = stylized_rgb.shape[:2] # print(height, width) # Resize the mask to fit the image. mask = scipy.misc.imresize(mask, (height, width), interp='bilinear') stylized_rgb = cv2.bitwise_and(stylized_rgb, stylized_rgb, mask=mask) # Stitch the style + stylized output together, but only if there's one style image if args.concat: # Resize style img to same height as frame # style_prefix = style_prefix + "CON_" # content_img_resized = scipy.misc.imresize(content_img, (stylized_rgb.shape[0], stylized_rgb.shape[0])) style_img_resized = scipy.misc.imresize( style_img, (stylized_rgb.shape[0], stylized_rgb.shape[0])) # margin = np.ones((style_img_resized.shape[0], 10, 3)) * 255 stylized_rgb = np.hstack([style_img_resized, stylized_rgb]) # Format for out filename: {out_path}/{content_prefix}_{style_prefix}.{content_ext} # out_f = f'{content_prefix}_{style_prefix}.{content_ext}' out_f = os.path.join( args.out_path, '{}_{}{}'.format(content_prefix, style_prefix, content_ext)) if args.remaster: # outf = os.path.join(args.out_path, '{}_{}_REMASTERED{}'.format(content_prefix, style_prefix, content_ext)) stylized_rgb = remaster_pic(stylized_rgb) save_img(out_f, stylized_rgb) totalfiles = len([ name for name in os.listdir(args.out_path) if os.path.isfile(os.path.join(args.out_path, name)) ]) # percent = math.floor(float(totalfiles/thetotal)) print("{}/{} TOTAL FILES".format(count, thetotal)) count += 1 print("{}: Wrote stylized output image to {}".format(count, out_f))
def main(): start = time.time() session_conf = tf.ConfigProto( allow_soft_placement=args.allow_soft_placement, log_device_placement=args.log_device_placement) session_conf.gpu_options.per_process_gpu_memory_fraction = args.gpu_fraction with tf.Graph().as_default(): # with tf.Session(config=session_conf) as sess: # Load the WCT model wct_model = WCT(checkpoints=args.checkpoints, relu_targets=args.relu_targets, vgg_path=args.vgg_path, device=args.device, ss_patch_size=args.ss_patch_size, ss_stride=args.ss_stride) with wct_model.sess as sess: # 训练的时候不需要style_img,所以在inference的时候重新保存一次checkpoint saver = tf.train.Saver() log_path = args.log_path if args.log_path is not None else os.path.join( args.checkpoint, 'log') summary_writer = tf.summary.FileWriter(log_path, sess.graph) # 部分node没有保存在checkpoint中,需要重新初始化 # sess.run(tf.global_variables_initializer()) # Get content & style full paths if os.path.isdir(args.content_path): content_files = get_files(args.content_path) else: # Single image file content_files = [args.content_path] if os.path.isdir(args.style_path): style_files = get_files(args.style_path) if args.random > 0: style_files = np.random.choice(style_files, args.random) else: # Single image file style_files = [args.style_path] os.makedirs(args.out_path, exist_ok=True) count = 0 # Apply each style to each content image for content_fullpath in content_files: content_prefix, content_ext = os.path.splitext( content_fullpath) content_prefix = os.path.basename( content_prefix) # Extract filename prefix without ext content_img = get_img(content_fullpath) if args.content_size > 0: content_img = resize_to(content_img, args.content_size) for style_fullpath in style_files: style_prefix, _ = os.path.splitext(style_fullpath) style_prefix = os.path.basename( style_prefix) # Extract filename prefix without ext # style_img = get_img_crop(style_fullpath, resize=args.style_size, crop=args.crop_size) # style_img = resize_to(get_img(style_fullpath), content_img.shape[0]) style_img = get_img(style_fullpath) if args.style_size > 0: style_img = resize_to(style_img, args.style_size) if args.crop_size > 0: style_img = center_crop(style_img, args.crop_size) if args.keep_colors: style_img = preserve_colors_np(style_img, content_img) # if args.noise: # Generate textures from noise instead of images # frame_resize = np.random.randint(0, 256, frame_resize.shape, np.uint8) # frame_resize = gaussian_filter(frame_resize, sigma=0.5) # Run the frame through the style network stylized_rgb = wct_model.predict(content_img, style_img, args.alpha, args.swap5, args.ss_alpha, args.adain) if args.passes > 1: for _ in range(args.passes - 1): stylized_rgb = wct_model.predict( stylized_rgb, style_img, args.alpha, args.swap5, args.ss_alpha, args.adain) save_path = saver.save( sess, os.path.join(args.checkpoint, 'model.ckpt')) print("Model saved in file: %s" % save_path) # Stitch the style + stylized output together, but only if there's one style image if args.concat: # Resize style img to same height as frame style_img_resized = scipy.misc.imresize( style_img, (stylized_rgb.shape[0], stylized_rgb.shape[0])) # margin = np.ones((style_img_resized.shape[0], 10, 3)) * 255 stylized_rgb = np.hstack( [style_img_resized, stylized_rgb]) # Format for out filename: {out_path}/{content_prefix}_{style_prefix}.{content_ext} out_f = os.path.join( args.out_path, '{}_{}{}'.format(content_prefix, style_prefix, content_ext)) # out_f = f'{content_prefix}_{style_prefix}.{content_ext}' # print(stylized_rgb, stylized_rgb.shape, type(stylized_rgb)) # print(out_f) save_img(out_f, stylized_rgb) count += 1 print("{}: Wrote stylized output image to {} at {}".format( count, out_f, time.time())) print("breaking...") break break print("Finished stylizing {} outputs in {}s".format( count, time.time() - start))
def main(): start = time.time() # Load the WCT model wct_model = WCT(input_checkpoint=args.input_checkpoint, relu_targets=args.relu_targets, vgg_path=args.vgg_path, device=args.device, ss_patch_size=args.ss_patch_size, ss_stride=args.ss_stride) print("model construct end !") # Get content & style full paths if os.path.isdir(args.content_path): content_files = get_files(args.content_path) else: # Single image file content_files = [args.content_path] content_seg_files = list(map(lambda x: x.replace("2017", "2017_seg").replace("jpg", "png"), content_files)) assert reduce(lambda a, b : a + b, map(lambda x: int(os.path.exists(x)),content_seg_files + content_files)) > 0 if os.path.isdir(args.style_path): style_files = get_files(args.style_path) if args.random > 0: style_files = np.random.choice(style_files, args.random) else: # Single image file style_files = [args.style_path] style_seg_files = list(map(lambda x: x.replace("2017", "2017_seg").replace("jpg", "png"), style_files)) assert reduce(lambda a, b : a + b, map(lambda x: int(os.path.exists(x)),style_seg_files + style_files)) > 0 os.makedirs(args.out_path, exist_ok=True) count = 0 ### Apply each style to each content image for i in range(len(content_files)): content_fullpath = content_files[i] content_prefix, content_ext = os.path.splitext(content_fullpath) content_prefix = os.path.basename(content_prefix) # Extract filename prefix without ext content_img = get_img(content_fullpath) if args.content_size > 0: content_img = np.asarray(Image.fromarray(content_img.astype(np.uint8)).resize((args.content_size, args.content_size))).astype(np.float32) content_seg = get_img_ori(content_seg_files[i]) if args.content_size > 0: content_seg = np.asarray(Image.fromarray(content_seg.astype(np.uint8)).resize((args.content_size, args.content_size))).astype(np.float32) content_seg = image_to_pixel_map(content_seg)[...,np.newaxis] content_img = np.concatenate([content_img, content_seg], axis=-1) for i in range(len(style_files)): style_fullpath = style_files[i] style_prefix, _ = os.path.splitext(style_fullpath) style_prefix = os.path.basename(style_prefix) # Extract filename prefix without ext style_img = get_img(style_fullpath) style_seg = get_img_ori(style_seg_files[i]) if args.style_size > 0: style_img = resize_to(style_img, args.style_size) style_seg = resize_to(style_seg, args.style_size) if args.crop_size > 0: style_img = center_crop(style_img, args.crop_size) style_seg = center_crop(style_seg, args.crop_size) style_seg = image_to_pixel_map(style_seg)[...,np.newaxis] style_img = np.concatenate([style_img, style_seg], axis=-1) assert not args.keep_colors if args.keep_colors: style_img = preserve_colors_np(style_img, content_img) # if args.noise: # Generate textures from noise instead of images # frame_resize = np.random.randint(0, 256, frame_resize.shape, np.uint8) # frame_resize = gaussian_filter(frame_resize, sigma=0.5) # Run the frame through the style network stylized_rgb = wct_model.predict(content_img, style_img, args.alpha, args.swap5, args.ss_alpha, args.adain) if args.passes > 1: for _ in range(args.passes-1): stylized_rgb = np.concatenate([stylized_rgb ,content_seg], axis=-1) stylized_rgb = wct_model.predict(stylized_rgb, style_img, args.alpha, args.swap5, args.ss_alpha, args.adain) # Stitch the style + stylized output together, but only if there's one style image assert not args.concat if args.concat: # Resize style img to same height as frame style_img_resized = scipy.misc.imresize(style_img, (stylized_rgb.shape[0], stylized_rgb.shape[0])) # margin = np.ones((style_img_resized.shape[0], 10, 3)) * 255 stylized_rgb = np.hstack([style_img_resized, stylized_rgb]) ####+++++++++++++++++++++++++++++++++++ # Format for out filename: {out_path}/{content_prefix}_{style_prefix}.{content_ext} out_f = os.path.join(args.out_path, '{}_{}{}'.format(content_prefix, style_prefix, content_ext)) save_img(out_f, stylized_rgb) count += 1 print("{}: Wrote stylized output image to {}".format(count, out_f)) print("Finished stylizing {} outputs in {}s".format(count, time.time() - start))
def main(): # Load the WCT model wct_model = WCT(checkpoints=args.checkpoints, relu_targets=args.relu_targets, vgg_path=args.vgg_path, device=args.device, ss_patch_size=args.ss_patch_size, ss_stride=args.ss_stride) # Create needed dirs in_dir = os.path.join(args.tmp_dir, 'input') out_dir = os.path.join(args.tmp_dir, 'sytlized') if not os.path.exists(in_dir): os.makedirs(in_dir) if not os.path.exists(out_dir): os.makedirs(out_dir) if os.path.isdir(args.in_path): in_path = get_files(args.in_path) else: # Single image file in_path = [args.in_path] if os.path.isdir(args.style_path): style_files = get_files(args.style_path) else: # Single image file style_files = [args.style_path] print(style_files) # time.sleep(999) in_args = [ 'ffmpeg', '-i', args.in_path, '%s/frame_%%d.png' % in_dir ] subprocess.call(" ".join(in_args), shell=True) base_names = os.listdir(in_dir) in_files = [os.path.join(in_dir, x) for x in base_names] out_files = [os.path.join(out_dir, x) for x in base_names] s = time.time() for content_fullpath in in_path: content_prefix, content_ext = os.path.splitext(content_fullpath) content_prefix = os.path.basename(content_prefix) try: for style_fullpath in style_files: style_img = get_img(style_fullpath) if args.style_size > 0: style_img = resize_to(style_img, args.style_size) if args.crop_size > 0: style_img = center_crop(style_img, args.crop_size) style_prefix, _ = os.path.splitext(style_fullpath) style_prefix = os.path.basename(style_prefix) # print("ARRAY: ", style_img) out_v = os.path.join(args.out_path, '{}_{}{}'.format(content_prefix, style_prefix, content_ext)) print("OUT:", out_v) if os.path.isfile(out_v): print("SKIP", out_v) continue for in_f, out_f in zip(in_files, out_files): print('{} -> {}'.format(in_f, out_f)) content_img = get_img(in_f) if args.keep_colors: style_rgb = preserve_colors_np(style_img, content_img) else: style_rgb = style_img stylized = wct_model.predict(content_img, style_rgb, args.alpha, args.swap5, args.ss_alpha) if args.passes > 1: for _ in range(args.passes-1): stylized = wct_model.predict(stylized, style_rgb, args.alpha) # Stitch the style + stylized output together, but only if there's one style image if args.concat: # Resize style img to same height as frame style_img_resized = scipy.misc.imresize(style_rgb, (stylized.shape[0], stylized.shape[0])) stylized = np.hstack([style_img_resized, stylized]) save_img(out_f, stylized) fr = 30 out_args = [ 'ffmpeg', '-i', '%s/frame_%%d.png' % out_dir, '-f', 'mp4', '-q:v', '0', '-vcodec', 'mpeg4', '-r', str(fr), '"' + out_v + '"' ] print(out_args) subprocess.call(" ".join(out_args), shell=True) print('Video at: %s' % out_v) if args.keep_tmp is True or len(style_files) > 1: continue else: shutil.rmtree(args.tmp_dir) print('Processed in:', (time.time() - s)) print('Processed in:', (time.time() - s)) except Exception as e: print("EXCEPTION: ", e)
