pr3

.gitattributes

@@ -35,4 +35,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.png filter=lfs diff=lfs merge=lfs -text
-*.mp4 filter=lfs diff=lfs merge=lfs -text

Makefile

@@ -21,9 +21,4 @@ dev:
 
 hf:
 	chmod 777 hf.sh
-	./hf.sh
-
-requirements:
-	uv pip compile --no-annotate pyproject.toml --no-deps --no-strip-extras --no-header \
-	| sed -E 's/([a-zA-Z0-9_-]+(\[[a-zA-Z0-9_,-]+\])?)[=><~!].*/\1/g' \
-	> requirements.txt
+	./hf.sh

README.md

@@ -1,79 +1,79 @@
 ---
 title: ImageAlfred
 emoji: 😻
-tags:
-  - mcp-server-track
 colorFrom: green
 colorTo: purple
 sdk: gradio
-sdk_version: 5.33.0
+sdk_version: 5.32.1
 app_file: src/app.py
 pinned: false
 license: apache-2.0
 short_description: 'Alfred of Images: An MCP server to handle your image edits.'
 ---
 
-<div align="center">
-<a href="https://github.com/mahan-ym/ImageAlfred">
-<img src="./src/assets/icons/ImageAlfredIcon.png" alt="ImageAlfred" width=200 height=200>
+![Image Alfred](./src/assets/ImageAlfredIcon.png)
 
-<span><img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white"></span>
+# ImageAlfred
 
-</a>
-<h1>Image Alfred</h1>
+ImageAlfred is an image Model Context Protocol (MCP) tool designed to streamline image processing workflows.
+<!-- It provides a user-friendly interface for interacting with image models, leveraging the power of Gradio for the frontend and Modal for scalable backend deployment. -->
 
-ImageAlfred is an image Model Context Protocol (MCP) tool designed to streamline image processing workflows
-
-<img alt="Python Version from PEP 621 TOML" src="https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fmahan-ym%2FImageAlfred%2Fmain%2Fpyproject.toml">
-<img src="https://badge.mcpx.dev?type=server" title="MCP Server"/>
-<img alt="GitHub License" src="https://img.shields.io/github/license/mahan-ym/ImageAlfred">
-
-<a href=https://huggingface.co> <img src="src/assets/icons/hf-logo.svg" alt="huggingface" height=40> </a>
-<a href="https://www.python.org"><img src="src/assets/icons/python-logo-only.svg" alt="python" height=40></a>
-</div>
-
-## Demo
-
-[🎬 Video demo](https://youtu.be/tEov-Bcuulk)
+<!-- ## Features
+- Intuitive web interface for image processing
+- Powered by Gradio for rapid prototyping and UI
+- Scalable and serverless execution with Modal
+- Easily extendable for custom image models and workflows -->
 
 ## Maintainers
+[Mahan Yarmohammad (Mahan-ym)](https://www.mahan-ym.com/)
+[Saaed Saadatipour (Soodoo)](https://soodoo.me/)
 
-[Mahan-ym | Mahan Yarmohammad](https://www.mahan-ym.com/)
-
-[Soodoo | Saaed Saadatipour](https://soodoo.me/)
-
-## Tools
-
-- [Gradio](https://www.gradio.app/): Serving user interface and MCP server.
-- [Modal.com](https://modal.com/): AI infrastructure making all the magic 🔮 possible.
-- [SAM](https://segment-anything.com/): Segment Anything model by meta for image segmentation and mask generation.
-- [CLIPSeg](https://github.com/timojl/clipseg): Image Segmentation using CLIP. We used it as a more precise object detection model.
-- [OWLv2](https://huggingface.co/google/owlv2-large-patch14-ensemble): Zero-Shot object detection (Better performance in license plate detection and privacy preserving use-cases)
+# Used Tools
+- [Gradio](https://www.gradio.app/): Serving user interface and MCP server
+- [lang-segment-anything](https://github.com/luca-medeiros/lang-segment-anything): Which uses [SAM](https://segment-anything.com/) and [Grounding Dino](https://github.com/IDEA-Research/GroundingDINO) under the hood to segment images.
 - [HuggingFace](https://huggingface.co/): Downloading SAM and using Space for hosting.
+- [Modal.com](https://modal.com/): AI infrastructure making all the magic possible.
 
 ## Getting Started
 
 ### Prerequisites
-
-- Python 3.12+
+- Python 3.13+
 - [uv](https://github.com/astral-sh/uv) (a fast Python package installer and virtual environment manager)
 
 ### Installation
 
-It will create virtual environment, activate it, install dependecies and setup modal
+1. **Create a virtual environment using uv:**
 
 ```bash
-make install
+uv venv
 ```
 
-### Running the App
+2. **Activate the virtual environment:**
 
-This will launch the Gradio interface for ImageAlfred.
+```bash
+source .venv/bin/activate
+```
+
+3. **Install dependencies:**
+
+```bash
+uv sync
+```
+
+4. **Setup Modal**
+
+```bash
+modal setup
+```
+
+### Running the App
 
 ```bash
-make run
+uv run src/app.py
 ```
 
+This will launch the Gradio interface for ImageAlfred.
+
 ## License
 
 This project is licensed under the terms of the LICENSE file in this repository.

claude_desktop_config.json

@@ -1,22 +0,0 @@
-{
-    "mcpServers": {
-        "Image Alfred": {
-            "command": "npx",
-            "args": [
-                "mcp-remote",
-                "https://agents-mcp-hackathon-imagealfred.hf.space/gradio_api/mcp/sse",
-                "--transport",
-                "sse-only"
-            ]
-        },
-        "local Image Alfred": {
-            "command": "npx",
-            "args": [
-                "mcp-remote",
-                "http://127.0.0.1:7860/gradio_api/mcp/sse",
-                "--transport",
-                "sse-only"
-            ]
-        }
-    }
-}

hf.sh

@@ -5,6 +5,7 @@ REPO_URL="https://github.com/mahan-ym/ImageAlfred"
 REPO_DIR="ImageAlfred"
 TEMP_DIR="./tmp"
 SRC_DIR="src"
+REQUIREMENTS_FILE="requirements.txt"
 
 echo "🚀 Starting Huggingface Space update script..."
 
