segment.py

import cv2
import numpy as np

def segment_input(PATH,filename):
    # Insert location of image here
    image = cv2.imread(PATH)
    ret,image = cv2.threshold(image,220,255,cv2.THRESH_BINARY)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

    kernel = np.ones((5, 5), np.uint8)
    img_dilated = cv2.dilate(thresh, kernel, iterations = 1)
    
    cnts, _ = cv2.findContours(img_dilated.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    # cnts, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    # Array of initial bounding rects
    rects = []

    # Bool array indicating which initial bounding rect has
    # already been used
    rectsUsed = []

    # Just initialize bounding rects and set all bools to false
    for cnt in cnts:
        rects.append(cv2.boundingRect(cnt))
        rectsUsed.append(False)

    # Sort bounding rects by x coordinate
    def getXFromRect(item):
        return item[0]

    rects.sort(key = getXFromRect)

    # Array of accepted rects
    acceptedRects = []

    # Merge threshold for x coordinate distance
    xThr = 5

    # Iterate all initial bounding rects
    for supIdx, supVal in enumerate(rects):
        if (rectsUsed[supIdx] == False):

            # Initialize current rect
            currxMin = supVal[0]
            currxMax = supVal[0] + supVal[2]
            curryMin = supVal[1]
            curryMax = supVal[1] + supVal[3]

            # This bounding rect is used
            rectsUsed[supIdx] = True

            # Iterate all initial bounding rects
            # starting from the next
            for subIdx, subVal in enumerate(rects[(supIdx+1):], start = (supIdx+1)):

                # Initialize merge candidate
                candxMin = subVal[0]
                candxMax = subVal[0] + subVal[2]
                candyMin = subVal[1]
                candyMax = subVal[1] + subVal[3]

                # Check if x distance between current rect
                # and merge candidate is small enough
                if (candxMin <= currxMax + xThr):

                    # Reset coordinates of current rect
                    currxMax = candxMax
                    curryMin = min(curryMin, candyMin)
                    curryMax = max(curryMax, candyMax)

                    # Merge candidate (bounding rect) is used
                    rectsUsed[subIdx] = True
                else:
                    break

            # No more merge candidates possible, accept current rect
            acceptedRects.append([currxMin, curryMin, currxMax - currxMin, curryMax - curryMin])

    # for rect in acceptedRects:
        # print(rect[0],rect[1],rect[2],rect[3])
        # img = cv2.rectangle(image, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (121, 11, 189), 2)

    # cv2_imshow(image)
    c=0
    for i in acceptedRects:
        stpt = [i[0], i[1]]
        endpt=[i[0]+i[2], i[1]+i[3]]
        area = (i[2]-i[0]) * (i[3]-i[1])
        cropp= image[stpt[1]:endpt[1], stpt[0]:endpt[0]]
        color = [255, 255, 255]
        pad=100
        new_im = cv2.copyMakeBorder(cropp, pad, pad, pad, pad, cv2.BORDER_CONSTANT,value=color)
        c+=1
        # cv2.imwrite("./images/"+str(filename)+"tmp"+str(c)+".png", new_im)
        # cv2.imwrite("./images/"+str(filename)+"c"+str(c)+".png", cropp)
        cv2.imwrite("./images/"+str(filename)+"c"+str(c)+".png", new_im)
    return c


    #Image will be stored in the same directory as c1.jpg, c2.jpg and so on