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)