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import gradio
import cv2
import numpy as np
def inference(img):
#out = cv2.erode(img,(15,15))
#out = cv2.dilate(out,(55,55))
# https://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.remove_small_objects
# my_result = cv2.remove_small_objects(binarized.astype(bool), min_size=2, connectivity=2).astype(int)
#img_bw = 255*(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) > 5).astype('uint8')
#se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
#se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
#mask = cv2.morphologyEx(img_bw, cv2.MORPH_CLOSE, se1)
#mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2)
#mask = np.dstack([mask, mask, mask]) / 255
#out = img * mask
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY ) # grayscale
#out = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,133,9)
out = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,221,9)
#kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernelSize)
#opening = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)
return out
# For information on Interfaces, head to https://gradio.app/docs/
# For user guides, head to https://gradio.app/guides/
# For Spaces usage, head to https://huggingface.co/docs/hub/spaces
iface = gradio.Interface(
fn=inference,
inputs='image',
outputs='image',
title='Hello World',
description='The simplest interface!',
examples=["detail_with_lines_and_noise.jpg", "lama.webp", "dT4KW.png"])
#examples=["detail_with_lines_and_noise.jpg", "lama.webp", "test_lines.jpg","llama.jpg", "dT4KW.png"])
iface.launch() |