Initial deploy

README.md

@@ -1,12 +1,17 @@
 ---
 title: Semantic Segmentation
-emoji: 📉
-colorFrom: green
-colorTo: blue
+emoji: "\U0001F3A8"
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 6.11.0
+sdk_version: "5.29.0"
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Semantic Segmentation
+
+Upload an image to see pixel-level classification with DeepLabV3.
+
+**Courses**: 100 ch3 (Computer Vision), 360 ch4 (Perception)

app.py

@@ -0,0 +1,143 @@
+"""
+Semantic Segmentation — Pixel-level classification with DeepLabV3
+Courses: 100 ch3, 360 ch4
+"""
+
+import numpy as np
+import torch
+import torchvision.models.segmentation as seg_models
+import torchvision.transforms as T
+import gradio as gr
+from PIL import Image
+
+device = torch.device("cpu")
+
+# Load DeepLabV3 with MobileNetV3 backbone (lightweight)
+model = seg_models.deeplabv3_mobilenet_v3_large(
+    weights=seg_models.DeepLabV3_MobileNet_V3_Large_Weights.DEFAULT
+).eval().to(device)
+
+preprocess = T.Compose([
+    T.ToTensor(),
+    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# PASCAL VOC class names (21 classes)
+CLASS_NAMES = [
+    "background", "aeroplane", "bicycle", "bird", "boat",
+    "bottle", "bus", "car", "cat", "chair",
+    "cow", "dining table", "dog", "horse", "motorbike",
+    "person", "potted plant", "sheep", "sofa", "train",
+    "tv/monitor",
+]
+
+# Color palette for each class
+PALETTE = np.array([
+    [0, 0, 0],        # background
+    [128, 0, 0],      # aeroplane
+    [0, 128, 0],      # bicycle
+    [128, 128, 0],    # bird
+    [0, 0, 128],      # boat
+    [128, 0, 128],    # bottle
+    [0, 128, 128],    # bus
+    [128, 128, 128],  # car
+    [64, 0, 0],       # cat
+    [192, 0, 0],      # chair
+    [64, 128, 0],     # cow
+    [192, 128, 0],    # dining table
+    [64, 0, 128],     # dog
+    [192, 0, 128],    # horse
+    [64, 128, 128],   # motorbike
+    [192, 128, 128],  # person
+    [0, 64, 0],       # potted plant
+    [128, 64, 0],     # sheep
+    [0, 192, 0],      # sofa
+    [128, 192, 0],    # train
+    [0, 64, 128],     # tv/monitor
+], dtype=np.uint8)
+
+
+def segment(image: Image.Image, display_mode: str):
+    if image is None:
+        return None, None, ""
+
+    img = image.convert("RGB")
+    w, h = img.size
+
+    # Inference
+    inp = preprocess(img).unsqueeze(0).to(device)
+    with torch.no_grad():
+        output = model(inp)["out"]
+    pred = output.argmax(1).squeeze().cpu().numpy()
+
+    # Resize prediction to original size
+    pred_resized = np.array(
+        Image.fromarray(pred.astype(np.uint8)).resize((w, h), Image.NEAREST)
+    )
+
+    # Color segmentation map
+    seg_color = PALETTE[pred_resized]
+
+    # Overlay
+    img_np = np.array(img)
+    overlay = (img_np * 0.5 + seg_color * 0.5).astype(np.uint8)
+
+    # Detected classes
+    unique_classes = np.unique(pred_resized)
+    detected = [CLASS_NAMES[c] for c in unique_classes if c != 0]
+
+    legend = "**Detected classes:**\n\n"
+    for c in unique_classes:
+        if c == 0:
+            continue
+        color = PALETTE[c]
+        pixel_pct = np.sum(pred_resized == c) / pred_resized.size * 100
+        color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}"
+        legend += f"- <span style='color:{color_hex};font-weight:bold;'>██</span> {CLASS_NAMES[c]} ({pixel_pct:.1f}%)\n"
+
+    if not detected:
+        legend += "- No objects detected (background only)"
+
+    if display_mode == "Overlay":
+        return overlay, seg_color, legend
+    elif display_mode == "Segmentation Only":
+        return seg_color, seg_color, legend
+    else:  # Side by Side
+        return overlay, seg_color, legend
+
+
+with gr.Blocks(title="Semantic Segmentation") as demo:
+    gr.Markdown(
+        "# Semantic Segmentation\n"
+        "Upload an image to see pixel-level classification (21 PASCAL VOC classes).\n"
+        "*Courses: 100 Deep Learning ch3, 360 Autonomous Driving ch4*"
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_image = gr.Image(type="pil", label="Upload Image")
+            mode = gr.Radio(
+                ["Overlay", "Segmentation Only", "Side by Side"],
+                value="Overlay",
+                label="Display Mode",
+            )
+            btn = gr.Button("Segment", variant="primary")
+
+        with gr.Column(scale=2):
+            with gr.Row():
+                overlay_out = gr.Image(label="Result")
+                seg_out = gr.Image(label="Segmentation Map")
+            legend_md = gr.Markdown()
+
+    btn.click(segment, [input_image, mode], [overlay_out, seg_out, legend_md])
+
+    gr.Examples(
+        examples=[
+            ["examples/street.jpg", "Overlay"],
+            ["examples/room.jpg", "Side by Side"],
+        ],
+        inputs=[input_image, mode],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio>=5.0.0
+torch>=2.0.0
+torchvision>=0.15.0
+numpy
+Pillow