@@ -31,21 +32,6 @@ if [ -d "$SRC_DIR" ]; then
 fi
 cp -r "$TEMP_DIR/$REPO_DIR/$SRC_DIR" .
 mv "$TEMP_DIR/$REPO_DIR/Makefile" .
-mv "$TEMP_DIR/$REPO_DIR/requirements.txt" .
-mv "$TEMP_DIR/$REPO_DIR/pyproject.toml" .
-mv "$TEMP_DIR/$REPO_DIR/uv.lock" .
-mv "$TEMP_DIR/$REPO_DIR/claude_desktop_config.json" .
-mv "$TEMP_DIR/$REPO_DIR/LICENSE" .
-
-# Concatenate README files
-echo "📄 Creating combined README file..."
-if [ -f "$TEMP_DIR/$REPO_DIR/hf_readme.md" ] && [ -f "$TEMP_DIR/$REPO_DIR/README.md" ]; then
-    cat "$TEMP_DIR/$REPO_DIR/hf_readme.md" "$TEMP_DIR/$REPO_DIR/README.md" > README.md
-    echo "✅ Combined README created successfully!"
-else
-    echo "⚠️ Could not find one or both README files for concatenation."
-fi
-
 
 # Check if copy was successful
 if [ $? -eq 0 ]; then

pyproject.toml

@@ -9,6 +9,7 @@ requires-python = ">=3.12"
 
 dependencies = [
     "gradio[mcp]>=5.32.1",
+    "matplotlib>=3.10.3",
     "modal>=1.0.2",
     "numpy>=2.2.6",
     "pillow>=11.2.1",
@@ -17,11 +18,8 @@ dependencies = [
 [dependency-groups]
 dev = [
     "jupyterlab>=4.4.3",
-    "matplotlib>=3.10.3",
     "opencv-contrib-python>=4.11.0.86",
-    "rapidfuzz>=3.13.0",
     "ruff>=0.11.12",
-    "supervision>=0.25.1",
 ]
 
 [tool.ruff]

requirements.txt

@@ -1,4 +1,4 @@
 gradio[mcp]
 modal
 numpy
-pillow
+pillow

src/app.py

@@ -6,7 +6,6 @@ from tools import (
     change_color_objects_hsv,
     change_color_objects_lab,
     privacy_preserve_image,
-    remove_background,
 )
 
 gr.set_static_paths(paths=[Path.cwd().absolute() / "assets"])
@@ -19,19 +18,20 @@ title = """Image Alfred - Recolor and Privacy Preserving Image MCP Tools
 """  # noqa: E501
 
 hsv_df_input = gr.Dataframe(
-    headers=["Object", "Red", "Green", "Blue"],
-    datatype=["str", "number", "number", "number"],
-    col_count=(4, "fixed"),
+    headers=["Object", "Hue", "Saturation Scale"],
+    datatype=["str", "number", "number"],
+    col_count=(3, "fixed"),
     show_row_numbers=True,
-    label="Target Objects and Their new RGB Colors",
+    label="Target Objects and New Settings",
     type="array",
+    # row_count=(1, "dynamic"),
 )
 
 lab_df_input = gr.Dataframe(
     headers=["Object", "New A", "New B"],
     datatype=["str", "number", "number"],
-    col_count=(3, "fixed"),
-    label="Target Objects and New Settings.(0-255 -- 128 = Neutral)",
+    col_count=(3,"fixed"),
+    label="Target Objects and New Settings",
     type="array",
 )
 
@@ -45,20 +45,19 @@ change_color_objects_hsv_tool = gr.Interface(
     title="Image Recolor Tool (HSV)",
     description="""
     This tool allows you to recolor objects in an image using the HSV color space.
-    You can specify the RGB values for each object.""",  # noqa: E501
+    You can specify the hue and saturation scale for each object.""",  # noqa: E501
     examples=[
         [
             "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_1.jpg",
-            [
-                ["pants", 255, 178, 102],
-            ],
+            [["pants", 128, 1]],
+        ],
+        [
+            "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_4.jpg",
+            [["desk", 15, 0.5], ["left cup", 40, 1.1]],
         ],
         [
-            "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_8.jpg",
-            [
-                ["pants", 114, 117, 34],
-                ["shirt", 51, 51, 37],
-            ],
+            "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_5.jpg",
+            [["suits", 60, 1.5], ["pants", 10, 0.8]],
         ],
     ],
 )
@@ -78,15 +77,15 @@ change_color_objects_lab_tool = gr.Interface(
     examples=[
         [
             "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_1.jpg",
-            [["pants", 112, 128]],
+            [["pants", 128, 1]],
         ],
         [
             "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_4.jpg",
-            [["desk", 166, 193]],
+            [["desk", 15, 0.5], ["left cup", 40, 1.1]],
         ],
         [
             "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_5.jpg",
-            [["suits coat", 110, 133]],
+            [["suits", 60, 1.5], ["pants", 10, 0.8]],
         ],
     ],
 )
@@ -107,14 +106,6 @@ privacy_preserve_tool = gr.Interface(
             step=1,
             info="Higher values result in stronger blurring.",
         ),
-        gr.Slider(
-            label="Detection Threshold",
-            minimum=0.01,
-            maximum=0.99,
-            value=0.2,
-            step=0.01,
-            info="Model threshold for detecting objects.",
-        ),
     ],
     outputs=gr.Image(label="Output Image"),
     title="Privacy Preserving Tool",
@@ -122,59 +113,19 @@ privacy_preserve_tool = gr.Interface(
     examples=[
         [
             "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_3.jpg",
-            "license plate",
+            "license plate.",
             10,
-            0.5,
-        ],
-        [
-            "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_8.jpg",
-            "face",
-            15,
-            0.1,
-        ],
-        [
-            "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_6.jpg",
-            "face",
-            20,
-            0.1,
-        ],
-    ],
-)
-
-remove_background_tool = gr.Interface(
-    fn=remove_background,
-    inputs=[
-        gr.Image(label="Input Image", type="pil"),
-    ],
-    outputs=gr.Image(label="Output Image"),
-    title="Remove Image Background Tool",
-    description="Upload an image to remove the background.",
-    examples=[
-        [
-            "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_5.jpg",
-        ],
-        [
-            "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_6.jpg",
-        ],
-        [
-            "https://raw.githubusercontent.com/mahan-ym/ImageAlfred/main/src/assets/examples/test_8.jpg",
         ],
     ],
 )
 
