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8386270 98d3fb1 f240077 8386270 43cb5ef ddb49c7 43cb5ef 8386270 98d3fb1 8386270 0d73d25 ddb49c7 8386270 ddb49c7 746d2d7 ddb49c7 8386270 5a853eb 8386270 5a853eb 8386270 5a853eb 8386270 5a853eb 8386270 | 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 | import gradio as gr
import cv2
import numpy as np
from PIL import Image
def convolve(img, kernel):
out_img = cv2.filter2D(src=img, ddepth =-1, kernel=kernel)
out_img = cv2.cvtColor(out_img, cv2.COLOR_BGR2RGB)
out_img = cv2.resize(out_img, (226,226))
return Image.fromarray(out_img)
def app(img, filter):
sobel_verticle = np.array([[-1,0,1],
[-1,0,1],
[-1,0,1]])
sobel_horizontal = np.array([[-1,-1,-1],
[ 0, 0, 0],
[ 1, 1, 1]])
laplacian = np.array([[ 0,-1, 0],
[-1, 5,-1],
[ 0,-1, 0]])
average = np.array([[1/9,1/9, 1/9],
[1/9,1/9, 1/9],
[1/9,1/9, 1/9]])
weighted_average = np.array([[1/16,2/16, 1/16],
[2/16,4/16, 2/16],
[1/16,2/16, 1/16]])
if filter =="sobel_verticle":
return convolve(img, sobel_verticle)
elif filter =="sobel_horizontal":
return convolve(img, sobel_horizontal)
elif filter == "laplacian":
return convolve(img, laplacian)
elif filter == "average":
return convolve(img, average)
else:
return convolve(img, weighted_average)
demo = gr.Interface(
app,
[
gr.Image(shape=(226, 226)),
gr.Radio(["sobel_verticle", "sobel_horizontal", "laplacian", "average", "weighted_average"])
],
gr.Image(shape=(226, 226)),
)
demo.launch()
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