bounding_boxes-v2.py

'''
This script puts boxes around text given an image (excluding text found near the margins).

image_filepath can be an image file of a page from a pdf file.
output_file should be an image file or filepath.

To run this script:
    python3 bounding_boxes-v2.py image_filepath {OUTPUT_FILE=}[filename] X_MIN=[INT] Y_MIN=[INT]

(The arguments OUTPUT_FILE, X_MIN, and Y_MIN, are optional, and the OUTPUT_FILE argument can be anywhere after the image_filepath and does not even need to be labeled with OUTPUT_FILE= in front of it.)
'''

# Packages to import
import sys
import cv2  # OpenCV, for identifying "structure"

print(sys.argv)

image_filepath = sys.argv[1]
default_output_file = "temp/index_boundingBoxes.png"

for item in sys.argv[2:]:

    if item[0:len("X_MIN=")] == "X_MIN=":
        x_min_distance = item[len("X_MIN="):]
        x_min_distance = int(x_min_distance)
    else:
        x_min_distance = 130

    if item[0:len("Y_MIN=")] == "Y_MIN=":
        y_min_distance = item[len("Y_MIN="):]
        y_min_distance = int(y_min_distance)
    else:
        y_min_distance = x_min_distance  # 150 seems to work well too

# If 2 argvs or fewer, there cannot be an output argv there.
output_file = ""
if len(sys.argv) < 3:
    output_file += default_output_file

elif len(sys.argv) >= 3:

    for item in sys.argv[3:]:
        if item[0:len("OUTPUT_FILE=")] == "OUTPUT_FILE=":
            output_file += str(item[len("OUTPUT_FILE"):])

    if not sys.argv[2][0:len("X_MIN=")] == "X_MIN=":
        if not sys.argv[2][0:len("Y_MIN=")] == "Y_MIN=":
            output_file += str(sys.argv[2])

    if output_file == "":
        output_file += default_output_file

# Printing arguments
print("------------------------------------")
print(f"Image filepath defined as {image_filepath}")
print(f"X_MIN distance defined as {x_min_distance}")
print(f"Y_MIN distance defined as {y_min_distance}")
print(f"OUTPUT_FILE defined as {output_file}")
print("------------------------------------")

# Reading image from filepath
original_image = cv2.imread(image_filepath)
x_length, y_length, z_length = original_image.shape
# x_length = original_image.shape[0]
# y_length = original_image.shape[1]
print(f"Length of x-axis on the original image: {x_length}")
print(f"Length of y-axis on the original image: {y_length}")
print(f"Length of z-axis(?) on the original image: {z_length}")

# Cropping doesn't seem to help with the purpose we have in mind, but it does work here if you want to test it:
# cropped_image = original_image[min_distance:x_length -
# min_distance, min_distance:y_length-min_distance]
# cv2.imwrite("CroppedImage.png", cropped_image)

# Preprocessing for Structure (not individual characters)
# grayscale
# grayscaled_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
grayscaled_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
cv2.imwrite("temp/index_gray.png", grayscaled_image)

# blur
# reminder: look into these parameters further
blurred_image = cv2.GaussianBlur(grayscaled_image, (7, 7), 0)  # 7-by-7 size
cv2.imwrite("temp/index_blur.png", blurred_image)

# thresh
# makes a black image with white text
# reminder2: look into these parameters further too
threshold_image = cv2.threshold(
    blurred_image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
cv2.imwrite("temp/index_threshold.png", threshold_image)

# kernels and dilation (convolutions)
# this "kernel" version should not be visible
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 13))
# cv2.imwrite("temp/index_kernel.png", kernel)
after_convolutions = cv2.dilate(threshold_image, kernel, iterations=1)
cv2.imwrite("temp/dilated.png", after_convolutions)

# Making the bounding boxes themselves now that we have the structure more clear
contours = cv2.findContours(
    after_convolutions, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# the number of contours you're looking for should be the number that len(contours should end up with)
contours = contours[0] if len(contours) == 2 else contours[1]
contours = sorted(contours, key=lambda x: cv2.boundingRect(x)[1])

# Adding conditions to the rectangles made
for contour in contours:
    xValue, yValue, width, height = cv2.boundingRect(contour)

    if height < 200 and xValue in range(y_min_distance, x_length - x_min_distance) and yValue in range(y_min_distance, y_length):
        cv2.rectangle(original_image, (xValue, yValue),
                      (xValue+width, yValue+height), (0, 255, 0), 2)

# Output
cv2.imwrite(output_file, original_image)
print(f"Wrote image to {output_file}.")