refined gradio

app copy.py

@@ -0,0 +1,740 @@
+try:
+    import spaces
+except:
+    pass
+
+import os
+import gradio as gr
+
+import torch
+from gradio_image_prompter import ImagePrompter
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+from omegaconf import OmegaConf
+from PIL import Image
+import numpy as np
+from copy import deepcopy
+import cv2
+
+import torch.nn.functional as F
+import torchvision
+from einops import rearrange
+import tempfile
+
+from objctrl_2_5d.utils.ui_utils import process_image, get_camera_pose, get_subject_points, get_points, undo_points, mask_image
+from ZoeDepth.zoedepth.utils.misc import colorize
+
+from cameractrl.inference import get_pipeline
+from objctrl_2_5d.utils.examples import examples, sync_points
+
+from objctrl_2_5d.utils.objmask_util import RT2Plucker, Unprojected, roll_with_ignore_multidim, dilate_mask_pytorch
+from objctrl_2_5d.utils.filter_utils import get_freq_filter, freq_mix_3d
+
+
+### Title and Description ###
+#### Description ####
+title = r"""<h1 align="center">ObjCtrl-2.5D: Training-free Object Control with Camera Poses</h1>"""
+# subtitle = r"""<h2 align="center">Deployed on SVD Generation</h2>"""
+important_link = r"""
+<div align='center'>
+ <a href='https://wzhouxiff.github.io/projects/MotionCtrl/assets/paper/MotionCtrl.pdf'>[Paper]</a>
+&ensp; <a href='https://wzhouxiff.github.io/projects/MotionCtrl/'>[Project Page]</a>
+&ensp; <a href='https://github.com/TencentARC/MotionCtrl'>[Code]</a>
+</div>
+"""
+
+authors = r"""
+<div align='center'>
+ <a href='https://wzhouxiff.github.io/'>Zhouxia Wang</a>
+&ensp; <a href='https://nirvanalan.github.io/'>Yushi Lan</a>
+&ensp; <a href='https://shangchenzhou.com/'>Shanchen Zhou</a>
+&ensp; <a href='https://www.mmlab-ntu.com/person/ccloy/index.html'>Chen Change Loy</a>
+</div>
+"""
+
+affiliation = r"""
+<div align='center'>
+ <a href='https://www.mmlab-ntu.com/'>S-Lab, NTU Singapore</a>
+</div>
+"""
+
+description = r"""
+<b>Official Gradio demo</b> for <a href='https://github.com/TencentARC/MotionCtrl' target='_blank'><b>ObjCtrl-2.5D: Training-free Object Control with Camera Poses</b></a>.<br>
+🔥 ObjCtrl2.5D enables object motion control in a I2V generated video via transforming 2D trajectories to 3D using depth, subsequently converting them into camera poses, 
+thereby leveraging the exisitng camera motion control module for object motion control without requiring additional training.<br>
+"""
+
+article = r"""
+If ObjCtrl2.5D is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/MotionCtrl' target='_blank'>Github Repo</a>. Thanks! 
+[![GitHub Stars](https://img.shields.io/github/stars/TencentARC%2FMotionCtrl
+)](https://github.com/TencentARC/MotionCtrl)
+
+---
+
+📝 **Citation**
+<br>
+If our work is useful for your research, please consider citing:
+```bibtex
+@inproceedings{wang2024motionctrl,
+  title={Motionctrl: A unified and flexible motion controller for video generation},
+  author={Wang, Zhouxia and Yuan, Ziyang and Wang, Xintao and Li, Yaowei and Chen, Tianshui and Xia, Menghan and Luo, Ping and Shan, Ying},
+  booktitle={ACM SIGGRAPH 2024 Conference Papers},
+  pages={1--11},
+  year={2024}
+}
+```
+
+📧 **Contact**
+<br>
+If you have any questions, please feel free to reach me out at <b>zhouzi1212@gmail.com</b>.
+
+"""
+
+# -------------- initialization --------------
+
+CAMERA_MODE = ["Traj2Cam", "Rotate", "Clockwise", "Translate"]
+
+# select the device for computation
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif torch.backends.mps.is_available():
+    device = torch.device("mps")
+else:
+    device = torch.device("cpu")
+print(f"using device: {device}")
+
+# segmentation model
+segmentor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-tiny", cache_dir="ckpt", device=device)
+
+# depth model
+d_model_NK = torch.hub.load('./ZoeDepth', 'ZoeD_NK', source='local', pretrained=True).to(device)
+
+# cameractrl model
+config = "configs/svd_320_576_cameractrl.yaml"
+model_id = "stabilityai/stable-video-diffusion-img2vid"
+ckpt = "checkpoints/CameraCtrl_svd.ckpt"
+if not os.path.exists(ckpt):
+    os.makedirs("checkpoints", exist_ok=True)
+    os.system("wget -c https://huggingface.co/hehao13/CameraCtrl_SVD_ckpts/resolve/main/CameraCtrl_svd.ckpt?download=true")
+    os.system("mv CameraCtrl_svd.ckpt?download=true checkpoints/CameraCtrl_svd.ckpt")
+model_config = OmegaConf.load(config)
+
+
+pipeline = get_pipeline(model_id, "unet", model_config['down_block_types'], model_config['up_block_types'],
+                        model_config['pose_encoder_kwargs'], model_config['attention_processor_kwargs'],
+                        ckpt, True, device)
+
+# segmentor = None
+# d_model_NK = None
+# pipeline = None
+
+### run the demo ##
+# @spaces.GPU(duration=5)
+def segment(canvas, image, logits):
+    if logits is not None:
+        logits *=  32.0
+    _, points = get_subject_points(canvas)
+    image = np.array(image)
+
+    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
+        segmentor.set_image(image)
+        input_points = []
+        input_boxes = []
+        for p in points:
+            [x1, y1, _, x2, y2, _] = p
+            if x2==0 and y2==0:
+                input_points.append([x1, y1])
+            else:
+                input_boxes.append([x1, y1, x2, y2])
+        if len(input_points) == 0:
+            input_points = None
+            input_labels = None
+        else:
+            input_points = np.array(input_points)
+            input_labels = np.ones(len(input_points))
+        if len(input_boxes) == 0:
+            input_boxes = None
+        else:
+            input_boxes = np.array(input_boxes)
+        masks, _, logits = segmentor.predict(
+            point_coords=input_points,
+            point_labels=input_labels,
+            box=input_boxes,
+            multimask_output=False,
+            return_logits=True,
+            mask_input=logits,
+        )
+        mask = masks > 0
+        masked_img = mask_image(image, mask[0], color=[252, 140, 90], alpha=0.9)
+        masked_img = Image.fromarray(masked_img)
+        
+    return mask[0], masked_img, masked_img, logits / 32.0
+
+# @spaces.GPU(duration=5)
+def get_depth(image, points):
+    
+    depth = d_model_NK.infer_pil(image)    
+    colored_depth = colorize(depth, cmap='gray_r') # [h, w, 4] 0-255
+    
+    depth_img = deepcopy(colored_depth[:, :, :3])
+    if len(points) > 0:
+        for idx, point in enumerate(points):
+            if idx % 2 == 0:
+                cv2.circle(depth_img, tuple(point), 10, (255, 0, 0), -1)
+            else:
+                cv2.circle(depth_img, tuple(point), 10, (0, 0, 255), -1)
+            if idx > 0:
+                cv2.arrowedLine(depth_img, points[idx-1], points[idx], (255, 255, 255), 4, tipLength=0.5)
+    
+    return depth, depth_img, colored_depth[:, :, :3]
+
+
+# @spaces.GPU(duration=80)
+def run_objctrl_2_5d(condition_image, 
+                        mask, 
+                        depth, 
+                        RTs, 
+                        bg_mode, 
+                        shared_wapring_latents, 
+                        scale_wise_masks, 
+                        rescale, 
+                        seed, 
+                        ds, dt, 
+                        num_inference_steps=25):
+    
+    DEBUG = False
+
+    if DEBUG:
+        cur_OUTPUT_PATH = 'outputs/tmp'
+        os.makedirs(cur_OUTPUT_PATH, exist_ok=True)
+
+    # num_inference_steps=25
+    min_guidance_scale = 1.0