 demo = gr.TabbedInterface(
     [
-        privacy_preserve_tool,
-        remove_background_tool,
         change_color_objects_hsv_tool,
         change_color_objects_lab_tool,
+        privacy_preserve_tool,
     ],
-    [
-        "Privacy Preserving Tool",
-        "Remove Background Tool",
-        "Change Color Objects HSV",
-        "Change Color Objects LAB",
-    ],
+    ["Change Color Objects HSV", "Change Color Objects LAB", "Privacy Preserving Tool"],
     title=title,
     theme=gr.themes.Default(
         primary_hue="blue",

src/assets/vid/demo.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:249a630becf81774a3cc86bf35858c648c7a673a4e8c2854b4a92fb02eb7c01f
-size 3678354

src/modal_app.py

@@ -29,17 +29,13 @@ image = (
             "TORCH_HOME": TORCH_HOME,
         }
     )
-    .apt_install(
-        "git",
-    )
+    .apt_install("git")
     .pip_install(
         "huggingface-hub",
         "hf_transfer",
         "Pillow",
         "numpy",
-        "transformers",
         "opencv-contrib-python-headless",
-        "scipy",
         gpu="A10G",
     )
     .pip_install(
@@ -48,284 +44,52 @@ image = (
         index_url="https://download.pytorch.org/whl/cu124",
         gpu="A10G",
     )
-    .pip_install("git+https://github.com/openai/CLIP.git", gpu="A10G")
-    .pip_install("git+https://github.com/facebookresearch/sam2.git", gpu="A10G")
     .pip_install(
-        "git+https://github.com/PramaLLC/BEN2.git#egg=ben2",
+        "git+https://github.com/luca-medeiros/lang-segment-anything.git",
         gpu="A10G",
     )
 )
 
