Upload lab1_sky_detector.py

lab1_sky_detector.py

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+# -*- coding: utf-8 -*-
+"""lab1_sky_detector.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1fbwiWdURCSaRB3fbSs0gF88hiFqpJhC1
+"""
+
+!pip install gradio
+
+import gradio as gr
+#from google.colab import files
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+from scipy.signal import medfilt
+
+# upload input image
+#def upload_image():
+#    uploaded = files.upload()
+#    file = next(iter(uploaded))
+#    img = cv2.imread(file)
+#    return img
+
+# display input image
+#def display_image(img, title="Image"):
+#    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # convert from BGR to RGB for OpenCV
+#    plt.imshow(img_rgb)
+#    plt.title(title)
+#    plt.axis("off")
+#    plt.show()
+
+# convert input image to grayscale to prepare for edge detection
+def convert_to_grayscale_and_blur(img):
+    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img_blurred = cv2.blur(img_gray, (5, 5)) # Gaussian blurring with a kernel size of (9, 3) to reduce noise
+    return img_blurred
+
+# detect edges using Laplacian filter
+def calculate_gradient(img_blurred, threshold=7):
+    laplacian = cv2.Laplacian(img_blurred, cv2.CV_8U)
+    gradient_mask = (laplacian < threshold).astype(np.uint8)
+    return gradient_mask
+
+# refine skyline using median filtering and morphological operation
+def refine_skyline(mask):
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
+    eroded_mask = cv2.morphologyEx(mask, cv2.MORPH_ERODE, kernel)
+    skyline_mask = cal_skyline(eroded_mask)
+    return skyline_mask
+
+# adjust skyline using median filtering to isolate the sky
+def cal_skyline(mask):
+    h, w = mask.shape
+    for i in range(w):
+        column = mask[:, i]
+        after_median = medfilt(column, kernel_size=21)
+        try:
+            first_white_index = np.where(after_median == 1)[0][0]
+            first_black_index = np.where(after_median == 0)[0][0]
+            if first_black_index > first_white_index:
+                mask[:first_black_index, i] = 1
+                mask[first_black_index:, i] = 0
+        except IndexError:
+            continue
+    return mask
+
+# extract sky region by applying the mask
+def get_sky_region(img, mask):
+    sky_region = cv2.bitwise_and(img, img, mask=mask)
+    return sky_region
+
+# run in order and show the detected sky region
+#def sky_detector(img):
+#    display_image(img, "Original Image")
+#    img_blurred = convert_to_grayscale_and_blur(img)
+#    gradient_mask = calculate_gradient(img_blurred)
+#    skyline_mask = refine_skyline(gradient_mask)
+#    sky_region = get_sky_region(img, skyline_mask)
+#    display_image(sky_region, "Sky Region")
+
+# main
+#image = upload_image()
+#sky_detector(image)
+
+# run in order for Gardio
+def sky_detection(image):
+    img_blurred = convert_to_grayscale_and_blur(image)
+    gradient_mask = calculate_gradient(img_blurred)
+    skyline_mask = refine_skyline(gradient_mask)
+    sky_region = get_sky_region(image, skyline_mask)
+    sky_region_rgb = cv2.cvtColor(sky_region, cv2.COLOR_BGR2RGB)  # Convert to RGB for Gradio display
+    return sky_region_rgb
+
+# set up Gardio interface
+interface = gr.Interface(fn=sky_detection, inputs=gr.Image(), outputs="image")
+interface.launch(share=True)