+    max_guidance_scale = 3.0
+
+    area_ratio = 0.3
+    depth_scale_ = 5.2
+    center_margin = 10
+
+    height, width = 320, 576
+    num_frames = 14
+
+    intrinsics = np.array([[float(width), float(width), float(width) / 2, float(height) / 2]])
+    intrinsics = np.repeat(intrinsics, num_frames, axis=0) # [n_frame, 4]
+    fx = intrinsics[0, 0] / width
+    fy = intrinsics[0, 1] / height
+    cx = intrinsics[0, 2] / width
+    cy = intrinsics[0, 3] / height
+
+    down_scale = 8
+    H, W = height // down_scale, width // down_scale
+    K = np.array([[width / down_scale, 0, W / 2], [0, width / down_scale, H / 2], [0, 0, 1]])
+    
+    seed = int(seed)
+            
+    center_h_margin, center_w_margin = center_margin, center_margin
+    depth_center = np.mean(depth[height//2-center_h_margin:height//2+center_h_margin, width//2-center_w_margin:width//2+center_w_margin])
+    
+    if rescale > 0:
+        depth_rescale = round(depth_scale_ * rescale / depth_center, 2)
+    else:
+        depth_rescale = 1.0
+        
+    depth = depth * depth_rescale
+    
+    depth_down = F.interpolate(torch.tensor(depth).unsqueeze(0).unsqueeze(0), 
+                                (H, W), mode='bilinear', align_corners=False).squeeze().numpy() # [H, W]
+    
+    ## latent
+    generator = torch.Generator()
+    generator.manual_seed(seed)
+    
+    latents_org = pipeline.prepare_latents(
+            1,
+            14,
+            8,
+            height,
+            width,
+            pipeline.dtype,
+            device,
+            generator,
+            None,
+        )
+    latents_org = latents_org / pipeline.scheduler.init_noise_sigma
+    
+    cur_plucker_embedding, _, _ = RT2Plucker(RTs, RTs.shape[0], (height, width), fx, fy, cx, cy) # 6, V, H, W
+    cur_plucker_embedding = cur_plucker_embedding.to(device)
+    cur_plucker_embedding = cur_plucker_embedding[None, ...] # b 6 f h w
+    cur_plucker_embedding = cur_plucker_embedding.permute(0, 2, 1, 3, 4) # b f 6 h w
+    cur_plucker_embedding = cur_plucker_embedding[:, :num_frames, ...]
+    cur_pose_features = pipeline.pose_encoder(cur_plucker_embedding)
+    
+    # bg_mode = ["Fixed", "Reverse", "Free"]
+    if bg_mode == "Fixed":
+        fix_RTs = np.repeat(RTs[0][None, ...], num_frames, axis=0) # [n_frame, 4, 3]
+        fix_plucker_embedding, _, _ = RT2Plucker(fix_RTs, num_frames, (height, width), fx, fy, cx, cy) # 6, V, H, W
+        fix_plucker_embedding = fix_plucker_embedding.to(device)
+        fix_plucker_embedding = fix_plucker_embedding[None, ...] # b 6 f h w
+        fix_plucker_embedding = fix_plucker_embedding.permute(0, 2, 1, 3, 4) # b f 6 h w
+        fix_plucker_embedding = fix_plucker_embedding[:, :num_frames, ...]
+        fix_pose_features = pipeline.pose_encoder(fix_plucker_embedding)
+        
+    elif bg_mode == "Reverse":
+        bg_plucker_embedding, _, _ = RT2Plucker(RTs[::-1], RTs.shape[0], (height, width), fx, fy, cx, cy) # 6, V, H, W
+        bg_plucker_embedding = bg_plucker_embedding.to(device)
+        bg_plucker_embedding = bg_plucker_embedding[None, ...] # b 6 f h w
+        bg_plucker_embedding = bg_plucker_embedding.permute(0, 2, 1, 3, 4) # b f 6 h w
+        bg_plucker_embedding = bg_plucker_embedding[:, :num_frames, ...]
+        fix_pose_features = pipeline.pose_encoder(bg_plucker_embedding)
+        
+    else:
+        fix_pose_features = None
+        
+    #### preparing mask
+    
+    mask = Image.fromarray(mask)
+    mask = mask.resize((W, H))
+    mask = np.array(mask).astype(np.float32)
+    mask = np.expand_dims(mask, axis=-1)
+    
+    # visulize mask
+    if DEBUG:
+        mask_sum_vis = mask[..., 0]
+        mask_sum_vis = (mask_sum_vis * 255.0).astype(np.uint8)
+        mask_sum_vis = Image.fromarray(mask_sum_vis)
+        
+        mask_sum_vis.save(f'{cur_OUTPUT_PATH}/org_mask.png')
+    
+    try:
+        warped_masks = Unprojected(mask, depth_down, RTs, H=H, W=W, K=K)
+    
+        warped_masks.insert(0, mask)
+            
+    except:
+        # mask to bbox
+        print(f'!!! Mask is too small to warp; mask to bbox') 
+        mask = mask[:, :, 0]
+        coords = cv2.findNonZero(mask)
+        x, y, w, h = cv2.boundingRect(coords)
+        # mask[y:y+h, x:x+w] = 1.0
+        
+        center_x, center_y = x + w // 2, y + h // 2
+        center_z = depth_down[center_y, center_x]
+        
+        # RTs [n_frame, 3, 4] to [n_frame, 4, 4] , add [0, 0, 0, 1]
+        RTs = np.concatenate([RTs, np.array([[[0, 0, 0, 1]]] * num_frames)], axis=1)
+        
+        # RTs: world to camera
+        P0 = np.array([center_x, center_y, 1])
+        Pc0 = np.linalg.inv(K) @ P0 * center_z
+        pw = np.linalg.inv(RTs[0]) @ np.array([Pc0[0], Pc0[1], center_z, 1]) # [4]
+        
+        P = [np.array([center_x, center_y])]
+        for i in range(1, num_frames):
+            Pci = RTs[i] @ pw
+            Pi = K @ Pci[:3] / Pci[2]
+            P.append(Pi[:2])
+        
+        warped_masks = [mask]
+        for i in range(1, num_frames):
+            shift_x = int(round(P[i][0] - P[0][0]))
+            shift_y = int(round(P[i][1] - P[0][1]))
+
+            cur_mask = roll_with_ignore_multidim(mask, [shift_y, shift_x])
+            warped_masks.append(cur_mask)
+            
+            
+        warped_masks = [v[..., None] for v in warped_masks]
+            
+    warped_masks = np.stack(warped_masks, axis=0) # [f, h, w]
+    warped_masks = np.repeat(warped_masks, 3, axis=-1) # [f, h, w, 3]
+    
+    mask_sum = np.sum(warped_masks, axis=0, keepdims=True)  # [1, H, W, 3]
+    mask_sum[mask_sum > 1.0] = 1.0
+    mask_sum = mask_sum[0,:,:, 0]
+    
+    if DEBUG:
+        ## visulize warp mask    
+        warp_masks_vis = torch.tensor(warped_masks)
+        warp_masks_vis = (warp_masks_vis * 255.0).to(torch.uint8)
+        torchvision.io.write_video(f'{cur_OUTPUT_PATH}/warped_masks.mp4', warp_masks_vis, fps=10, video_codec='h264', options={'crf': '10'})
+        
+        # visulize mask
+        mask_sum_vis = mask_sum
+        mask_sum_vis = (mask_sum_vis * 255.0).astype(np.uint8)
+        mask_sum_vis = Image.fromarray(mask_sum_vis)
+        
+        mask_sum_vis.save(f'{cur_OUTPUT_PATH}/merged_mask.png')
+        
+    if scale_wise_masks:
+        min_area = H * W * area_ratio # cal in downscale
+        non_zero_len = mask_sum.sum() 
+        
+        print(f'non_zero_len: {non_zero_len}, min_area: {min_area}')
+        
+        if non_zero_len > min_area:
+            kernel_sizes = [1, 1, 1, 3]
+        elif non_zero_len > min_area * 0.5:
+            kernel_sizes = [3, 1, 1, 5]
+        else:
+            kernel_sizes = [5, 3, 3, 7]
+    else:
+        kernel_sizes = [1, 1, 1, 1]
+        
+    mask = torch.from_numpy(mask_sum) # [h, w]
+    mask = mask[None, None, ...] # [1, 1, h, w]
+    mask = F.interpolate(mask, (height, width), mode='bilinear', align_corners=False) # [1, 1, H, W]
+    # mask = mask.repeat(1, num_frames, 1, 1) # [1, f, H, W]
+    mask = mask.to(pipeline.dtype).to(device)
+    
+    ##### Mask End ######
+    
+    ### Got blending pose features Start ###
+
+    pose_features = []
+    for i in range(0, len(cur_pose_features)):
+        kernel_size = kernel_sizes[i]
+        h, w = cur_pose_features[i].shape[-2:]
+        
+        if fix_pose_features is None:
+            pose_features.append(torch.zeros_like(cur_pose_features[i]))
+        else:
+            pose_features.append(fix_pose_features[i])
+            
