Box extraction using opencv

README

@@ -0,0 +1,12 @@
+
+When you are working with Optical character recognition(OCR) or any data or 
+object recognition problem, the first thing to do is preprocessing. Here preprocessing means
+to extract the location where our information is located. After extracting the location,
+any machine algorithm will be performed on that image. 
+
+The problem arises when you have to detect objects which are located in any tables/boxes or
+in row-column format. If the image is like this then you have to detect boxes and extract them one by one. 
+Now it should be done accurately for all images.
+
+This algorithm helps to detect every boxes accurately and save it in a "Cropped" folder.The code is shown in box_detection.py 
+and the test image is "41.jpg".

box_detection.py

@@ -0,0 +1,90 @@
+import cv2
+import numpy as np
+
+def sort_contours(cnts, method="left-to-right"):
+    # initialize the reverse flag and sort index
+    reverse = False
+    i = 0
+
+    # handle if we need to sort in reverse
+    if method == "right-to-left" or method == "bottom-to-top":
+        reverse = True
+
+    # handle if we are sorting against the y-coordinate rather than
+    # the x-coordinate of the bounding box
+    if method == "top-to-bottom" or method == "bottom-to-top":
+        i = 1
+
+    # construct the list of bounding boxes and sort them from top to
+    # bottom
+    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
+    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
+                                        key=lambda b: b[1][i], reverse=reverse))
+
+    # return the list of sorted contours and bounding boxes
+    return (cnts, boundingBoxes)
+
+def box_extraction(img_for_box_extraction_path, cropped_dir_path):
+
+    img = cv2.imread(img_for_box_extraction_path, 0)  # Read the image
+    (thresh, img_bin) = cv2.threshold(img, 128, 255,
+                                      cv2.THRESH_BINARY | cv2.THRESH_OTSU)  # Thresholding the image
+    img_bin = 255-img_bin  # Invert the image
+
+    cv2.imwrite("Image_bin.jpg",img_bin)
+
+    # Defining a kernel length
+    kernel_length = np.array(img).shape[1]//40
+
+    # A verticle kernel of (1 X kernel_length), which will detect all the verticle lines from the image.
+    verticle_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_length))
+    # A horizontal kernel of (kernel_length X 1), which will help to detect all the horizontal line from the image.
+    hori_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_length, 1))
+    # A kernel of (3 X 3) ones.
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
+
+    # Morphological operation to detect verticle lines from an image
+    img_temp1 = cv2.erode(img_bin, verticle_kernel, iterations=3)
+    verticle_lines_img = cv2.dilate(img_temp1, verticle_kernel, iterations=3)
+    cv2.imwrite("verticle_lines.jpg",verticle_lines_img)
+
+    # Morphological operation to detect horizontal lines from an image
+    img_temp2 = cv2.erode(img_bin, hori_kernel, iterations=3)
+    horizontal_lines_img = cv2.dilate(img_temp2, hori_kernel, iterations=3)
+    cv2.imwrite("horizontal_lines.jpg",horizontal_lines_img)
+
+    # Weighting parameters, this will decide the quantity of an image to be added to make a new image.
+    alpha = 0.5
+    beta = 1.0 - alpha
+    # This function helps to add two image with specific weight parameter to get a third image as summation of two image.
+    img_final_bin = cv2.addWeighted(verticle_lines_img, alpha, horizontal_lines_img, beta, 0.0)
+    img_final_bin = cv2.erode(~img_final_bin, kernel, iterations=2)
+    (thresh, img_final_bin) = cv2.threshold(img_final_bin, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+
+    # For Debugging
+    # Enable this line to see verticle and horizontal lines in the image which is used to find boxes
+    cv2.imwrite("img_final_bin.jpg",img_final_bin)
+    # Find contours for image, which will detect all the boxes
+    im2, contours, hierarchy = cv2.findContours(
+        img_final_bin, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    # Sort all the contours by top to bottom.
+    (contours, boundingBoxes) = sort_contours(contours, method="top-to-bottom")
+
+    idx = 0
+    for c in contours:
+        # Returns the location and width,height for every contour
+        x, y, w, h = cv2.boundingRect(c)
+
+        # If the box height is greater then 20, widht is >80, then only save it as a box in "cropped/" folder.
+        if (w > 80 and h > 20) and w > 3*h:
+            idx += 1
+            new_img = img[y:y+h, x:x+w]
+            cv2.imwrite(cropped_dir_path+str(idx) + '.png', new_img)
+
+    # For Debugging
+    # Enable this line to see all contours.
+    # cv2.drawContours(img, contours, -1, (0, 0, 255), 3)
+    # cv2.imwrite("./Temp/img_contour.jpg", img)
+
+
+box_extraction("41.jpg", "./Cropped/")