Update app.py

app.py

@@ -3,23 +3,22 @@ import requests
 from PIL import Image
 from io import BytesIO
 import numpy as np
-import re
 from landingai.common import decode_bitmap_rle
-import os # For accessing secrets
+import cv2
 
 ENDPOINT_ID = "ba678fa4-65d1-4b87-8c85-cebd15224783"
-API_KEY = os.environ.get("land_sk_ikq7WEKGtaKI7pXIcKt2x7RoyYE6FBReqGOmKtEhjcmFbLbQsK")
+API_KEY = "land_sk_ikq7WEKGtaKI7pXIcKt2x7RoyYE6FBReqGOmKtEhjcmFbLbQsK"
 API_URL = f"https://predict.app.landing.ai/inference/v1/predict?endpoint_id={ENDPOINT_ID}"
 
 def predict_from_landinglens(image_path):
     # Load and keep original image
     original_img = Image.open(image_path).convert("RGB")
     img_array = np.array(original_img)
-    
+
     # Get image dimensions
     height, width = img_array.shape[:2]
     total_pixels = height * width
-    
+
     # Prepare for API
     buffered = BytesIO()
     original_img.save(buffered, format="JPEG")
@@ -49,24 +48,27 @@ def predict_from_landinglens(image_path):
                 mask = decode_bitmap_rle(bitmap_data["bitmap"])
                 if isinstance(mask, list):
                     mask = np.array(mask)
-                
+
                 # Reshape mask to match image dimensions
-                mask = mask.reshape(prediction["predictions"]["imageHeight"], 
+                mask = mask.reshape(prediction["predictions"]["imageHeight"],
                                   prediction["predictions"]["imageWidth"])
-                
+
                 # Calculate area coverage
                 mask_area = np.sum(mask > 0)
                 coverage_percentage = (mask_area / total_pixels) * 100
                 label_name = bitmap_data.get("label_name", f"Mask {i}")
                 coverage_info.append(f"{label_name}: {coverage_percentage:.2f}%")
 
-                
-                
-                # Create colored overlay using Pillow
-                mask_image = Image.fromarray((mask > 0).astype(np.uint8) * 255)
-                overlay = Image.new("RGB", mask_image.size, (255, 0, 0))  # Red overlay
-                mask_image = mask_image.convert("RGB")
-                masked_image = Image.blend(original_img, overlay, alpha=0.5 * mask_image.convert("L").point(lambda x: x > 0 and 1))
+                # Create colored overlay
+                colored_mask = np.zeros_like(img_array)
+                colored_mask[mask > 0] = [255, 0, 0]  # Red overlay for mask
+
+                # Combine original image with colored mask
+                alpha = 0.5  # Transparency of the overlay
+                combined = cv2.addWeighted(img_array, 1, colored_mask, alpha, 0)
+
+                # Convert to PIL Image
+                masked_image = Image.fromarray(combined)
                 masked_images.append(masked_image)
 
             except Exception as e:
@@ -88,4 +90,6 @@ iface = gr.Interface(
     ],
     title="Crosswalk detection model",
 )
-iface.launch()
+
+if __name__ == "__main__":
+    iface.launch()