Update README.md

README.md

@@ -1,3 +1,58 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+```python
+# Example Code: Try on google colab
+
+# Install required libraries
+!pip install ultralytics --quiet
+!pip install huggingface_hub --quiet
+import cv2
+import matplotlib.pyplot as plt
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+from google.colab import files
+import os
+
+# Download the YOLO model from Hugging Face
+model_path = hf_hub_download(repo_id="krishnamishra8848/Road_Detection", filename="best.pt")
+
+# Load the YOLO model
+model = YOLO(model_path)
+
+# Upload a photo
+print("Please upload an image:")
+uploaded = files.upload()
+
+for filename in uploaded.keys():
+    # Read the uploaded image
+    image = cv2.imread(filename)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Perform inference
+    results = model(image)
+
+    # Draw bounding boxes and class names
+    for result in results[0].boxes:
+        box = result.xyxy[0].cpu().numpy()  # Bounding box (x_min, y_min, x_max, y_max)
+        cls = int(result.cls[0].cpu().numpy())  # Class ID
+        conf = result.conf[0].cpu().numpy()  # Confidence score
+        label = f"{model.names[cls]}: {conf:.2f}"  # Label with class name and confidence
+
+        # Draw the bounding box
+        cv2.rectangle(image_rgb, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 2)
+
+        # Draw the class name and confidence score
+        cv2.putText(image_rgb, label, (int(box[0]), int(box[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+    # Display the image with bounding boxes
+    plt.figure(figsize=(10, 10))
+    plt.imshow(image_rgb)
+    plt.axis('off')
+    plt.show()
+
+    # Save the processed image
+    output_filename = "output_" + filename
+    cv2.imwrite(output_filename, cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR))
+    print(f"Processed image saved as {output_filename}")