 
 @app.function(
-    image=image,
-    gpu="A10G",
-    volumes={volume_path: volume},
-    timeout=60 * 3,
-)
-def prompt_segment(
-    image_pil: Image.Image,
-    prompts: list[str],
-) -> list[dict]:
-    clip_results = clip.remote(image_pil, prompts)
-
-    if not clip_results:
-        print("No boxes returned from CLIP.")
-        return None
-
-    boxes = np.array(clip_results["boxes"])
-
-    sam_result_masks, sam_result_scores = sam2.remote(image_pil=image_pil, boxes=boxes)
-
-    print(f"sam_result_mask {sam_result_masks}")
-
-    if not sam_result_masks.any():
-        print("No masks or scores returned from SAM2.")
-        return None
-
-    if sam_result_masks.ndim == 3:
-        # If the masks are in 3D, we need to convert them to 4D
-        sam_result_masks = [sam_result_masks]
-
-    results = {
-        "labels": clip_results["labels"],
-        "boxes": boxes,
-        "clip_scores": clip_results["scores"],
-        "sam_masking_scores": sam_result_scores,
-        "masks": sam_result_masks,
-    }
-    return results
-
-
-@app.function(
-    image=image,
     gpu="A10G",
-    volumes={volume_path: volume},
-    timeout=60 * 3,
-)
-def privacy_prompt_segment(
-    image_pil: Image.Image,
-    prompts: list[str],
-    threshold: float,
-) -> list[dict]:
-    owlv2_results = owlv2.remote(image_pil, prompts, threshold=threshold)
-
-    if not owlv2_results:
-        print("No boxes returned from OWLV2.")
-        return None
-
-    boxes = np.array(owlv2_results["boxes"])
-
-    sam_result_masks, sam_result_scores = sam2.remote(image_pil=image_pil, boxes=boxes)
-
-    print(f"sam_result_mask {sam_result_masks}")
-
-    if not sam_result_masks.any():
-        print("No masks or scores returned from SAM2.")
-        return None
-
-    if sam_result_masks.ndim == 3:
-        # If the masks are in 3D, we need to convert them to 4D
-        sam_result_masks = [sam_result_masks]
-
-    results = {
-        "labels": owlv2_results["labels"],
-        "boxes": boxes,
-        "owlv2_scores": owlv2_results["scores"],
-        "sam_masking_scores": sam_result_scores,
-        "masks": sam_result_masks,
-    }
-    return results
-
-
-@app.function(
-    image=image,
-    gpu="A100",
-    volumes={volume_path: volume},
-    timeout=60 * 3,
-)
-def sam2(image_pil: Image.Image, boxes: list[np.ndarray]) -> list[dict]:
-    import torch
-    from sam2.sam2_image_predictor import SAM2ImagePredictor
-
-    predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-large")
-
-    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
-        predictor.set_image(image_pil)
-        masks, scores, _ = predictor.predict(
-            point_coords=None,
-            point_labels=None,
-            box=boxes,
-            multimask_output=False,
-        )
-    return masks, scores
-
-
-@app.function(
-    image=image,
-    gpu="A100",
-    volumes={volume_path: volume},
-)
-def owlv2(
-    image_pil: Image.Image,
-    labels: list[str],
-    threshold: float,
-) -> list[dict]:
-    """
-    Perform zero-shot segmentation on an image using specified labels.
-    Args:
-        image_pil (Image.Image): The input image as a PIL Image.
-        labels (list[str]): List of labels for zero-shot segmentation.
-
-    Returns:
-        list[dict]: List of dictionaries containing label and bounding box information.
-    """
-    from transformers import pipeline
-
-    checkpoint = "google/owlv2-large-patch14-ensemble"
-    detector = pipeline(
-        model=checkpoint,
-        task="zero-shot-object-detection",
-        device="cuda",
-        use_fast=True,
-    )
-    # Load the image
-    predictions = detector(
-        image_pil,
-        candidate_labels=labels,
-    )
-    labels = []
-    scores = []
-    boxes = []
-    for prediction in predictions:
-        if prediction["score"] < threshold:
-            continue
-        labels.append(prediction["label"])
-        scores.append(prediction["score"])
-        boxes.append(np.array(list(prediction["box"].values())))
-    if labels == []:
-        print("No predictions found with score above threshold.")
-        return None
-    predictions = {"labels": labels, "scores": scores, "boxes": boxes}
-    return predictions
-
-
-@app.function(
     image=image,
-    gpu="A100",
     volumes={volume_path: volume},
+    # min_containers=1,
     timeout=60 * 3,
 )
-def clip(
+def lang_sam_segment(
     image_pil: Image.Image,
-    prompts: list[str],
-) -> list[dict]:
-    """
-    returns:
-        dict with keys each are lists:
-            - labels: str, the prompt used for the prediction
-            - scores: float, confidence score of the prediction
-            - boxes: np.array representing bounding box coordinates
-    """
-
-    from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
-    import torch
-
-    processor = CLIPSegProcessor.from_pretrained(
-        "CIDAS/clipseg-rd64-refined",
-        use_fast=True,
-    )
-    model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
-
-    # Get original image dimensions
-    orig_width, orig_height = image_pil.size
-
-    inputs = processor(
-        text=prompts,
-        images=[image_pil] * len(prompts),
-        padding="max_length",
-        return_tensors="pt",
+    prompt: str,
+    box_threshold=0.3,
+    text_threshold=0.25,
+) -> list:
+    """Segments an image using LangSAM based on a text prompt.
+    This function uses LangSAM to segment objects in the image based on the provided prompt.
+    """  # noqa: E501
+    from lang_sam import LangSAM  # type: ignore
+
+    model = LangSAM(sam_type="sam2.1_hiera_large")
+    langsam_results = model.predict(
+        images_pil=[image_pil],
+        texts_prompt=[prompt],
+        box_threshold=box_threshold,
+        text_threshold=text_threshold,
     )
-    # predict
-    with torch.no_grad():
-        outputs = model(**inputs)
-    preds = outputs.logits.unsqueeze(1)
-
-    # Get the dimensions of the prediction output
-    pred_height, pred_width = preds.shape[-2:]
-
-    # Calculate scaling factors
-    width_scale = orig_width / pred_width
-    height_scale = orig_height / pred_height
-
-    labels = []
-    scores = []
-    boxes = []
-
-    # Process each prediction to find bounding boxes in high probability regions
-    for i, prompt in enumerate(prompts):
-        # Apply sigmoid to get probability map
-        pred_tensor = torch.sigmoid(preds[i][0])
-        # Convert tensor to numpy array
-        pred_np = pred_tensor.cpu().numpy()
-
-        # Convert to uint8 for OpenCV processing
-        heatmap = (pred_np * 255).astype(np.uint8)
-
-        # Apply threshold to find high probability regions
-        _, binary = cv2.threshold(heatmap, 127, 255, cv2.THRESH_BINARY)
-
-        # Find contours in thresholded image
-        contours, _ = cv2.findContours(
-            binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
-        )
-
-        # Process each contour to get bounding boxes
-        for contour in contours:
-            # Skip very small contours that might be noise
-            if cv2.contourArea(contour) < 100:  # Minimum area threshold
-                continue
-
-            # Get bounding box coordinates in prediction space
-            x, y, w, h = cv2.boundingRect(contour)
-
-            # Scale coordinates to original image dimensions
-            x_orig = int(x * width_scale)
-            y_orig = int(y * height_scale)
-            w_orig = int(w * width_scale)
-            h_orig = int(h * height_scale)
-
-            # Calculate confidence score based on average probability in the region
-            mask = np.zeros_like(pred_np)
-            cv2.drawContours(mask, [contour], 0, 1, -1)
-            confidence = float(np.mean(pred_np[mask == 1]))
-
-            labels.append(prompt)
-            scores.append(confidence)
-            boxes.append(
-                np.array(
-                    [
-                        x_orig,
-                        y_orig,
-                        x_orig + w_orig,
-                        y_orig + h_orig,
-                    ]
-                )
-            )
-
-    if labels == []:
+    if len(langsam_results[0]["labels"]) == 0:
+        print("No masks found for the given prompt.")
         return None
+    
+    print(f"found {len(langsam_results[0]['labels'])} masks for prompt: {prompt}")
+    print("labels:", langsam_results[0]["labels"])
+    print("scores:", langsam_results[0]["scores"])
+    print("masks scores:", langsam_results[0].get("mask_scores", "No mask scores available"))  # noqa: E501
 
-    results = {
-        "labels": labels,
-        "scores": scores,
-        "boxes": boxes,
-    }
-    return results
+    return langsam_results
 
