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@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+source/1.gif filter=lfs diff=lfs merge=lfs -text
+source/2.gif filter=lfs diff=lfs merge=lfs -text
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+source/brief-framework2-min.png filter=lfs diff=lfs merge=lfs -text
+source/overview.png filter=lfs diff=lfs merge=lfs -text
+source/vis_comparison-min.png filter=lfs diff=lfs merge=lfs -text

CODE_OF_CONDUCT.md

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+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
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+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
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+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
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+Examples of representing a project or community include using an official
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+
+This Code of Conduct also applies outside the project spaces when there is a
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+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
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+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
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+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

LICENSE.txt

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OPM/1-get-initial-mask-of-controlled-object.py

@@ -0,0 +1,79 @@
+import os
+import cv2
+import json
+import numpy as np
+
+from PIL import Image, ImageDraw
+from segment_anything import SamPredictor, sam_model_registry
+
+model_type = "vit_b"
+if model_type == "vit_h":
+    sam_checkpoint = "YOUR_PATH/sam_vit_h_4b8939.pth"
+    # https://huggingface.co/spaces/abhishek/StableSAM/blob/main/sam_vit_h_4b8939.pth
+elif model_type == "vit_b":
+    sam_checkpoint = "YOUR_PATH/sam_vit_b_01ec64.pth"
+    # https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/sams/sam_vit_b_01ec64.pth
+else:
+    raise ValueError(f"Invalid model type: {model_type}")
+
+
+sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
+predictor = SamPredictor(sam)
+
+
+image_folder = 'data/VOI'
+seqs = os.listdir(image_folder)
+seqs.sort()
+
+trajectory_path = 'data/VOI-GT'
+
+img_with_initial_point = 'data/VOI-initial-point'
+if not os.path.exists(img_with_initial_point):
+    os.makedirs(img_with_initial_point)
+
+# 统计时间
+import time
+import torch
+torch.cuda.synchronize()
+start = time.time()
+for seq in seqs:
+    print(f"Processing {seq}, {seqs.index(seq)+1}/{len(seqs)}")
+    seq_path = os.path.join(image_folder, seq)
+    images = os.listdir(seq_path)
+    images.sort()
+    image_path = os.path.join(seq_path, images[0])
+    image = Image.open(image_path)
+
+    file_path = os.path.join(trajectory_path, seq + '.json')
+    data = json.load(open(file_path))
+    init_point = data[0][0][0]
+    print(init_point)
+
+    # Step-1:use initial point to segment the object
+    
+    predictor.set_image(cv2.imread(image_path))
+    masks, _, _ = predictor.predict(point_coords=np.array([init_point]), point_labels=np.array([1]), multimask_output=False)
+
+    mask = masks[0]
+    mask_image = Image.fromarray((mask * 255).astype(np.uint8))
+
+    # Create a completely transparent mask
+    red_mask = Image.new('RGBA', image.size, (0, 0, 0, 0))  
+
+    # Add a red semi-transparent mask only in the segmentation area
+    mask_image = mask_image.convert("L")
+    red_mask.paste((255, 0, 0, 100), (0, 0), mask_image)  
+
+    # Transfer to RGBA mode and merge with mask
+    combined_image = Image.alpha_composite(image.convert("RGBA"), red_mask)
+
+    # Transfer to RGB mode and save
+    combined_image_rgb = combined_image.convert('RGB')
+    save_path = os.path.join(img_with_initial_point, seq + '_segmented_sam_b.jpg')
+    combined_image_rgb.save(save_path)
+
+
+torch.cuda.synchronize()
+end = time.time()
+print(f"Time: {end-start}")
+print(f"Average time: {(end-start)/len(seqs)}")

OPM/2-recognize_objects.py

@@ -0,0 +1,43 @@
+import os
+import cv2
+import json
+import numpy as np
+
+from PIL import Image, ImageDraw
+from gpt4v import get_image_description
+
+image_folder = 'data/VOI'
+
+seqs = os.listdir(image_folder)
+seqs.sort()
+print(seqs)
+
+trajectory_path = 'data/VOI-GT'
+
+img_with_initial_point = 'data/VOI-initial-point'
+
+# time synchronization and time statistics
+import time
+start = time.time()
+
+for seq in seqs:
+    print(f"Processing {seq}, {seqs.index(seq)+1}/{len(seqs)}")
+    title = "Analyze the image to identify objects. First, identify the object segmented by a prominent color at the part level, then determine other objects it may contact. List only the categories of these objects as single words, separated by commas, with no additional text, symbols, or line breaks."
+    seg_path = os.path.join(img_with_initial_point, seq + '_segmented_sam_b.jpg')
+    description = get_image_description(seg_path, title)
+    print(description)
+    
+    words = [word.strip() for word in description[0].replace('\n', '').replace(',', '').split()]
+
+    #  Change the list of words to JSON format
+    description_json = json.dumps({"classes": words})
+
+    # print(description_json)
+    save_path = os.path.join(image_folder,seq, 'objects_classes.json')
+    with open(save_path, 'w') as f:
+        f.write(description_json)
+
+
+end = time.time()
+print(f"Time: {end-start}")
+print(f"Average time: {(end-start)/len(seqs)}")

OPM/3-Gen-object-info.py

@@ -0,0 +1 @@
+# Go to OPM/submodules/Grounded-Segment-Anything/grounding_dino_CDrag.py

OPM/gpt4v.py

@@ -0,0 +1,55 @@
+import os
+import base64
+import requests
+
+# Function to encode the image
+def encode_image(image_path):
+  with open(image_path, "rb") as image_file:
+    return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Function to get image description
+def get_image_description(image_path, title):
+  base64_image = encode_image(image_path)
+
+  headers = {
+    "Content-Type": "application/json",
+    "Authorization": 'YOUR_GPT4o_API_KEY'
+  }
+
+  payload = {
+    "model": "gpt-4o",
+    "messages": [
+      {
+        "role": "user",
+        "content": [
+          {
+            "type": "text",
+            "text": title
+          },
+          {
+            "type": "image_url",
+            "image_url": {
+              "url": f"data:image/jpeg;base64,{base64_image}",
+              "detail": "low"
+            }
+          }
+        ]
+      }
+    ],
+    "max_tokens": 1800
+  }
+
+
+
+  response = requests.post("https://api.kwwai.top/v1/chat/completions", headers=headers, json=payload)
+
+  if response.status_code != 200:
+    print(response.text)
+    return None
+
+  # Extract the description from the response
+  if 'content' in response.json()['choices'][0]['message']:
+    description = response.json()['choices'][0]['message']['content']
+  else:
+    description = None
+  return description