+        cur_mask = F.interpolate(mask, (h, w), mode='bilinear', align_corners=False)
+        cur_mask = dilate_mask_pytorch(cur_mask, kernel_size=kernel_size) # [1, 1, H, W]
+        cur_mask = cur_mask.repeat(1, num_frames, 1, 1) # [1, f, H, W]
+        
+        if DEBUG:
+            # visulize mask
+            mask_vis = cur_mask[0, 0].cpu().numpy() * 255.0
+            mask_vis = Image.fromarray(mask_vis.astype(np.uint8))
+            mask_vis.save(f'{cur_OUTPUT_PATH}/mask_k{kernel_size}_scale{i}.png')
+            
+        cur_mask = cur_mask[None, ...] # [1, 1, f, H, W]
+        pose_features[-1] = cur_pose_features[i] * cur_mask + pose_features[-1] * (1 - cur_mask)
+
+    ### Got blending pose features End ###
+    
+    ##### Warp Noise Start ######
+    
+    if shared_wapring_latents:
+        noise = latents_org[0, 0].data.cpu().numpy().copy() #[14, 4, 40, 72]
+        noise = np.transpose(noise, (1, 2, 0)) # [40, 72, 4]
+
+        try:
+            warp_noise = Unprojected(noise, depth_down, RTs, H=H, W=W, K=K)
+            warp_noise.insert(0, noise)
+        except:
+            print(f'!!! Noise is too small to warp; mask to bbox')
+            
+            warp_noise = [noise]
+            for i in range(1, num_frames):
+                shift_x = int(round(P[i][0] - P[0][0]))
+                shift_y = int(round(P[i][1] - P[0][1]))
+                
+                cur_noise= roll_with_ignore_multidim(noise, [shift_y, shift_x])
+                warp_noise.append(cur_noise)
+                
+            warp_noise = np.stack(warp_noise, axis=0) # [f, h, w, 4]
+    
+        if DEBUG:
+            ## visulize warp noise
+            warp_noise_vis = torch.tensor(warp_noise)[..., :3] * torch.tensor(warped_masks)
+            warp_noise_vis = (warp_noise_vis - warp_noise_vis.min()) / (warp_noise_vis.max() - warp_noise_vis.min())
+            warp_noise_vis = (warp_noise_vis * 255.0).to(torch.uint8)
+    
+            torchvision.io.write_video(f'{cur_OUTPUT_PATH}/warp_noise.mp4', warp_noise_vis, fps=10, video_codec='h264', options={'crf': '10'})
+    
+    
+        warp_latents = torch.tensor(warp_noise).permute(0, 3, 1, 2).to(latents_org.device).to(latents_org.dtype) # [frame, 4, H, W]
+        warp_latents = warp_latents.unsqueeze(0) # [1, frame, 4, H, W]
+        
+        warped_masks = torch.tensor(warped_masks).permute(0, 3, 1, 2).unsqueeze(0) # [1, frame, 3, H, W]
+        mask_extend = torch.concat([warped_masks, warped_masks[:,:,0:1]], dim=2) # [1, frame, 4, H, W]
+        mask_extend = mask_extend.to(latents_org.device).to(latents_org.dtype)
+        
+        warp_latents = warp_latents * mask_extend + latents_org * (1 - mask_extend)
+        warp_latents = warp_latents.permute(0, 2, 1, 3, 4)
+        random_noise = latents_org.clone().permute(0, 2, 1, 3, 4)
+            
+        filter_shape = warp_latents.shape
+
+        freq_filter = get_freq_filter(
+            filter_shape, 
+            device = device, 
+            filter_type='butterworth',
+            n=4,
+            d_s=ds,
+            d_t=dt
+        )
+        
+        warp_latents = freq_mix_3d(warp_latents, random_noise, freq_filter)
+        warp_latents = warp_latents.permute(0, 2, 1, 3, 4)
+        
+    else:
+        warp_latents = latents_org.clone()
+        
+    generator.manual_seed(42)
+
+    with torch.no_grad():
+        result = pipeline(
+            image=condition_image,
+            pose_embedding=cur_plucker_embedding,
+            height=height,
+            width=width,
+            num_frames=num_frames,
+            num_inference_steps=num_inference_steps,
+            min_guidance_scale=min_guidance_scale,
+            max_guidance_scale=max_guidance_scale,
+            do_image_process=True,
+            generator=generator,
+            output_type='pt',
+            pose_features= pose_features,
+            latents = warp_latents
+        ).frames[0].cpu() #[f, c, h, w]
+        
+    
+    result = rearrange(result, 'f c h w -> f h w c')
+    result = (result * 255.0).to(torch.uint8)
+
+    video_path = tempfile.NamedTemporaryFile(suffix='.mp4').name
+    torchvision.io.write_video(video_path, result, fps=10, video_codec='h264', options={'crf': '8'})
+    
+    return video_path
+
+# -------------- UI definition --------------
+with gr.Blocks() as demo:
+    # layout definition
+    gr.Markdown(title)
+    gr.Markdown(authors)
+    gr.Markdown(affiliation)
+    gr.Markdown(important_link)
+    gr.Markdown(description)
+    
+    
+    # with gr.Row():
+    #     gr.Markdown("""# <center>Repositioning the Subject within Image </center>""")
+    mask = gr.State(value=None) # store mask
+    removal_mask = gr.State(value=None) # store removal mask
+    selected_points = gr.State([]) # store points
+    selected_points_text = gr.Textbox(label="Selected Points", visible=False)
+    
+    original_image = gr.State(value=None) # store original input image
+    masked_original_image = gr.State(value=None) # store masked input image
+    mask_logits = gr.State(value=None) # store mask logits
+    
+    depth = gr.State(value=None) # store depth
+    org_depth_image = gr.State(value=None) # store original depth image
+    
+    camera_pose = gr.State(value=None) # store camera pose
+    
+    with gr.Column():
+        
+        outlines = """
+        <font size="5"><b>There are total 5 steps to complete the task.</b></font>
+        - Step 1: Input an image and Crop it to a suitable size;
+        - Step 2: Attain the subject mask;
+        - Step 3: Get depth and Draw Trajectory;
+        - Step 4: Get camera pose from trajectory or customize it;
+        - Step 5: Generate the final video.
+        """
+        
+        gr.Markdown(outlines)
+        
+        
+        with gr.Row():
+            with gr.Column():
+                # Step 1: Input Image
+                step1_dec = """
+                    <font size="4"><b>Step 1: Input Image</b></font>
+                    - Select the region using a <mark>bounding box</mark>, aiming for a ratio close to </mark>320:576</mark> (height:width).
+                    - All provided images in `Examples` are in 320 x 576 resolution. Simply press `Process` to proceed.
+                    """
+                step1 = gr.Markdown(step1_dec)
+                raw_input = ImagePrompter(type="pil", label="Raw Image", show_label=True, interactive=True)
+                # left_up_point = gr.Textbox(value = "-1 -1", label="Left Up Point", interactive=True)
+                process_button = gr.Button("Process")
+                
+            with gr.Column():
+                # Step 2: Get Subject Mask
+                step2_dec = """
+                    <font size="4"><b>Step 2: Get Subject Mask</b></font>
+                    - Use the <mark>bounding boxes</mark> or <mark>paints</mark> to select the subject.
+                    - Press `Segment Subject` to get the mask. <mark>Can be refined iteratively by updating points<mark>.
+                    """
+                step2 = gr.Markdown(step2_dec)
+                canvas = ImagePrompter(type="pil", label="Input Image", show_label=True, interactive=True) # for mask painting
+
+                select_button = gr.Button("Segment Subject")
+                
+        with gr.Row():
+            with gr.Column():
+                mask_dec = """
+                    <font size="4"><b>Mask Result</b></font>
+                    - Just for visualization purpose. No need to interact.
