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"/Users/yunkeli/anaconda3/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
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"text": [
"Running on local URL: http://127.0.0.1:7860\n",
"Running on public URL: https://a2d6699e89843c017c.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
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"<div><iframe src=\"https://a2d6699e89843c017c.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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"<IPython.core.display.HTML object>"
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"source": [
"import cv2\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"import gradio as gr\n",
"\n",
"# Helper function to detect sky condition and get the HSV range\n",
"def detect_sky_color(hsv_image):\n",
" # Crop the image to the upper half, because we assume the sky is always on the upper half of the image\n",
" height = hsv_image.shape[0]\n",
" upper_half_image = hsv_image[:height//2, :]\n",
"\n",
" # Define color ranges in HSV\n",
" blue_lower = np.array([46, 17, 148], np.uint8)\n",
" blue_upper = np.array([154, 185, 249], np.uint8)\n",
" orange_lower = np.array([10, 100, 100], np.uint8)\n",
" orange_upper = np.array([25, 183, 254], np.uint8)\n",
" pale_lower = np.array([0, 0, 129], np.uint8)\n",
" pale_upper = np.array([171, 64, 225], np.uint8)\n",
"\n",
" # Create masks for colors\n",
" blue_mask = cv2.inRange(upper_half_image, blue_lower, blue_upper)\n",
" orange_mask = cv2.inRange(upper_half_image, orange_lower, orange_upper)\n",
" pale_mask = cv2.inRange(upper_half_image, pale_lower, pale_upper)\n",
"\n",
" # Calculate the percentage of cropped image covered by each color\n",
" blue_percentage = np.sum(blue_mask > 0) / (upper_half_image.shape[0] * upper_half_image.shape[1]) * 100\n",
" orange_percentage = np.sum(orange_mask > 0) / (upper_half_image.shape[0] * upper_half_image.shape[1]) * 100\n",
" pale_percentage = np.sum(pale_mask > 0) / (upper_half_image.shape[0] * upper_half_image.shape[1]) * 100\n",
"\n",
" # Determine the predominant color in the upper half\n",
" max_color = max(blue_percentage, orange_percentage, pale_percentage)\n",
" if max_color == blue_percentage:\n",
" return blue_lower, blue_upper\n",
" elif max_color == orange_percentage:\n",
" return orange_lower, orange_upper\n",
" else:\n",
" return pale_lower, pale_upper\n",
"\n",
"\n",
"# Main function to process image and display sky masks\n",
"def sky_segmentation(uploaded_image):\n",
" # Read the image\n",
" image = cv2.imread(uploaded_image)\n",
"\n",
" # Convert to HSV image\n",
" hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n",
"\n",
" # Determine HSV range based on helper function\n",
" (hsv_lower, hsv_upper) = detect_sky_color(hsv)\n",
"\n",
" # Use hsv_lower and hsv_upper to create a mask, which isolates the sky region\n",
" mask_initial = cv2.inRange(hsv, hsv_lower, hsv_upper)\n",
"\n",
" # Apply morphological operations to fine-tune the mask\n",
" kernel = np.ones((3,3), np.uint8)\n",
" mask_fine_tuned = cv2.erode(mask_initial, kernel, iterations=1)\n",
" mask_fine_tuned = cv2.dilate(mask_fine_tuned, kernel, iterations=1)\n",
"\n",
" # Perform connected component analysis\n",
" num_labels, labels_im = cv2.connectedComponents(mask_fine_tuned)\n",
"\n",
" # Create an array to hold the size of each component\n",
" sizes = np.bincount(labels_im.flatten())\n",
"\n",
" # Set the size of the background (label 0) to zero\n",
" sizes[0] = 0\n",
"\n",
" # Find the largest component\n",
" max_label = np.argmax(sizes)\n",
"\n",
" # Create a mask with only the largest component\n",
" sky_mask = np.zeros_like(mask_fine_tuned)\n",
" sky_mask[labels_im == max_label] = 255 \n",
" \n",
" return sky_mask\n",
"\n",
"\n",
"# Create a Gradio demo\n",
"demo = gr.Interface(sky_segmentation, gr.Image(type='filepath'), \"image\")\n",
"if __name__ == \"__main__\":\n",
" demo.launch(share=True)\n"
]
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