 
 @app.function(
-    gpu="A10G",
+    gpu="T4",
     image=image,
     volumes={volume_path: volume},
     timeout=60 * 3,
@@ -334,20 +98,19 @@ def change_image_objects_hsv(
     image_pil: Image.Image,
     targets_config: list[list[str | int | float]],
 ) -> Image.Image:
+    """Changes the hue and saturation of specified objects in an image.
+    This function uses LangSAM to segment objects in the image based on provided prompts,
+    and then modifies the hue and saturation of those objects in the HSV color space.
+    """  # noqa: E501
     if not isinstance(targets_config, list) or not all(
         (
             isinstance(target, list)
-            and len(target) == 4
+            and len(target) == 3
             and isinstance(target[0], str)
-            and isinstance(target[1], (int))
-            and isinstance(target[2], (int))
-            and isinstance(target[3], (int))
-            and target[1] >= 0
-            and target[1] <= 255
+            and isinstance(target[1], (int, float))
+            and isinstance(target[2], (int, float))
+            and 0 <= target[1] <= 179
             and target[2] >= 0
-            and target[2] <= 255
-            and target[3] >= 0
-            and target[3] <= 255
         )
         for target in targets_config
     ):
@@ -355,66 +118,38 @@ def change_image_objects_hsv(
             "targets_config must be a list of lists, each containing [target_name, hue, saturation_scale]."  # noqa: E501
         )
     print("Change image objects hsv targets config:", targets_config)
-    prompts = [target[0].strip() for target in targets_config]
+    prompts = ". ".join(target[0] for target in targets_config)
 
-    prompt_segment_results = prompt_segment.remote(
-        image_pil=image_pil,
-        prompts=prompts,
-    )
-    if not prompt_segment_results:
+    langsam_results = lang_sam_segment.remote(image_pil=image_pil, prompt=prompts)
+    if not langsam_results:
         return image_pil
 
-    output_labels = prompt_segment_results["labels"]
+    labels = langsam_results[0]["labels"]
+    scores = langsam_results[0]["scores"]
 
     img_array = np.array(image_pil)
     img_hsv = cv2.cvtColor(img_array, cv2.COLOR_RGB2HSV).astype(np.float32)
 
-    for idx, label in enumerate(output_labels):
-        if not label or label == "":
-            print("Skipping empty label.")
-            continue
-        if label not in prompts:
-            print(f"Label '{label}' not found in prompts. Skipping.")
+    for target_spec in targets_config:
+        target_obj = target_spec[0]
+        hue = target_spec[1]
+        saturation_scale = target_spec[2]
+
+        try:
+            mask_idx = labels.index(target_obj)
+        except ValueError:
+            print(
+                f"Warning: Label '{target_obj}' not found in the image. Skipping this target."  # noqa: E501
+            )
             continue
-        input_label_idx = prompts.index(label)
-        target_rgb = targets_config[input_label_idx][1:]
-        target_hsv = cv2.cvtColor(np.uint8([[target_rgb]]), cv2.COLOR_RGB2HSV)[0][0]
-
-        mask = prompt_segment_results["masks"][idx][0].astype(bool)
-        h, s, v = cv2.split(img_hsv)
-        # Convert all channels to float32 for consistent processing
-        h = h.astype(np.float32)
-        s = s.astype(np.float32)
-        v = v.astype(np.float32)
-
-        # Compute original S and V means inside the mask
-        mean_s = np.mean(s[mask])
-        mean_v = np.mean(v[mask])
-
-        # Target S and V
-        target_hue, target_s, target_v = target_hsv
-
-        # Compute scaling factors (avoid div by zero)
-        scale_s = target_s / mean_s if mean_s > 0 else 1.0
-        scale_v = target_v / mean_v if mean_v > 0 else 1.0
-
-        scale_s = np.clip(scale_s, 0.8, 1.2)
-        scale_v = np.clip(scale_v, 0.8, 1.2)
-
-        # Apply changes only in mask
-        h[mask] = target_hue
-        s = s.astype(np.float32)
-        v = v.astype(np.float32)
-        s[mask] = np.clip(s[mask] * scale_s, 0, 255)
-        v[mask] = np.clip(v[mask] * scale_v, 0, 255)
-
-        # Merge and convert back
-        img_hsv = cv2.merge(
-            [
-                h.astype(np.uint8),
-                s.astype(np.uint8),
-                v.astype(np.uint8),
-            ]
+
+        mask = langsam_results[0]["masks"][mask_idx]
+        mask_bool = mask.astype(bool)
+
+        img_hsv[mask_bool, 0] = float(hue)
+        img_hsv[mask_bool, 1] = np.minimum(
+            img_hsv[mask_bool, 1] * saturation_scale,
+            255.0,
         )
 
     output_img = cv2.cvtColor(img_hsv.astype(np.uint8), cv2.COLOR_HSV2RGB)
@@ -423,7 +158,7 @@ def change_image_objects_hsv(
 
 
 @app.function(
-    gpu="A10G",
+    gpu="T4",
     image=image,
     volumes={volume_path: volume},
     timeout=60 * 3,
@@ -454,35 +189,33 @@ def change_image_objects_lab(
 
     print("change image objects lab targets config:", targets_config)
 
-    prompts = [target[0].strip() for target in targets_config]
+    prompts = ". ".join(target[0] for target in targets_config)
 
-    prompt_segment_results = prompt_segment.remote(
+    langsam_results = lang_sam_segment.remote(
         image_pil=image_pil,
-        prompts=prompts,
+        prompt=prompts,
     )
-    if not prompt_segment_results:
+    if not langsam_results:
         return image_pil
 
-    output_labels = prompt_segment_results["labels"]
-
+    labels = langsam_results[0]["labels"]
+    scores = langsam_results[0]["scores"]
     img_array = np.array(image_pil)
     img_lab = cv2.cvtColor(img_array, cv2.COLOR_RGB2Lab).astype(np.float32)
-
-    for idx, label in enumerate(output_labels):
-        if not label or label == "":
-            print("Skipping empty label.")
-            continue
-
-        if label not in prompts:
-            print(f"Label '{label}' not found in prompts. Skipping.")
+    for target_spec in targets_config:
+        target_obj = target_spec[0]
+        new_a = target_spec[1]
+        new_b = target_spec[2]
+
+        try:
+            mask_idx = labels.index(target_obj)
+        except ValueError:
+            print(
+                f"Warning: Label '{target_obj}' not found in the image. Skipping this target."  # noqa: E501
+            )
             continue
 
