File size: 2,251 Bytes
2ec286a 7d4cb84 2ec286a 7d4cb84 2ec286a 9e0ee47 7d4cb84 2ec286a |
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 |
from cv2 import threshold
import keras_ocr
import cv2
import math
import numpy as np
import gradio as gr
def midpoint(x1, y1, x2, y2):
x_mid = int((x1 + x2)/2)
y_mid = int((y1 + y2)/2)
return (x_mid, y_mid)
def segment_img(img):
hsv=cv2.cvtColor(img,cv2.COLOR_RGB2HSV)
#mask
mask=cv2.inRange(hsv,(40,25,25),(70,255,255))
imask=mask>0
threshold=np.zeros_like(img,np.uint8)
threshold[imask]=img[imask]
return threshold
#Main function that detects text and inpaints.
#Inputs are the image path and kreas_ocr pipeline
def inpaint_text(img_path, pipeline):
# read the image
img = keras_ocr.tools.read(img_path)
#img=segment_img(img)
# Recogize text (and corresponding regions)
# Each list of predictions in prediction_groups is a list of
# (word, box) tuples.
prediction_groups = pipeline.recognize([img])
#Define the mask for inpainting
mask = np.zeros(img.shape[:2], dtype="uint8")
for box in prediction_groups[0]:
x0, y0 = box[1][0]
x1, y1 = box[1][1]
x2, y2 = box[1][2]
x3, y3 = box[1][3]
x_mid0, y_mid0 = midpoint(x1, y1, x2, y2)
x_mid1, y_mi1 = midpoint(x0, y0, x3, y3)
#For the line thickness, we will calculate the length of the line between
#the top-left corner and the bottom-left corner.
thickness = int(math.sqrt( (x2 - x1)**2 + (y2 - y1)**2 ))
#Define the line and inpaint
cv2.line(mask, (x_mid0, y_mid0), (x_mid1, y_mi1), 255,
thickness)
inpainted_img = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS)
return (inpainted_img)
# keras-ocr will automatically download pretrained
# weights for the detector and recognizer.
pipeline = keras_ocr.pipeline.Pipeline()
def RemoveText(image):
img_text_removed = inpaint_text(image, pipeline)
return cv2.cvtColor(img_text_removed, cv2.COLOR_BGR2RGB)
iface = gr.Interface(fn=RemoveText,
inputs=gr.inputs.Image(label="Image to Remove Text From", type="numpy"),
outputs="image",
examples=[["1.jpg"]],
title="Remove Text for Image")
iface.launch(debug=True)
|