Spaces:
Sleeping
Sleeping
File size: 3,018 Bytes
f6d6527 |
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 82 83 84 85 86 87 |
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) |