-        input_label_idx = prompts.index(label)
-
-        new_a = targets_config[input_label_idx][1]
-        new_b = targets_config[input_label_idx][2]
-
-        mask = prompt_segment_results["masks"][idx][0]
+        mask = langsam_results[0]["masks"][mask_idx]
         mask_bool = mask.astype(bool)
 
         img_lab[mask_bool, 1] = new_a
@@ -495,7 +228,7 @@ def change_image_objects_lab(
 
 
 @app.function(
-    gpu="A10G",
+    gpu="T4",
     image=image,
     volumes={volume_path: volume},
     timeout=60 * 3,
@@ -523,80 +256,57 @@ def apply_mosaic_with_bool_mask(
 
 
 @app.function(
-    gpu="A10G",
+    gpu="T4",
     image=image,
     volumes={volume_path: volume},
     timeout=60 * 3,
 )
 def preserve_privacy(
     image_pil: Image.Image,
-    prompts: list[str],
+    prompt: str,
     privacy_strength: int = 15,
-    threshold: float = 0.2,
 ) -> Image.Image:
     """
     Preserves privacy in an image by applying a mosaic effect to specified objects.
     """
-    print(f"Preserving privacy for prompt: {prompts} with strength {privacy_strength}")
-    if isinstance(prompts, str):
-        prompts = [prompt.strip() for prompt in prompts.split(".")]
-        print(f"Parsed prompts: {prompts}")
-    prompt_segment_results = privacy_prompt_segment.remote(
+    print(f"Preserving privacy for prompt: {prompt} with strength {privacy_strength}")
+
+    langsam_results = lang_sam_segment.remote(
         image_pil=image_pil,
-        prompts=prompts,
-        threshold=threshold,
+        prompt=prompt,
+        box_threshold=0.35,
+        text_threshold=0.40,
     )
-    if not prompt_segment_results:
+    if not langsam_results:
         return image_pil
 
     img_array = np.array(image_pil)
 
-    for i, mask in enumerate(prompt_segment_results["masks"]):
-        mask_bool = mask[0].astype(bool)
-
-        # Create kernel for morphological operations
-        kernel_size = 100
-        kernel = np.ones((kernel_size, kernel_size), np.uint8)
-
-        # Convert bool mask to uint8 for OpenCV operations
-        mask_uint8 = mask_bool.astype(np.uint8) * 255
-
-        # Apply dilation to slightly expand the mask area
-        mask_uint8 = cv2.dilate(mask_uint8, kernel, iterations=2)
-        # Optional: Apply erosion again to refine the mask
-        mask_uint8 = cv2.erode(mask_uint8, kernel, iterations=2)
+    for result in langsam_results:
+        print(f"result: {result}")
+        
+        for i, mask in enumerate(result["masks"]):
+            if "mask_scores" in result:
+                if (
+                    hasattr(result["mask_scores"], "shape")
+                    and result["mask_scores"].ndim > 0
+                ):
+                    mask_score = result["mask_scores"][i]
+                else:
+                    mask_score = result["mask_scores"]
+            if mask_score < 0.6:
+                print(f"Skipping mask {i + 1}/{len(result['masks'])} -> low score.")
+                continue
+            print(
+                f"Processing mask {i + 1}/{len(result['masks'])} Mask score: {mask_score}"  # noqa: E501
+            )
 
-        # Convert back to boolean mask
-        mask_bool = mask_uint8 > 127
+            mask_bool = mask.astype(bool)
 
-        img_array = apply_mosaic_with_bool_mask.remote(
-            img_array, mask_bool, privacy_strength
-        )
+            img_array = apply_mosaic_with_bool_mask.remote(
+                img_array, mask_bool, privacy_strength
+            )
 
     output_image_pil = Image.fromarray(img_array)
 
     return output_image_pil
-
-
-@app.function(
-    gpu="A10G",
-    image=image,
-    volumes={volume_path: volume},
-    timeout=60 * 2,
-)
-def remove_background(image_pil: Image.Image) -> Image.Image:
-    import torch  # type: ignore
-    from ben2 import BEN_Base  # type: ignore
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    print(f"Using device: {device}")
-    print("type of image_pil:", type(image_pil))
-    model = BEN_Base.from_pretrained("PramaLLC/BEN2")
-    model.to(device).eval()  # todo check if this should be outside the function
-
-    output_image = model.inference(
-        image_pil,
-        refine_foreground=True,
-    )
-    print(f"output type: {type(output_image)}")
-    return output_image

src/tools.py

@@ -9,40 +9,10 @@ from PIL import Image
 modal_app_name = "ImageAlfred"
 
 
-def remove_background(
-    input_img,
-) -> np.ndarray | Image.Image | str | Path | None:
-    """
-    Remove the background of the image.
-
-    Args:
-        input_img: Input image or can be URL string of the image or base64 string. Cannot be None.
-    Returns:
-        bytes: Binary image data of the modified image.
-    """  # noqa: E501
-    if not input_img:
-        raise gr.Error("Input image cannot be None or empty.")
-
-    func = modal.Function.from_name(modal_app_name, "remove_background")
-    output_pil = func.remote(
-        image_pil=input_img,
-    )
-
-    if output_pil is None:
-        raise gr.Error("Received None from server.")
-    if not isinstance(output_pil, Image.Image):
-        raise gr.Error(
-            f"Expected Image.Image from server function, got {type(output_pil)}"
-        )
-
-    return output_pil
-
-
 def privacy_preserve_image(
     input_img,
     input_prompt,
     privacy_strength: int = 15,
-    threshold: float = 0.2,
 ) -> np.ndarray | Image.Image | str | Path | None:
     """
     Obscures specified objects in the input image based on a natural language prompt, using a privacy-preserving blur or distortion effect.
@@ -52,30 +22,27 @@ def privacy_preserve_image(
 
