def id_tabs(img, avg_line_height=20, line_blur=20, tab_wiggle_room=5, disp=False):
"""
Attempts to identify the indent level of each line of text, with the assumption that the first line is at level 0.
:param img: the input image (should contain text)
:param avg_line_height: the expected vertical height of a line of text
:param line_blur: how far the image is blurred to extract features (img.width/line_blur)
:param tab_wiggle_room: how far in pixels tabs are allowed to be from on another before they are considered distinct
:param disp: whether to display intermediate results
:return: An integer list representing the tab level for each line
"""
# load image as grayscale
# aggressively horizontally blur the image
r, c = len(img), len(img[0])
horizontal_size = c / line_blur
horizontal_structure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontal_size, 1))
img = cv2.filter2D(img, -1, horizontal_structure)
if disp:
vis_img, _ = auto_crop.reduce_image(img.copy())
cv2.imshow('Horizontally Blur', vis_img)
cv2.waitKey(0)
# Identify connected components & generate bounding boxes
n, regions = cv2.connectedComponents(img, img)
img = np.uint8(regions)
bbs = _generate_bounding_boxes(img, n, avg_line_height)
return _analyze_bounding_boxes(bbs, tab_wiggle_room)
def main():
disp = False # Set to true to view pre-processing of the images.
img = cv2.imread('../images/training/training3.jpg', 0)
img = auto_crop.crop_to_bounding_box(img, disp=disp)
# binarize image
_, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
if disp:
vis_img, _ = auto_crop.reduce_image(img.copy())
cv2.imshow('Binary Image', vis_img)
cv2.waitKey(0)
img = 255 - img
if disp:
vis_img, _ = auto_crop.reduce_image(img.copy())
cv2.imshow('Inverted', vis_img)
cv2.waitKey(0)
# normalize image height
img, avg_line_height = normalize_training_image(img, 30, disp=disp)
if disp:
cv2.imshow('Normalized', img)
cv2.waitKey(0)
cv2.waitKey(0)
# identify indent levels
tabs = id_tabs(255 - img,
avg_line_height=avg_line_height,
line_blur=20,
tab_wiggle_room=2,
disp=True)
print tabs
cv2.imwrite("text.png", img)
pil_img = Image.open("text.png")
translation = pytesseract.image_to_string(pil_img, 'hww')
print "TESSERACT RESULTS: "
print translation
print "\n\nPROOFREAD: "
print proofread(translation.split('\n'), tabs)
def normalize_training_image(img, threshold_height, disp=False):
"""
Takes in a binary image and normalizes the text within the image to the given height.
:param img: the image to normalize
:param threshold_height: the average height we're trying to obtain
:param disp: whether to display intermediate results
"""
img = _remove_circles(img)
if disp:
vis_img, _ = reduce_image(img.copy())
cv2.imshow('Removed Bounding Circles', vis_img)
cv2.waitKey(0)
cc = _horizontally_blur_image(img)
_, cc = cv2.threshold(cc, 1, 255, cv2.THRESH_BINARY)
_, contours, hierarchy = cv2.findContours(cc, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
avg_height = 0
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
avg_height += h
avg_height /= len(contours)
parsed_contours = []
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
if h >= avg_height / 2:
parsed_contours.append(cnt)
new_avg_height = 0
for cnt in parsed_contours:
x, y, w, h = cv2.boundingRect(cnt)
new_avg_height += h
new_avg_height /= len(parsed_contours)
p = threshold_height / new_avg_height
height, width = img.shape
dim = (int(width * p), int(height * p))
return cv2.resize(img, dim), threshold_height