+                """
+                mask_vis = gr.Markdown(mask_dec)
+                mask_output = gr.Image(type="pil", label="Mask", show_label=True, interactive=False)
+            with gr.Column():
+                # Step 3: Get Depth and Draw Trajectory
+                step3_dec = """
+                    <font size="4"><b>Step 3: Get Depth and Draw Trajectory</b></font>
+                    - Press `Get Depth` to get the depth image.
+                    - Draw the trajectory by selecting points on the depth image. <mark>No more than 14 points</mark>.
+                    - Press `Undo point` to remove all points.
+                """
+                step3 = gr.Markdown(step3_dec)
+                depth_image = gr.Image(type="pil", label="Depth Image", show_label=True, interactive=False)
+                with gr.Row():
+                    depth_button = gr.Button("Get Depth")
+                    undo_button = gr.Button("Undo point")
+                    
+        with gr.Row():
+            with gr.Column():
+                # Step 4: Trajectory to Camera Pose or Get Camera Pose
+                step4_dec = """
+                    <font size="4"><b>Step 4: Get camera pose from trajectory or customize it</b></font>
+                    - Option 1: Transform the 2D trajectory to camera poses with depth. <mark>`Rescale` is used for depth alignment. Larger value can speed up the object motion.</mark>
+                    - Option 2: Rotate the camera with a specific `Angle`.
+                    - Option 3: Rotate the camera clockwise or counterclockwise with a specific `Angle`.
+                    - Option 4: Translate the camera with `Tx` (<mark>Pan Left/Right</mark>), `Ty` (<mark>Pan Up/Down</mark>), `Tz` (<mark>Zoom In/Out</mark>) and `Speed`.
+                """
+                step4 = gr.Markdown(step4_dec)
+                camera_pose_vis = gr.Plot(None, label='Camera Pose')
+                with gr.Row():
+                    with gr.Column():
+                        speed = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1.0, label="Speed", interactive=True)
+                        rescale = gr.Slider(minimum=0.0, maximum=10, step=0.1, value=1.0, label="Rescale", interactive=True)
+                        # traj2pose_button = gr.Button("Option1: Trajectory to Camera Pose")
+                        
+                        angle = gr.Slider(minimum=-360, maximum=360, step=1, value=60, label="Angle", interactive=True)
+                        # rotation_button = gr.Button("Option2: Rotate")
+                        # clockwise_button = gr.Button("Option3: Clockwise")
+                    with gr.Column():
+                        
+                        Tx = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Tx", interactive=True)
+                        Ty = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Ty", interactive=True)
+                        Tz = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Tz", interactive=True)
+                        # translation_button = gr.Button("Option4: Translate")
+                with gr.Row():
+                    camera_option = gr.Radio(choices = CAMERA_MODE, label='Camera Options', value=CAMERA_MODE[0], interactive=True)
+                with gr.Row():
+                    get_camera_pose_button = gr.Button("Get Camera Pose")
+                        
+            with gr.Column():
+                # Step 5: Get the final generated video
+                step5_dec = """
+                    <font size="4"><b>Step 5: Get the final generated video</b></font>
+                    - 3 modes for background: <mark>Fixed</mark>, <mark>Reverse</mark>, <mark>Free</mark>.
+                    - Enable <mark>Scale-wise Masks</mark> for better object control.
+                    - Option to enable <mark>Shared Warping Latents</mark> and set <mark>stop frequency</mark> for spatial (`ds`) and temporal (`dt`) dimensions. Larger stop frequency will lead to artifacts.
+                """
+                step5 = gr.Markdown(step5_dec)
+                generated_video = gr.Video(None, label='Generated Video')
+                
+                with gr.Row():
+                    seed = gr.Textbox(value = "42", label="Seed", interactive=True)
+                    # num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, value=25, label="Number of Inference Steps", interactive=True)
+                    bg_mode = gr.Radio(choices = ["Fixed", "Reverse", "Free"], label="Background Mode", value="Fixed", interactive=True)
+                # swl_mode = gr.Radio(choices = ["Enable SWL", "Disable SWL"], label="Shared Warping Latent", value="Disable SWL", interactive=True)
+                scale_wise_masks = gr.Checkbox(label="Enable Scale-wise Masks", interactive=True, value=True)
+                with gr.Row():
+                    with gr.Column():
+                        shared_wapring_latents = gr.Checkbox(label="Enable Shared Warping Latents", interactive=True)
+                    with gr.Column():
+                        ds = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.5, label="ds", interactive=True)
+                        dt = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.5, label="dt", interactive=True)
+                
+                generated_button = gr.Button("Generate")
+
+                
+
+    # # event definition
+    process_button.click(
+        fn = process_image,
+        inputs = [raw_input],
+        outputs = [original_image, canvas]
+    )
+    
+    select_button.click(
+        segment,
+        [canvas, original_image, mask_logits],
+        [mask, mask_output, masked_original_image, mask_logits]
+    )
+    
+    depth_button.click(
+        get_depth,
+        [original_image, selected_points],
+        [depth, depth_image, org_depth_image]
+    )
+    
+    depth_image.select(
+        get_points,
+        [depth_image, selected_points],
+        [depth_image, selected_points],
+    )
+    undo_button.click(
+        undo_points,
+        [org_depth_image],
+        [depth_image, selected_points]
+    )
+    
+    get_camera_pose_button.click(
+        get_camera_pose(CAMERA_MODE),
+        [camera_option, selected_points, depth, mask, rescale, angle, Tx, Ty, Tz, speed],
+        [camera_pose, camera_pose_vis, rescale]
+    )
+    
+    generated_button.click(
+        run_objctrl_2_5d,
+        [
+         original_image,
+         mask,
+         depth,
+         camera_pose,
+         bg_mode,
+         shared_wapring_latents,
+         scale_wise_masks,
+         rescale,
+         seed,
+         ds,
+         dt,
+        #  num_inference_steps
+         ],
+        [generated_video],
+    )
+
+    gr.Examples(
+        examples=examples,
+        inputs=[
+            raw_input,
+            rescale,
+            speed,
+            angle,
+            Tx,
+            Ty,
+            Tz,
+            camera_option,
+            bg_mode,
+            shared_wapring_latents,
+            scale_wise_masks,
+            ds,
+            dt,
+            seed,
+            selected_points_text  # selected_points
+        ],
+        outputs=[generated_video], 
+        examples_per_page=10
+    )
+    
+    selected_points_text.change(
+        sync_points,
+        inputs=[selected_points_text],
+        outputs=[selected_points]
+    )
+
+
+    gr.Markdown(article)
+
+
+demo.queue().launch(share=True)