     Args:
         input_img: Input image or can be URL string of the image or base64 string. Cannot be None.
-        input_prompt (str): Object to obscure in the image has to be a dot-separated string. It can be a single word or multiple words, e.g., "left person face", "license plate" but it must be as short as possible and avoid using symbols or punctuation. e.g. input_prompt = "face. right car. blue shirt."
+        input_prompt (str): Object to obscure in the image has to be a dot-separated string. It can be a single word or multiple words, e.g., "left person face", "license plate" but it must be as short as possible and avoid using symbols or punctuation. Also you have to use single form of the word, e.g., "person" instead of "people", "face" instead of "faces". e.g. input_prompt = "face. right car. blue shirt."
         privacy_strength (int): Strength of the privacy preservation effect. Higher values result in stronger blurring. Default is 15.
-        threshold (float): Model threshold for detecting objects. It should be between 0.01 and 0.99. Default is 0.2. for detecting smaller objects, small regions or faces a lower threshold is recommended.
     Returns:
         bytes: Binary image data of the modified image.
 
     example:
-        input_prompt = "faces, license plates, logos"
+        input_prompt = ["face", "license plate"]
     """  # noqa: E501
     if not input_img:
         raise gr.Error("Input image cannot be None or empty.")
+    valid_pattern = re.compile(r"^[a-zA-Z\s.]+$")
     if not input_prompt or input_prompt.strip() == "":
         raise gr.Error("Input prompt cannot be None or empty.")
-    if threshold < 0.01 or threshold > 0.99:
-        raise gr.Error("Threshold must be between 0.01 and 0.99.")
-    if isinstance(input_prompt, str):
-        prompts = [prompt.strip() for prompt in input_prompt.split(".")]
+    if not valid_pattern.match(input_prompt):
+        raise gr.Error("Input prompt must contain only letters, spaces, and dots.")
 
-    func = modal.Function.from_name(modal_app_name, "preserve_privacy")
+    func = modal.Function.from_name("ImageAlfred", "preserve_privacy")
     output_pil = func.remote(
         image_pil=input_img,
-        prompts=prompts,
+        prompt=input_prompt,
         privacy_strength=privacy_strength,
-        threshold=threshold,
     )
 
     if output_pil is None:
@@ -94,22 +61,36 @@ def change_color_objects_hsv(
 ) -> np.ndarray | Image.Image | str | Path | None:
     """
     Changes the hue and saturation of specified objects in an image using the HSV color space.
-    This function segments image regions based on a user-provided text prompt and applies
-    color transformations in the HSV color space. HSV separates chromatic content (hue) from
-    intensity (value), making it more intuitive for color manipulation tasks.
+
+    This function segments objects in the image based on a user-provided text prompt, then
+    modifies their hue and saturation in the HSV (Hue, Saturation, Value) space. HSV is intuitive
+    for color manipulation where users think in terms of basic color categories and intensity,
+    making it useful for broad, vivid color shifts.
+
     Use this method when:
-    - You want to change the color of objects based on their hue and saturation.
-    - You want to apply color transformations that are less influenced by lighting conditions or brightness variations.
+    - Performing broad color changes or visual effects (e.g., turning a shirt from red to blue).
+    - Needing intuitive control over color categories (e.g., shifting everything that's red to purple).
+    - Saturation and vibrancy manipulation are more important than accurate perceptual matching.
+
+    OpenCV HSV Ranges:
+        - H: 0-179 (Hue angle on color wheel, where 0 = red, 60 = green, 120 = blue, etc.)
+        - S: 0-255 (Saturation)
+        - V: 0-255 (Brightness)
+
+    Common HSV color references:
+        - Red: (Hue≈0), Green: (Hue≈60), Blue: (Hue≈120), Yellow: (Hue≈30), Purple: (Hue≈150)
+        - Typically used with Saturation=255 for vivid colors.
+
 
     Args:
         input_img: Input image or can be URL string of the image or base64 string. Cannot be None.
-        user_input : A list of target specifications for color transformation. Each inner list must contain exactly four elements in the following order: 1. target_object (str) - A short, human-readable description of the object to be modified. Multi-word, descriptions are allowed for disambiguation (e.g., "right person shirt"), but they must be concise and free of punctuation, symbols, or special characters.2. Red (int) - Desired red value in RGB color space from 0 to 255. 3. Green (int) - Desired green value in RGB color space from 0 to 255. 4. Blue (int) - Desired blue value in RGB color space from 0 to 255. Example: user_input = [["hair", 30, 55, 255], ["shirt", 70, 0 , 157]].
+        user_input : A list of target specifications for color transformation. Each inner list must contain exactly three elements in the following order: 1. target_object (str) - A short, human-readable description of the object to be modified.Multi-word descriptions are allowed for disambiguation (e.g., "right person shirt"), but they must be at most three words and concise and free of punctuation, symbols, or special characters.2. hue (int) - Desired hue value in the HSV color space, ranging from 0 to 179. Represents the color angle on the HSV color wheel (e.g., 0 = red, 60 = green, 120 = blue)3. saturation_scale (float) - A multiplicative scale factor applied to the current saturation   of the object (must be > 0). For example, 1.0 preserves current saturation, 1.2 increases vibrancy, and 0.8 slightly desaturates. Each target object must be uniquely defined in the list to avoid conflicting transformations.Example: [["hair", 30, 1.2], ["right person shirt", 60, 1.0]]
 
     Returns:
         Base64-encoded string.
 
