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.claude/settings.local.json

@@ -0,0 +1,22 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(file /config/workspace/demo_img/*.tif)",
+      "Bash(python3 -c ' *)",
+      "Bash(python3 *)",
+      "Bash(uv run python -c ' *)",
+      "Bash(/config/.local/bin/uv run *)",
+      "Bash(git -C /config/workspace remote -v)",
+      "Bash(huggingface-cli whoami *)",
+      "Bash(git lfs *)",
+      "Bash(git remote *)",
+      "Bash(git add *)",
+      "Bash(git commit *)",
+      "Bash(git config *)",
+      "Bash(git push *)",
+      "Bash(hf auth *)",
+      "Bash(git -C /config/workspace add README.md)",
+      "Bash(git -C /config/workspace commit -m \"fix: valid HF colorTo value in README\")"
+    ]
+  }
+}

.gitattributes

@@ -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
+demo_img/tile_00005.tif filter=lfs diff=lfs merge=lfs -text
+demo_img/tile_00010.tif filter=lfs diff=lfs merge=lfs -text
+demo_img/tile_00049.tif filter=lfs diff=lfs merge=lfs -text
+demo_img/tile_00059.tif filter=lfs diff=lfs merge=lfs -text
+demo_img/tile_00060.tif filter=lfs diff=lfs merge=lfs -text
+demo_img/tile_00065.tif filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Python
+__pycache__/
+*.py[oc]
+*.egg-info/
+build/
+dist/
+
+# Virtual environments
+.venv/
+
+# Local dev tooling
+.python-version
+uv.lock
+pyproject.toml
+
+# Gradio
+.gradio/
+
+# App outputs
+pedestrian_detection.png
+.claude

.gradio/certificate.pem

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+-----BEGIN CERTIFICATE-----
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

README.md

@@ -1,12 +1,15 @@
 ---
-title: Geoimg Sam3 Demo
-emoji: 🌖
-colorFrom: pink
+title: SAM3 Pedestrian Crossing Detector
+emoji: 🚶
+colorFrom: red
 colorTo: yellow
 sdk: gradio
-sdk_version: 6.13.0
+sdk_version: 5.29.0
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# SAM3 — Détection de passages piétons
+
+Détecte et surligne les **passages piétons** (zebra crossing / crosswalk) par segmentation guidée par texte avec [SAM3](https://huggingface.co/facebook/sam3).
+

