for contour in contours:
x, y, width, height = cv2.boundingRect(contour)
area = width * height
area_list.append(area)
width_list.append(width)
height_list.append(height)
return area_list, width_list, height_list
# Reading the images
extracted_img = cv2.imread('extracted_img.jpg')
img2 = cv2.imread('img2_underlined.jpg')
img2 = cv2.resize(img2, (600, 1200))
img = cv2.imread('extracted_img.jpg')
img = crop_img(img)
extracted_img = crop_img(extracted_img)
extracted_img = img2
_, extracted_img = cv2.threshold(extracted_img, 127, 255, cv2.THRESH_BINARY)
extracted_img = cv2.Canny(np.asarray(extracted_img), 100, 200)
_, contours, _ = cv2.findContours(extracted_img, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
al, wl, hl = counters_area_list(contours, extracted_img)
mask = np.zeros(extracted_img.shape, dtype="uint8")
for i, c in enumerate(contours):
"-a",
"--annotated-img",
type=str,
help="please enter the path to the annotated image with extension")
args = parser.parse_args()
original_img_path = args.original_img
annotated_img_path = args.annotated_img
# Loading the images
original_img, annotated_img = api.load_and_resize(original_img_path,
annotated_img_path)
# Cropping and obtining the central text parts from the image
cropped_original = crop_img(original_img)
cv2.imshow('croped', cropped_original)
# Finding the features and matching the images
transform_coordinates = api.orb_flann_matcher(cropped_original, annotated_img)
# Find extra text using the match detected
extra_text_contours = api.detect_extra_text(annotated_img,
transform_coordinates)
# Find the underlines
underline_contours = api.detect_underline(annotated_img, transform_coordinates)
# Convert the images into color
annotated_img = cv2.cvtColor(annotated_img, cv2.COLOR_GRAY2BGR)
# Make a copy of the annotated image
"""
import cv2
import numpy as np
from crop_original import crop_img, find_components
original_img = cv2.imread('img1.jpg', cv2.IMREAD_GRAYSCALE)
annotated_img = cv2.imread('img2_underlined.jpg')
original_img = cv2.resize(original_img, (600, 1200))
annotated_img = cv2.resize(annotated_img, (600, 1200))
final_img = cv2.imread('img2_underlined.jpg')
final_img = cv2.resize(final_img, (600, 1200))
original_img = crop_img(original_img)
gray_annotated_img = cv2.cvtColor(annotated_img, cv2.COLOR_BGR2GRAY)
# Thresholding removes background noise like dust particles and small lines
# that appear while scanning the physical document
# threshold(src.img, threshold-value, max_val, thresholding_type)
#_,original_img = cv2.threshold(original_img,127,255,cv2.THRESH_BINARY)
#_,annotated_img = cv2.threshold(annotated_img,127,255,cv2.THRESH_BINARY)
#sobely = cv2.Sobel(original_img, cv2.CV_64F, 0, 1)
def find_match(original_img, annotated_img):
# Features