app.py

@@ -1,12 +1,18 @@
-import spaces
+try:
+    import spaces
+except:
+    pass
+
 import os
 import gradio as gr
+import json
+import ast
 
 import torch
 from gradio_image_prompter import ImagePrompter
 from sam2.sam2_image_predictor import SAM2ImagePredictor
 from omegaconf import OmegaConf
-from PIL import Image
+from PIL import Image, ImageDraw
 import numpy as np
 from copy import deepcopy
 import cv2
@@ -16,7 +22,7 @@ import torchvision
 from einops import rearrange
 import tempfile
 
-from objctrl_2_5d.utils.ui_utils import process_image, get_camera_pose, get_subject_points, get_points, undo_points, mask_image
+from objctrl_2_5d.utils.ui_utils import process_image, get_camera_pose, get_subject_points, get_points, undo_points, mask_image, traj2cam, get_mid_params
 from ZoeDepth.zoedepth.utils.misc import colorize
 
 from cameractrl.inference import get_pipeline
@@ -25,7 +31,6 @@ from objctrl_2_5d.utils.examples import examples, sync_points
 from objctrl_2_5d.utils.objmask_util import RT2Plucker, Unprojected, roll_with_ignore_multidim, dilate_mask_pytorch
 from objctrl_2_5d.utils.filter_utils import get_freq_filter, freq_mix_3d
 
-
 ### Title and Description ###
 #### Description ####
 title = r"""<h1 align="center">ObjCtrl-2.5D: Training-free Object Control with Camera Poses</h1>"""
@@ -85,9 +90,40 @@ If you have any questions, please feel free to reach me out at <b>zhouzi1212@gma
 
 """
 
+# pre-defined parameters
+DEBUG = False
+
+if DEBUG:
+    cur_OUTPUT_PATH = 'outputs/tmp'
+    os.makedirs(cur_OUTPUT_PATH, exist_ok=True)
+
+# num_inference_steps=25
+min_guidance_scale = 1.0
+max_guidance_scale = 3.0
+
+area_ratio = 0.3
+depth_scale_ = 5.2
+center_margin = 10
+
+height, width = 320, 576
+num_frames = 14
+
+intrinsics = np.array([[float(width), float(width), float(width) / 2, float(height) / 2]])
+intrinsics = np.repeat(intrinsics, num_frames, axis=0) # [n_frame, 4]
+fx = intrinsics[0, 0] / width
+fy = intrinsics[0, 1] / height
+cx = intrinsics[0, 2] / width
+cy = intrinsics[0, 3] / height
+
+down_scale = 8
+H, W = height // down_scale, width // down_scale
+K = np.array([[width / down_scale, 0, W / 2], [0, width / down_scale, H / 2], [0, 0, 1]])
+
+
 # -------------- initialization --------------
 
-CAMERA_MODE = ["Traj2Cam", "Rotate", "Clockwise", "Translate"]
+# CAMERA_MODE = ["Traj2Cam", "Rotate", "Clockwise", "Translate"]
+CAMERA_MODE = ["None", "ZoomIn", "ZoomOut", "PanRight", "PanLeft", "TiltUp", "TiltDown", "ClockWise", "Anti-CW", "Rotate60"]
 