def main(): # Load the WCT model wct_model = WCT(checkpoints=args.checkpoints, relu_targets=args.relu_targets, vgg_path=args.vgg_path, device=args.device, ss_patch_size=args.ss_patch_size, ss_stride=args.ss_stride) # Load a panel to control style settings style_window = StyleWindow(args.style_path, args.style_size, args.crop_size, args.scale, args.alpha, args.swap5, args.ss_alpha, args.passes) # Start the webcam stream cap = WebcamVideoStream(args.video_source, args.width, args.height).start() _, frame = cap.read() # Grab a sample frame to calculate frame size frame_resize = cv2.resize(frame, None, fx=args.scale, fy=args.scale) img_shape = frame_resize.shape # Setup video out writer if args.video_out is not None: fourcc = cv2.VideoWriter_fourcc(*'XVID') if args.concat: out_shape = (img_shape[1]+img_shape[0],img_shape[0]) # Make room for the style img else: out_shape = (img_shape[1],img_shape[0]) print('Video Out Shape:', out_shape) video_writer = cv2.VideoWriter(args.video_out, fourcc, args.fps, out_shape) fps = FPS().start() # Track FPS processing speed # Toggles changed with kb shortcuts keep_colors = args.keep_colors swap_style = args.swap5 use_adain = args.adain count = 0 while(True): ret, frame = cap.read() if ret is True: frame_resize = cv2.resize(frame, None, fx=style_window.scale, fy=style_window.scale) if args.noise: # Generate textures from noise instead of images frame_resize = np.random.randint(0, 256, frame_resize.shape, np.uint8) count += 1 print("Frame:",count,"Orig shape:",frame.shape,"New shape",frame_resize.shape) content_rgb = cv2.cvtColor(frame_resize, cv2.COLOR_BGR2RGB) # OpenCV uses BGR, we need RGB if args.random > 0 and count % args.random == 0: style_window.set_style(random=True) if keep_colors: style_rgb = preserve_colors_np(style_window.style_rgb, content_rgb) else: style_rgb = style_window.style_rgb # Run the frame through the style network stylized_rgb = wct_model.predict(content_rgb, style_rgb, style_window.alpha, swap_style, style_window.ss_alpha, use_adain) # Repeat stylization pipeline if style_window.passes > 1: for i in range(style_window.passes-1): stylized_rgb = wct_model.predict(stylized_rgb, style_rgb, style_window.alpha, swap_style, style_window.ss_alpha, use_adain) # Stitch the style + stylized output together if args.concat: # Resize style img to same height as frame style_rgb_resized = cv2.resize(style_rgb, (stylized_rgb.shape[0], stylized_rgb.shape[0])) stylized_rgb = np.hstack([style_rgb_resized, stylized_rgb]) stylized_bgr = cv2.cvtColor(stylized_rgb, cv2.COLOR_RGB2BGR) if args.video_out is not None: stylized_bgr = cv2.resize(stylized_bgr, out_shape) # Make sure frame matches video size video_writer.write(stylized_bgr) cv2.imshow('WCT Universal Style Transfer', stylized_bgr) fps.update() key = cv2.waitKey(10) if key & 0xFF == ord('r'): # Load new random style style_window.set_style(random=True) elif key & 0xFF == ord('c'): # Toggle color preservation keep_colors = not keep_colors print('Switching to keep_colors:',keep_colors) elif key & 0xFF == ord('s'): # Toggle style swap swap_style = not swap_style print('New value for flag swap_style:',swap_style) elif key & 0xFF == ord('a'): # Toggle AdaIN use_adain = not use_adain print('New value for flag use_adain:',use_adain) elif key & 0xFF == ord('w'): # Write stylized frame out_f = "{}.png".format(time.time()) save_img(out_f, stylized_rgb) print('Saved image to:',out_f) elif key & 0xFF == ord('q'): # Quit gracefully break else: break fps.stop() print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) cap.stop() if args.video_out is not None: video_writer.release() cv2.destroyAllWindows()