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image, ImageDraw, ImageFilter
+from transformers import Sam3Processor, Sam3Model
+import os
+
+DEMO_IMAGES = sorted([f"demo_img/{f}" for f in os.listdir("demo_img") if f.endswith(".tif")])
+
+OVERLAY_COLOR = (255, 50, 50)
+
+
+class Sam3PedestrianDetector:
+    def __init__(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model = None
+        self.processor = None
+        print(f"Initializing SAM3 on {self.device}")
+        try:
+            self.model = Sam3Model.from_pretrained("facebook/sam3").to(self.device)
+            self.processor = Sam3Processor.from_pretrained("facebook/sam3")
+            print("SAM3 loaded successfully.")
+        except Exception as e:
+            print(f"Error loading SAM3: {e}")
+
+    def predict_masks(self, image_pil, text_prompt, threshold=0.35):
+        if self.model is None:
+            return []
+        inputs = self.processor(
+            images=image_pil,
+            text=text_prompt,
+            return_tensors="pt"
+        ).to(self.device)
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        results = self.processor.post_process_instance_segmentation(
+            outputs,
+            threshold=threshold,
+            mask_threshold=0.5,
+            target_sizes=inputs["original_sizes"].tolist()
+        )[0]
+        masks = []
+        if "masks" in results:
+            for mask_tensor in results["masks"]:
+                mask_np = (mask_tensor.cpu().numpy() * 255).astype(np.uint8)
+                masks.append(Image.fromarray(mask_np))
+        return masks
+
+
+engine = Sam3PedestrianDetector()
+
+
+def draw_overlay(image_pil, masks, color_rgb, opacity):
+    """Semi-transparent colored fill over each detected mask."""
+    result = image_pil.copy().convert("RGBA")
+    overlay = Image.new("RGBA", image_pil.size, (0, 0, 0, 0))
+    alpha = int(opacity * 255)
+    r, g, b = color_rgb
+    for mask in masks:
+        fill = Image.new("RGBA", image_pil.size, (r, g, b, alpha))
+        overlay.paste(fill, mask=mask)
+    return Image.alpha_composite(result, overlay).convert("RGB")
+
+
+def draw_contours(image_pil, masks, color_rgb=(255, 50, 0)):
+    """Draw a 3-pixel contour border around each mask."""
+    result = image_pil.copy()
+    for mask in masks:
+        eroded = mask.filter(ImageFilter.MinFilter(5))
+        contour = np.clip(
+            np.array(mask).astype(int) - np.array(eroded).astype(int), 0, 255
+        ).astype(np.uint8)
+        contour_pil = Image.fromarray(contour)
+        color_layer = Image.new("RGB", image_pil.size, color_rgb)
+        result.paste(color_layer, mask=contour_pil)
+    return result
+
+
+def compute_stats(masks, image_pil):
+    w, h = image_pil.size
+    total_pixels = w * h
+    covered = np.zeros((h, w), dtype=bool)
+    areas = []
+    for mask in masks:
+        mask_np = np.array(mask) > 127
+        areas.append(int(mask_np.sum()))
+        covered |= mask_np
+    coverage_pct = round(100.0 * covered.sum() / total_pixels, 2)
+    return {"count": len(masks), "areas": areas, "coverage_pct": coverage_pct}
+
+
+def format_stats(stats):
+    lines = [f"**Passages piétons détectés : {stats['count']}**"]
+    lines.append(f"Couverture totale : **{stats['coverage_pct']}%** de l'image")
+    for i, area in enumerate(stats["areas"]):
+        lines.append(f"- Passage #{i+1} : {area:,} px")
+    return "\n\n".join(lines)
+
+
+def process_image(input_img, text_prompt, confidence, opacity):
+    if input_img is None:
+        return None, None, "Aucune image fournie."
+    if isinstance(input_img, np.ndarray):
+        image_pil = Image.fromarray(input_img).convert("RGB")
+    else:
+        image_pil = input_img.convert("RGB")
+
+    masks = engine.predict_masks(image_pil, text_prompt, confidence)
+
+    result = draw_overlay(image_pil, masks, OVERLAY_COLOR, opacity)
+    result = draw_contours(result, masks, OVERLAY_COLOR)
+
+    stats = compute_stats(masks, image_pil)
+    output_path = "pedestrian_detection.png"
+    result.save(output_path)
+
+    return np.array(result), output_path, format_stats(stats)
+
+
+
+with gr.Blocks(title="SAM3 — Détection de passages piétons", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# SAM3 — Détection de passages piétons")
+    gr.Markdown(
+        "Détecte et surligne les **passages piétons** (zebra crossing / crosswalk) "
+        "par segmentation guidée par texte avec SAM3."
+    )
+
+    with gr.Row():
+        with gr.Column():
+            im_input = gr.Image(label="Image d'entrée", type="numpy")
+            im_prompt = gr.Textbox(
+                label="Prompt texte",
+                value="pedestrian crossing",
+                info="Exemples : 'zebra crossing', 'crosswalk', 'pedestrian crossing'"
+            )
+            im_conf = gr.Slider(0.1, 1.0, value=0.35, step=0.05, label="Seuil de confiance")
+            im_opacity = gr.Slider(0.1, 1.0, value=0.45, step=0.05, label="Opacité de l'overlay")
+        with gr.Column():
+            im_output = gr.Image(label="Résultat")
+            im_stats = gr.Markdown()
+            im_dl = gr.File(label="Télécharger l'image")
+
+    gr.Examples(
+        examples=DEMO_IMAGES,
+        inputs=im_input,
+        label="Images de démonstration (demo_img/)"
+    )
+
+    im_input.change(
+        process_image,
+        [im_input, im_prompt, im_conf, im_opacity],
+        [im_output, im_dl, im_stats]
+    )
+
+demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+accelerate>=1.13.0
+gradio>=6.13.0
+numpy>=2.4.4
+pillow>=12.2.0
+torch>=2.11.0
+torchvision>=0.26.0
+git+https://github.com/huggingface/transformers.git