 # select the device for computation
 if torch.cuda.is_available():
@@ -96,11 +132,9 @@ elif torch.backends.mps.is_available():
     device = torch.device("mps")
 else:
     device = torch.device("cpu")
-    device = torch.device("cuda")
-    print(f"Force device to {device} due to ZeroGPU")
 print(f"using device: {device}")
 
-# segmentation model
+# # segmentation model
 segmentor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-tiny", cache_dir="ckpt", device=device)
 
 # depth model
@@ -126,7 +160,7 @@ pipeline = get_pipeline(model_id, "unet", model_config['down_block_types'], mode
 # pipeline = None
 
 ### run the demo ##
-@spaces.GPU(duration=5)
+# @spaces.GPU(duration=5)
 def segment(canvas, image, logits):
     if logits is not None:
         logits *=  32.0
@@ -165,28 +199,9 @@ def segment(canvas, image, logits):
         masked_img = mask_image(image, mask[0], color=[252, 140, 90], alpha=0.9)
         masked_img = Image.fromarray(masked_img)
         
-    return mask[0], masked_img, masked_img, logits / 32.0
-
-@spaces.GPU(duration=5)
-def get_depth(image, points):
-    
-    depth = d_model_NK.infer_pil(image)    
-    colored_depth = colorize(depth, cmap='gray_r') # [h, w, 4] 0-255
-    
-    depth_img = deepcopy(colored_depth[:, :, :3])
-    if len(points) > 0:
-        for idx, point in enumerate(points):
-            if idx % 2 == 0:
-                cv2.circle(depth_img, tuple(point), 10, (255, 0, 0), -1)
-            else:
-                cv2.circle(depth_img, tuple(point), 10, (0, 0, 255), -1)
-            if idx > 0:
-                cv2.arrowedLine(depth_img, points[idx-1], points[idx], (255, 255, 255), 4, tipLength=0.5)
-    
-    return depth, depth_img, colored_depth[:, :, :3]
+    return mask[0], {'image': masked_img, 'points': points}, logits / 32.0
 
-
-@spaces.GPU(duration=80)
+# @spaces.GPU(duration=80)
 def run_objctrl_2_5d(condition_image, 
                         mask, 
                         depth, 
@@ -198,35 +213,6 @@ def run_objctrl_2_5d(condition_image,
                         seed, 
                         ds, dt, 
                         num_inference_steps=25):
-    
-    DEBUG = False
-
-    if DEBUG:
-        cur_OUTPUT_PATH = 'outputs/tmp'
-        os.makedirs(cur_OUTPUT_PATH, exist_ok=True)
-
-    # num_inference_steps=25
-    min_guidance_scale = 1.0
-    max_guidance_scale = 3.0
-
-    area_ratio = 0.3
-    depth_scale_ = 5.2
-    center_margin = 10
-
-    height, width = 320, 576
-    num_frames = 14
-
-    intrinsics = np.array([[float(width), float(width), float(width) / 2, float(height) / 2]])
-    intrinsics = np.repeat(intrinsics, num_frames, axis=0) # [n_frame, 4]
-    fx = intrinsics[0, 0] / width
-    fy = intrinsics[0, 1] / height
-    cx = intrinsics[0, 2] / width
-    cy = intrinsics[0, 3] / height
-
-    down_scale = 8
-    H, W = height // down_scale, width // down_scale
-    K = np.array([[width / down_scale, 0, W / 2], [0, width / down_scale, H / 2], [0, 0, 1]])
-    
     seed = int(seed)
             
     center_h_margin, center_w_margin = center_margin, center_margin
@@ -288,7 +274,7 @@ def run_objctrl_2_5d(condition_image,
         fix_pose_features = None
         
     #### preparing mask
-    
+
     mask = Image.fromarray(mask)
     mask = mask.resize((W, H))
     mask = np.array(mask).astype(np.float32)
@@ -500,6 +486,97 @@ def run_objctrl_2_5d(condition_image,
     
     return video_path
 
+
+# UI function
+# @spaces.GPU(duration=5)
+def process_image(raw_image, trajectory_points):
+    
+    image, points = raw_image['image'], raw_image['points']
+    
+    print(points)
+    
+    try:
+        assert(len(points)) == 1, "Please draw only one bbox"
+        [x1, y1, _, x2, y2, _] = points[0]
+        
+        image = image.crop((x1, y1, x2, y2))
+        image = image.resize((width, height))
+    except:
+        image = image.resize((width, height))
+
+    depth = d_model_NK.infer_pil(image)    
+    colored_depth = colorize(depth, cmap='gray_r') # [h, w, 4] 0-255
+    
+    depth_img = deepcopy(colored_depth[:, :, :3])
+    if len(trajectory_points) > 0:
+        for idx, point in enumerate(trajectory_points):
+            if idx % 2 == 0:
+                cv2.circle(depth_img, tuple(point), 10, (255, 0, 0), -1)
+            else:
+                cv2.circle(depth_img, tuple(point), 10, (0, 0, 255), -1)
+            if idx > 0:
+                line_length = np.sqrt((trajectory_points[idx][0] - trajectory_points[idx-1][0])**2 + (trajectory_points[idx][1] - trajectory_points[idx-1][1])**2)
+                arrow_head_length = 10
+                tip_length = arrow_head_length / line_length
+                cv2.arrowedLine(depth_img, trajectory_points[idx-1], trajectory_points[idx], (0, 255, 0), 4, tipLength=tip_length)
+    
+    return image, {'image': image}, depth, depth_img, colored_depth[:, :, :3]
+
+
+
+def draw_points_on_image(img, points):
+    # img = Image.fromarray(np.array(image))
+    draw = ImageDraw.Draw(img)
+    
+    for p in points:
+        x1, y1, _, x2, y2, _ = p
+        
+        if x2 == 0 and y2 == 0:
+            # Point: 青色点带黑边
+            point_radius = 4
+            draw.ellipse(
+                (x1 - point_radius, y1 - point_radius, x1 + point_radius, y1 + point_radius),
+                fill="cyan", outline="black", width=1
+            )
+        else:
+            # Bounding Box: 黑色矩形框
+            draw.rectangle([x1, y1, x2, y2], outline="black", width=3)
+    
+    return img
+
+# @spaces.GPU(duration=10)
+def from_examples(raw_input, raw_image_points, canvas, seg_image_points, selected_points_text, camera_option, mask_bk):
+    
+    selected_points = ast.literal_eval(selected_points_text)
+    mask = np.array(mask_bk)
+    mask = mask[:,:,0] > 0
+    selected_points = ast.literal_eval(selected_points_text)
+    
+    image, _, depth, depth_img, colored_depth = process_image(raw_input, selected_points)
+    
+    # get camera pose
+    if camera_option == "None":
+        # traj2came
+        rescale = 1.0
+        camera_pose, camera_pose_vis, rescale, _ = traj2cam(selected_points,  depth , rescale)
+    else:
+        rescale = 0.0
+        angle = 60
+        speed = 4.0
+        camera_pose, camera_pose_vis, rescale = get_camera_pose(CAMERA_MODE)(camera_option, depth, mask, rescale, angle, speed)
+        
+    raw_image_points = ast.literal_eval(raw_image_points)
+    seg_image_points = ast.literal_eval(seg_image_points)
+    
+    raw_image = draw_points_on_image(raw_input['image'], raw_image_points)
+    seg_image = draw_points_on_image(canvas['image'], seg_image_points)
+        
+    return image, mask, depth, depth_img, colored_depth, camera_pose, \
+            camera_pose_vis, rescale, selected_points, \
+            gr.update(value={'image': raw_image, 'points': raw_image_points}), \
+            gr.update(value={'image': seg_image, 'points': seg_image_points}), \
+
+
 # -------------- UI definition --------------
 with gr.Blocks() as demo:
     # layout definition
@@ -513,12 +590,16 @@ with gr.Blocks() as demo:
     # with gr.Row():
     #     gr.Markdown("""# <center>Repositioning the Subject within Image </center>""")
     mask = gr.State(value=None) # store mask
+    mask_bk = gr.Image(type="pil", label="Mask", show_label=True, interactive=False, visible=False)
+    
     removal_mask = gr.State(value=None) # store removal mask
     selected_points = gr.State([]) # store points
     selected_points_text = gr.Textbox(label="Selected Points", visible=False)
+    raw_image_points = gr.Textbox(label="Raw Image Points", visible=False)
+    seg_image_points = gr.Textbox(label="Segment Image Points", visible=False)
     
