Spaces:
Sleeping
Sleeping
File size: 3,059 Bytes
9d46f10 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
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)
|