Delete sky_segmentation_with_traditional_ML.py

sky_segmentation_with_traditional_ML.py

@@ -1,80 +0,0 @@
-import cv2
-import numpy as np
-import gradio as gr
-
-# Helper function to detect sky condition and get the HSV range
-def detect_sky_color(hsv_image):
-    # Crop the image to the upper half, because we assume the sky is always on the upper half of the image
-    height = hsv_image.shape[0]
-    upper_half_image = hsv_image[:height//2, :]
-
-    # Define color ranges in HSV
-    blue_lower = np.array([46, 17, 148], np.uint8)
-    blue_upper = np.array([154, 185, 249], np.uint8)
-    orange_lower = np.array([10, 100, 100], np.uint8)
-    orange_upper = np.array([25, 183, 254], np.uint8)
-    pale_lower = np.array([0, 0, 129], np.uint8)
-    pale_upper = np.array([171, 64, 225], np.uint8)
-
-    # Create masks for colors
-    blue_mask = cv2.inRange(upper_half_image, blue_lower, blue_upper)
-    orange_mask = cv2.inRange(upper_half_image, orange_lower, orange_upper)
-    pale_mask = cv2.inRange(upper_half_image, pale_lower, pale_upper)
-
-    # Calculate the percentage of cropped image covered by each color
-    blue_percentage = np.sum(blue_mask > 0) / (upper_half_image.shape[0] * upper_half_image.shape[1]) * 100
-    orange_percentage = np.sum(orange_mask > 0) / (upper_half_image.shape[0] * upper_half_image.shape[1]) * 100
-    pale_percentage = np.sum(pale_mask > 0) / (upper_half_image.shape[0] * upper_half_image.shape[1]) * 100
-
-    # Determine the predominant color in the upper half
-    max_color = max(blue_percentage, orange_percentage, pale_percentage)
-    if max_color == blue_percentage:
-        return blue_lower, blue_upper
-    elif max_color == orange_percentage:
-        return orange_lower, orange_upper
-    else:
-        return pale_lower, pale_upper
-
-
-# Main function to process image and display sky masks
-def sky_segmentation(uploaded_image):
-    # Read the image
-    image = cv2.imread(uploaded_image)
-
-    # Convert to HSV image
-    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
-
-    # Determine HSV range based on helper function
-    (hsv_lower, hsv_upper) = detect_sky_color(hsv)
-
-    # Use hsv_lower and hsv_upper to create a mask, which isolates the sky region
-    mask_initial = cv2.inRange(hsv, hsv_lower, hsv_upper)
-
-    # Apply morphological operations to fine-tune the mask
-    kernel = np.ones((3,3), np.uint8)
-    mask_fine_tuned = cv2.erode(mask_initial, kernel, iterations=1)
-    mask_fine_tuned = cv2.dilate(mask_fine_tuned, kernel, iterations=1)
-
-    # Perform connected component analysis
-    num_labels, labels_im = cv2.connectedComponents(mask_fine_tuned)
-
-    # Create an array to hold the size of each component
-    sizes = np.bincount(labels_im.flatten())
-
-    # Set the size of the background (label 0) to zero
-    sizes[0] = 0
-
-    # Find the largest component
-    max_label = np.argmax(sizes)
-
-    # Create a mask with only the largest component
-    sky_mask = np.zeros_like(mask_fine_tuned)
-    sky_mask[labels_im == max_label] = 255    
-    
-    return sky_mask
-
-
-# Create a Gradio demo
-demo = gr.Interface(sky_segmentation, gr.Image(type='filepath'), "image")
-if __name__ == "__main__":
-    demo.launch(share=True)