     original_image = gr.State(value=None) # store original input image
-    masked_original_image = gr.State(value=None) # store masked input image
+    # masked_original_image = gr.State(value=None) # store masked input image
     mask_logits = gr.State(value=None) # store mask logits
     
     depth = gr.State(value=None) # store depth
@@ -526,14 +607,22 @@ with gr.Blocks() as demo:
     
     camera_pose = gr.State(value=None) # store camera pose
     
+    rescale = gr.Slider(minimum=0.0, maximum=10, step=0.1, value=1.0, label="Rescale", interactive=True, visible=False)
+    angle = gr.Slider(minimum=-360, maximum=360, step=1, value=60, label="Angle", interactive=True, visible=False)
+    
+    seed = gr.Textbox(value = "42", label="Seed", interactive=True, visible=False)
+    scale_wise_masks = gr.Checkbox(label="Enable Scale-wise Masks", interactive=True, value=True, visible=False)
+    ds = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.25, label="ds", interactive=True, visible=False)
+    dt = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.1, label="dt", interactive=True, visible=False)
+    
     with gr.Column():
         
         outlines = """
         <font size="5"><b>There are total 5 steps to complete the task.</b></font>
-        - Step 1: Input an image and Crop it to a suitable size;
+        - Step 1: Input an image and Crop it to a suitable size and attained depth;
         - Step 2: Attain the subject mask;
-        - Step 3: Get depth and Draw Trajectory;
-        - Step 4: Get camera pose from trajectory or customize it;
+        - Step 3: Draw trajectory on depth map or skip to use camera pose;
+        - Step 4: Select camera poses or skip.
         - Step 5: Generate the final video.
         """
         
@@ -545,125 +634,92 @@ with gr.Blocks() as demo:
                 # Step 1: Input Image
                 step1_dec = """
                     <font size="4"><b>Step 1: Input Image</b></font>
-                    - Select the region using a <mark>bounding box</mark>, aiming for a ratio close to </mark>320:576</mark> (height:width).
-                    - All provided images in `Examples` are in 320 x 576 resolution. Simply press `Process` to proceed.
                     """
                 step1 = gr.Markdown(step1_dec)
                 raw_input = ImagePrompter(type="pil", label="Raw Image", show_label=True, interactive=True)
-                # left_up_point = gr.Textbox(value = "-1 -1", label="Left Up Point", interactive=True)
+                
+                step1_notes = """
+                - Select the region using a <mark>bounding box</mark>, aiming for a ratio close to </mark>320:576</mark> (height:width).
+                - If the input is in 320 x 576, press `Process` directly.
+                """
+                notes = gr.Markdown(step1_notes)
+                
                 process_button = gr.Button("Process")
                 
             with gr.Column():
                 # Step 2: Get Subject Mask
                 step2_dec = """
                     <font size="4"><b>Step 2: Get Subject Mask</b></font>
-                    - Use the <mark>bounding boxes</mark> or <mark>paints</mark> to select the subject.
-                    - Press `Segment Subject` to get the mask. <mark>Can be refined iteratively by updating points<mark>.
                     """
                 step2 = gr.Markdown(step2_dec)
                 canvas = ImagePrompter(type="pil", label="Input Image", show_label=True, interactive=True) # for mask painting
 
+                step2_notes = """
+                    - Use the <mark>bounding boxes</mark> or <mark>points</mark> to select the subject.
+                    - Press `Segment Subject` to get the mask. <mark>Can be refined iteratively by updating points<mark>.
+                """
+                notes = gr.Markdown(step2_notes)
+
                 select_button = gr.Button("Segment Subject")
                 
