def preprocess_images():
auto_crop.crop_to_bounding_boxes(glob.glob('../images/training/*training*'))
listing = os.listdir("../images/temp/")
for file in listing:
img = cv2.imread("../images/temp/" + file, 0)
if file != "training":
if "training" in file and "cropped" in file:
_, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
img = normalize_training_image(img, 30)
tabs = id_tabs(255 - img, 50)
print(tabs)
cv2.imwrite("../images/preprocessed/" + file, img)
# cleanup: remove temp folder
if os.path.exists('../images/temp'):
shutil.rmtree('../images/temp')
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)
