Initial YOLOv8 measurement Space

app.py

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+import gradio as gr, numpy as np, cv2
+from PIL import Image
+from ultralytics import YOLO
+
+# 1)  Use Ultralytics' built-in small model (22 MB) – no extra download
+model = YOLO("yolov8s.pt")          # auto-downloads first run
+
+REF_WIDTH_CM = 10.0                 # width of calibration card
+
+def measure(image: Image.Image) -> Image.Image:
+    frame = np.array(image)
+    res   = model(frame, verbose=False)[0]
+    boxes = res.boxes.xyxy.cpu().numpy()
+
+    if len(boxes) < 2:              # need statue + card
+        return image
+
+    areas   = (boxes[:,2]-boxes[:,0]) * (boxes[:,3]-boxes[:,1])
+    ref_idx = int(np.argmin(areas))      # smallest = card
+    obj_idx = int(np.argmax(areas))      # largest  = statue
+
+    x1r,y1r,x2r,y2r = boxes[ref_idx]
+    x1o,y1o,x2o,y2o = boxes[obj_idx]
+
+    px_per_cm = (x2r - x1r) / REF_WIDTH_CM
+    w_cm  = round((x2o - x1o) / px_per_cm, 2)
+    h_cm  = round((y2o - y1o) / px_per_cm, 2)
+
+    canvas = frame.copy()
+    cv2.rectangle(canvas, (int(x1o),int(y1o)), (int(x2o),int(y2o)), (0,255,0), 2)
+    cv2.rectangle(canvas, (int(x1r),int(y1r)), (int(x2r),int(y2r)), (0,0,255), 1)
+    cv2.putText(canvas,
+                f"{w_cm} × {h_cm} cm",
+                (int(x1o), int(max(y1o-10,0))),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0,255,0), 2, cv2.LINE_AA)
+    return Image.fromarray(canvas)
+
+demo = gr.Interface(
+    measure,
+    gr.Image(type="pil", label="Upload studio photo (10 cm card bottom-left)"),
+    gr.Image(type="pil", label="BBox + cm overlay"),
+    title="SmartDimension – YOLOv8 demo",
+    description="Detects gilded statues, finds 10 cm reference card, outputs real-world dimensions."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+torch
+torchvision
+ultralytics
+opencv-python
+Pillow
+huggingface-hub