     Raises:
-        ValueError: If user_input format is invalid, or image format is invalid or corrupted.
+        ValueError: If user_input format is invalid, hue values are outside [0, 179] range, saturation_scale is not positive, or image format is invalid or corrupted.
         TypeError: If input_img is not a supported type or modal function returns unexpected type.
     """  # noqa: E501
     if len(user_input) == 0 or not isinstance(user_input, list):
@@ -118,13 +99,13 @@ def change_color_objects_hsv(
         )
     if not input_img:
         raise gr.Error("input img cannot be None or empty.")
-
+    
     print("before processing input:", user_input)
     valid_pattern = re.compile(r"^[a-zA-Z\s]+$")
     for item in user_input:
-        if len(item) != 4:
+        if len(item) != 3:
             raise gr.Error(
-                "Each item in user_input must be a list of [object, red, green, blue]"  # noqa: E501
+                "Each item in user_input must be a list of [object, hue, saturation_scale]"  # noqa: E501
             )
         if not item[0] or not valid_pattern.match(item[0]):
             raise gr.Error(
@@ -133,31 +114,28 @@ def change_color_objects_hsv(
 
         if not isinstance(item[0], str):
             item[0] = str(item[0])
-
-        try:
-            item[1] = int(item[1])
-        except ValueError:
-            raise gr.Error("Red must be an integer.")
-        if item[1] < 0 or item[1] > 255:
-            raise gr.Error("Red must be in the range [0, 255]")
-
-        try:
-            item[2] = int(item[2])
-        except ValueError:
-            raise gr.Error("Green must be an integer.")
-        if item[2] < 0 or item[2] > 255:
-            raise gr.Error("Green must be in the range [0, 255]")
-
-        try:
-            item[3] = int(item[3])
-        except ValueError:
-            raise gr.Error("Blue must be an integer.")
-        if item[3] < 0 or item[3] > 255:
-            raise gr.Error("Blue must be in the range [0, 255]")
+        if not item[1]:
+            raise gr.Error("Hue must be set and cannot be empty.")
+        if not isinstance(item[1], (int, float)):
+            try:
+                item[1] = int(item[1])
+            except ValueError:
+                raise gr.Error("Hue must be an integer.")
+            if item[1] < 0 or item[1] > 179:
+                raise gr.Error("Hue must be in the range [0, 179]")
+        if not item[2]:
+            raise gr.Error("Saturation scale must be set and cannot be empty.")
+        if not isinstance(item[2], (int, float)):
+            try:
+                item[2] = float(item[2])
+            except ValueError:
+                raise gr.Error("Saturation scale must be a float number.")
+            if item[2] <= 0:
+                raise gr.Error("Saturation scale must be greater than 0")
 
     print("after processing input:", user_input)
 
-    func = modal.Function.from_name(modal_app_name, "change_image_objects_hsv")
+    func = modal.Function.from_name("ImageAlfred", "change_image_objects_hsv")
     output_pil = func.remote(image_pil=input_img, targets_config=user_input)
 
     if output_pil is None:
@@ -202,7 +180,7 @@ def change_color_objects_lab(
         - Purple: (L=?, A≈180, B≈100)
 
     Args:
-        user_input: A list of color transformation instructions, each as a three-element list:[object_name (str), new_a (int, 0-255), new_b (int, 0-255)].- object_name: A short, unique identifier for the object to be recolored. Multi-word names are allowed for specificity (e.g., "right person shirt") but must be free of punctuation or special symbols.- new_a: The desired 'a' channel value in LAB space (green-red axis, 0-255, with 128 as neutral).- new_b: The desired 'b' channel value in LAB space (blue-yellow axis, 0-255, with 128 as neutral).Each object must appear only once in the list. Example:[["hair", 80, 128], ["right person shirt", 180, 160]]
+        user_input: A list of color transformation instructions, each as a three-element list:[object_name (str), new_a (int, 0-255), new_b (int, 0-255)].- object_name: A short, unique identifier for the object to be recolored. Multi-word names are allowed  for specificity (e.g., "right person shirt") but must be 3 words or fewer and free of punctuation or special symbols.- new_a: The desired 'a' channel value in LAB space (green-red axis, 0-255, with 128 as neutral).- new_b: The desired 'b' channel value in LAB space (blue-yellow axis, 0-255, with 128 as neutral).Each object must appear only once in the list. Example:[["hair", 80, 128], ["right person shirt", 180, 160]]
         input_img : Input image can be URL string of the image. Cannot be None.
 
     Returns:
@@ -220,7 +198,7 @@ def change_color_objects_lab(
         raise gr.Error("input img cannot be None or empty.")
     valid_pattern = re.compile(r"^[a-zA-Z\s]+$")
     print("before processing input:", user_input)
-
+    
     for item in user_input:
         if len(item) != 3:
             raise gr.Error(
@@ -252,7 +230,7 @@ def change_color_objects_lab(
                 raise gr.Error("new B must be in the range [0, 255]")
 
     print("after processing input:", user_input)
-    func = modal.Function.from_name(modal_app_name, "change_image_objects_lab")
+    func = modal.Function.from_name("ImageAlfred", "change_image_objects_lab")
     output_pil = func.remote(image_pil=input_img, targets_config=user_input)
     if output_pil is None:
         raise ValueError("Received None from modal remote function.")
@@ -260,5 +238,13 @@ def change_color_objects_lab(
         raise TypeError(
             f"Expected Image.Image from modal remote function, got {type(output_pil)}"
         )
+    # img_link = upload_image_to_tmpfiles(output_pil)
 
     return output_pil
+
+
+if __name__ == "__main__":
+    image_pil = Image.open("./src/assets/test_image.jpg")
+    change_color_objects_hsv(
+        user_input=[["hair", 30, 1.2], ["shirt", 60, 1.0]], input_img=image_pil
+    )

uv.lock

@@ -831,6 +831,7 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "gradio", extra = ["mcp"] },
+    { name = "matplotlib" },
     { name = "modal" },
     { name = "numpy" },
     { name = "pillow" },
@@ -839,16 +840,14 @@ dependencies = [
 [package.dev-dependencies]
 dev = [
     { name = "jupyterlab" },
-    { name = "matplotlib" },
     { name = "opencv-contrib-python" },
-    { name = "rapidfuzz" },
     { name = "ruff" },
-    { name = "supervision" },
 ]
 
 [package.metadata]
 requires-dist = [
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