-        with gr.Row():
-            with gr.Column():
-                mask_dec = """
-                    <font size="4"><b>Mask Result</b></font>
-                    - Just for visualization purpose. No need to interact.
-                """
-                mask_vis = gr.Markdown(mask_dec)
-                mask_output = gr.Image(type="pil", label="Mask", show_label=True, interactive=False)
             with gr.Column():
                 # Step 3: Get Depth and Draw Trajectory
                 step3_dec = """
-                    <font size="4"><b>Step 3: Get Depth and Draw Trajectory</b></font>
-                    - Press `Get Depth` to get the depth image.
-                    - Draw the trajectory by selecting points on the depth image. <mark>No more than 14 points</mark>.
-                    - Press `Undo point` to remove all points.
+                    <font size="4"><b>Step 3: Draw Trajectory on Depth or <mark>SKIP</mark></b></font>
+                    
                 """
                 step3 = gr.Markdown(step3_dec)
                 depth_image = gr.Image(type="pil", label="Depth Image", show_label=True, interactive=False)
-                with gr.Row():
-                    depth_button = gr.Button("Get Depth")
-                    undo_button = gr.Button("Undo point")
-                    
+                
+                step3_dec = """
+                    - Selecting points on the depth image. <mark>No more than 14 points</mark>.
+                    - Press `Undo point` to remove all points. Press `Traj2Cam` to get camera poses.
+                    """
+                notes = gr.Markdown(step3_dec)
+                
+                undo_button = gr.Button("Undo point")
+                traj2cam_button = gr.Button("Traj2Cam")
+                
         with gr.Row():
+            
             with gr.Column():
                 # Step 4: Trajectory to Camera Pose or Get Camera Pose
                 step4_dec = """
-                    <font size="4"><b>Step 4: Get camera pose from trajectory or customize it</b></font>
-                    - Option 1: Transform the 2D trajectory to camera poses with depth. <mark>`Rescale` is used for depth alignment. Larger value can speed up the object motion.</mark>
-                    - Option 2: Rotate the camera with a specific `Angle`.
-                    - Option 3: Rotate the camera clockwise or counterclockwise with a specific `Angle`.
-                    - Option 4: Translate the camera with `Tx` (<mark>Pan Left/Right</mark>), `Ty` (<mark>Pan Up/Down</mark>), `Tz` (<mark>Zoom In/Out</mark>) and `Speed`.
+                    <font size="4"><b>Step 4: Get Customized Camera Poses or <mark>Skip</mark></b></font>
                 """
                 step4 = gr.Markdown(step4_dec)
                 camera_pose_vis = gr.Plot(None, label='Camera Pose')
-                with gr.Row():
-                    with gr.Column():
-                        speed = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1.0, label="Speed", interactive=True)
-                        rescale = gr.Slider(minimum=0.0, maximum=10, step=0.1, value=1.0, label="Rescale", interactive=True)
-                        # traj2pose_button = gr.Button("Option1: Trajectory to Camera Pose")
-                        
-                        angle = gr.Slider(minimum=-360, maximum=360, step=1, value=60, label="Angle", interactive=True)
-                        # rotation_button = gr.Button("Option2: Rotate")
-                        # clockwise_button = gr.Button("Option3: Clockwise")
-                    with gr.Column():
-                        
-                        Tx = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Tx", interactive=True)
-                        Ty = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Ty", interactive=True)
-                        Tz = gr.Slider(minimum=-1, maximum=1, step=1, value=0, label="Tz", interactive=True)
-                        # translation_button = gr.Button("Option4: Translate")
-                with gr.Row():
-                    camera_option = gr.Radio(choices = CAMERA_MODE, label='Camera Options', value=CAMERA_MODE[0], interactive=True)
-                with gr.Row():
-                    get_camera_pose_button = gr.Button("Get Camera Pose")
+                camera_option = gr.Radio(choices = CAMERA_MODE, label='Camera Options', value=CAMERA_MODE[0], interactive=True)
+                speed = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=4.0, label="Speed", interactive=True, visible=True)
                         
             with gr.Column():
                 # Step 5: Get the final generated video
                 step5_dec = """
                     <font size="4"><b>Step 5: Get the final generated video</b></font>
-                    - 3 modes for background: <mark>Fixed</mark>, <mark>Reverse</mark>, <mark>Free</mark>.
-                    - Enable <mark>Scale-wise Masks</mark> for better object control.
-                    - Option to enable <mark>Shared Warping Latents</mark> and set <mark>stop frequency</mark> for spatial (`ds`) and temporal (`dt`) dimensions. Larger stop frequency will lead to artifacts.
                 """
                 step5 = gr.Markdown(step5_dec)
                 generated_video = gr.Video(None, label='Generated Video')
                 
-                with gr.Row():
-                    seed = gr.Textbox(value = "42", label="Seed", interactive=True)
-                    # num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, value=25, label="Number of Inference Steps", interactive=True)
-                    bg_mode = gr.Radio(choices = ["Fixed", "Reverse", "Free"], label="Background Mode", value="Fixed", interactive=True)
-                # swl_mode = gr.Radio(choices = ["Enable SWL", "Disable SWL"], label="Shared Warping Latent", value="Disable SWL", interactive=True)
-                scale_wise_masks = gr.Checkbox(label="Enable Scale-wise Masks", interactive=True, value=True)
-                with gr.Row():
-                    with gr.Column():
-                        shared_wapring_latents = gr.Checkbox(label="Enable Shared Warping Latents", interactive=True)
-                    with gr.Column():
-                        ds = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.5, label="ds", interactive=True)
-                        dt = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.5, label="dt", interactive=True)
+                # with gr.Row():
+                bg_mode = gr.Radio(choices = ["Fixed", "Reverse", "Free"], label="Background Mode", value="Fixed", interactive=True)
+                shared_wapring_latents = gr.Checkbox(label="Enable Shared Warping Latents", interactive=True, value=False, visible=True)
                 
                 generated_button = gr.Button("Generate")
 
+                get_mid_params_button = gr.Button("Get Mid Params")
                 
 
     # # event definition
     process_button.click(
         fn = process_image,
-        inputs = [raw_input],
-        outputs = [original_image, canvas]
+        inputs = [raw_input, selected_points],
+        outputs = [original_image, canvas, depth, depth_image, org_depth_image]
     )
     
     select_button.click(
         segment,
         [canvas, original_image, mask_logits],
-        [mask, mask_output, masked_original_image, mask_logits]
-    )
-    
-    depth_button.click(
-        get_depth,
-        [original_image, selected_points],
-        [depth, depth_image, org_depth_image]
+        [mask, canvas, mask_logits]
     )
     
     depth_image.select(
@@ -677,9 +733,15 @@ with gr.Blocks() as demo:
         [depth_image, selected_points]
     )
     
-    get_camera_pose_button.click(
+    traj2cam_button.click(
+        traj2cam,
+        [selected_points, depth, rescale],
+        [camera_pose, camera_pose_vis, rescale, camera_option]
+    )
+    
+    camera_option.change(
         get_camera_pose(CAMERA_MODE),
-        [camera_option, selected_points, depth, mask, rescale, angle, Tx, Ty, Tz, speed],
+        [camera_option, depth, mask, rescale, angle, speed],
         [camera_pose, camera_pose_vis, rescale]
     )
     
@@ -701,35 +763,44 @@ with gr.Blocks() as demo:
          ],
         [generated_video],
     )
+    
+    get_mid_params_button.click(
+        get_mid_params,
+        [raw_input, canvas, mask, selected_points, camera_option, bg_mode, shared_wapring_latents, generated_video]
+    )
+    
+    ## Get examples
+    with open('./assets/examples/examples.json', 'r') as f:
+        examples = json.load(f)
+        print(examples)
+    
+    # examples = [examples]
+    examples = [v for k, v in examples.items()]
 
     gr.Examples(
         examples=examples,
         inputs=[
             raw_input,
-            rescale,
-            speed,
-            angle,
-            Tx,
-            Ty,
-            Tz,
+            raw_image_points,
+            canvas,
+            seg_image_points,
+            mask_bk,
+            selected_points_text,  # selected_points
             camera_option,
             bg_mode,
             shared_wapring_latents,
-            scale_wise_masks,
-            ds,
-            dt,
-            seed,
-            selected_points_text  # selected_points
+            generated_video
         ],
-        outputs=[generated_video], 
-        examples_per_page=10
+        examples_per_page=20
     )
     
     selected_points_text.change(
-        sync_points,
-        inputs=[selected_points_text],
-        outputs=[selected_points]
+        from_examples,
+        inputs=[raw_input, raw_image_points, canvas, seg_image_points, selected_points_text, camera_option, mask_bk],
+        outputs=[original_image, mask, depth, depth_image, org_depth_image, camera_pose, camera_pose_vis, rescale, selected_points, raw_input, canvas]
     )
+    
+            
 
 
     gr.Markdown(article)