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import pandas as pd
from matplotlib import pyplot as plt
import regex as re
import cv2
import pytesseract
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
def display(im_path):
dpi = 100
im_data = plt.imread(im_path)
height, width = im_data.shape[:2]
figsize = width / float(dpi), height / float(dpi)
# create a figure of the right size with one axes that takes up the full fig
fig = plt.figure(figsize = figsize)
ax = fig.add_axes([0, 0, 1, 1]) # type: ignore
# hide spines, ticks, etc
ax.axis('off')
ax.imshow(im_data, cmap='gray')
plt.show()
return im_data
def getSkewAngle(cvImage) -> float:
# Prep image, copy, convert to gray scale, blur, and threshold
newImage = cvImage.copy()
gray = cv2.cvtColor(newImage, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (9, 9), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Apply dilate to merge text into meaningful lines/paragraphs.
# Use larger kernel on X axis to merge characters into single line, cancelling out any spaces.
# But use smaller kernel on Y axis to separate between different blocks of text
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 5))
dilate = cv2.dilate(thresh, kernel, iterations=2)
# Find all contours
contours, hierarchy = cv2.findContours(dilate, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key = cv2.contourArea, reverse = True)
for c in contours:
rect = cv2.boundingRect(c)
x,y,w,h = rect
cv2.rectangle(newImage,(x,y),(x+w,y+h),(0,255,0),2)
# Find largest contour and surround in min area box
largestContour = contours[0]
#print (len(contours))
minAreaRect = cv2.minAreaRect(largestContour)
cv2.imwrite("temp/boxes.jpg", newImage)
# Determine the angle. Convert it to the value that was originally used to obtain skewed image
angle = minAreaRect[-1]
if angle < -45:
angle = 90 + angle
if angle == 90:
return 0
return -1.0 * angle
# Rotate the image around its center
def rotateImage(cvImage, angle: float):
newImage = cvImage.copy()
(h, w) = newImage.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
newImage = cv2.warpAffine(newImage, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
return newImage
# Deskew image
def deskew(cvImage):
cvImage = cv2.imread(cvImage)
angle = getSkewAngle(cvImage)
return rotateImage(cvImage, angle=angle)
if __name__ == "__main__":
fixed = deskew(r'data/pagina_2_metade.png')
print("data/pagina_2_metade_deskew.png deskewed")
cv2.imwrite(r"data/pagina_2_metade_deskew.png", fixed) |