Spaces:
Running
on
Zero
Running
on
Zero
Process examples when loaded
Browse files
app.py
CHANGED
@@ -297,23 +297,11 @@ class ImageConductor:
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guidance_scale,
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num_inference_steps,
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personalized,
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examples_type,
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):
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print("Run!")
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if examples_type != "":
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### for adapting high version gradio
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tracking_points = gr.State([])
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first_frame_path = IMAGE_PATH[examples_type]
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points = json.load(open(POINTS[examples_type]))
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tracking_points.value.extend(points)
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print("example first_frame_path", first_frame_path)
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print("example tracking_points", tracking_points.value)
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original_width, original_height = 384, 256
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input_all_points = tracking_points
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else:
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input_all_points = tracking_points.value
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print("input_all_points", input_all_points)
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resized_all_points = [
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@@ -415,7 +403,7 @@ class ImageConductor:
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# outputs_path = os.path.join(output_dir, f'output_{i}_{id}.gif')
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# save_videos_grid(sample[0][None], outputs_path)
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print("Done!")
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return
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def reset_states(first_frame_path, tracking_points):
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@@ -487,6 +475,54 @@ def add_tracking_points(
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return {tracking_points_var: tracking_points, input_image: trajectory_map}
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def add_drag(tracking_points):
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if not tracking_points or tracking_points[-1]:
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tracking_points.append([])
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@@ -571,6 +607,15 @@ def delete_last_step(tracking_points, first_frame_path, drag_mode):
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return {tracking_points_var: tracking_points, input_image: trajectory_map}
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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ImageConductor_net = ImageConductor(
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device=device,
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@@ -725,9 +770,16 @@ with block:
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guidance_scale,
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num_inference_steps,
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personalized,
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examples_type,
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],
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[output_image, output_video],
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)
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block.queue().launch()
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guidance_scale,
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num_inference_steps,
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personalized,
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):
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print("Run!")
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original_width, original_height = 384, 256
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input_all_points = tracking_points
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print("input_all_points", input_all_points)
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resized_all_points = [
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# outputs_path = os.path.join(output_dir, f'output_{i}_{id}.gif')
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# save_videos_grid(sample[0][None], outputs_path)
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print("Done!")
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return visualized_drag, outputs_path
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def reset_states(first_frame_path, tracking_points):
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return {tracking_points_var: tracking_points, input_image: trajectory_map}
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def preprocess_example_image(image_path, tracking_points, drag_mode):
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image_pil = image2pil(image_path)
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raw_w, raw_h = image_pil.size
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resize_ratio = max(384 / raw_w, 256 / raw_h)
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image_pil = image_pil.resize((int(raw_w * resize_ratio), int(raw_h * resize_ratio)), Image.BILINEAR)
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image_pil = transforms.CenterCrop((256, 384))(image_pil.convert("RGB"))
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id = str(uuid.uuid4())[:4]
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first_frame_path = os.path.join(output_dir, f"first_frame_{id}.jpg")
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image_pil.save(first_frame_path, quality=95)
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if drag_mode == "object":
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color = (255, 0, 0, 255)
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elif drag_mode == "camera":
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color = (0, 0, 255, 255)
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transparent_background = Image.open(first_frame_path).convert("RGBA")
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w, h = transparent_background.size
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transparent_layer = np.zeros((h, w, 4))
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for track in tracking_points:
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if len(track) > 1:
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for i in range(len(track) - 1):
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start_point = track[i]
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end_point = track[i + 1]
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vx = end_point[0] - start_point[0]
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vy = end_point[1] - start_point[1]
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arrow_length = np.sqrt(vx**2 + vy**2)
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if i == len(track) - 2:
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cv2.arrowedLine(
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transparent_layer, tuple(start_point), tuple(end_point), color, 2, tipLength=8 / arrow_length
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)
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else:
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cv2.line(
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transparent_layer,
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tuple(start_point),
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tuple(end_point),
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color,
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2,
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)
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else:
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cv2.circle(transparent_layer, tuple(track[0]), 5, color, -1)
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transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8))
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trajectory_map = Image.alpha_composite(transparent_background, transparent_layer)
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return trajectory_map, first_frame_path
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def add_drag(tracking_points):
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if not tracking_points or tracking_points[-1]:
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tracking_points.append([])
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return {tracking_points_var: tracking_points, input_image: trajectory_map}
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def load_example(drag_mode, examples_type):
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example_image_path = IMAGE_PATH[examples_type]
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with open(POINTS[examples_type]) as f:
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tracking_points = json.load(f)
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tracking_points = np.round(tracking_points).astype(int).tolist()
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trajectory_map, first_frame_path = preprocess_example_image(example_image_path, tracking_points, drag_mode)
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return {input_image: trajectory_map, first_frame_path_var: first_frame_path, tracking_points_var: tracking_points}
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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ImageConductor_net = ImageConductor(
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device=device,
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guidance_scale,
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num_inference_steps,
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personalized,
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],
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[output_image, output_video],
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)
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examples_type.change(
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fn=load_example,
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inputs=[drag_mode, examples_type],
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outputs=[input_image, first_frame_path_var, tracking_points_var],
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api_name=False,
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queue=False,
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)
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block.queue().launch()
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