I have found the following code on this website:
import os
import os.path
import cv2
import glob
import imutils
CAPTCHA_IMAGE_FOLDER = "generated_captcha_images"
OUTPUT_FOLDER = "extracted_letter_images"
# Get a list of all the captcha images we need to process
captcha_image_files = glob.glob(os.path.join(CAPTCHA_IMAGE_FOLDER, "*"))
counts = {}
# loop over the image paths
for (i, captcha_image_file) in enumerate(captcha_image_files):
print("[INFO] processing image {}/{}".format(i + 1, len(captcha_image_files)))
# Since the filename contains the captcha text (i.e. "2A2X.png" has the text "2A2X"),
# grab the base filename as the text
filename = os.path.basename(captcha_image_file)
captcha_correct_text = os.path.splitext(filename)[0]
# Load the image and convert it to grayscale
image = cv2.imread(captcha_image_file)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Add some extra padding around the image
gray = cv2.copyMakeBorder(gray, 8, 8, 8, 8, cv2.BORDER_REPLICATE)
# threshold the image (convert it to pure black and white)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# find the contours (continuous blobs of pixels) the image
contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Hack for compatibility with different OpenCV versions
contours = contours[0] if imutils.is_cv2() else contours[1]
letter_image_regions = []
# Now we can loop through each of the four contours and extract the letter
# inside of each one
for contour in contours:
# Get the rectangle that contains the contour
(x, y, w, h) = cv2.boundingRect(contour)
# Compare the width and height of the contour to detect letters that
# are conjoined into one chunk
if w / h > 1.25:
# This contour is too wide to be a single letter!
# Split it in half into two letter regions!
half_width = int(w / 2)
letter_image_regions.append((x, y, half_width, h))
letter_image_regions.append((x + half_width, y, half_width, h))
else:
# This is a normal letter by itself
letter_image_regions.append((x, y, w, h))
# If we found more or less than 4 letters in the captcha, our letter extraction
# didn't work correcly. Skip the image instead of saving bad training data!
if len(letter_image_regions) != 4:
continue
# Sort the detected letter images based on the x coordinate to make sure
# we are processing them from left-to-right so we match the right image
# with the right letter
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
# Save out each letter as a single image
for letter_bounding_box, letter_text in zip(letter_image_regions, captcha_correct_text):
# Grab the coordinates of the letter in the image
x, y, w, h = letter_bounding_box
# Extract the letter from the original image with a 2-pixel margin around the edge
letter_image = gray[y - 2:y + h + 2, x - 2:x + w + 2]
# Get the folder to save the image in
save_path = os.path.join(OUTPUT_FOLDER, letter_text)
# if the output directory does not exist, create it
if not os.path.exists(save_path):
os.makedirs(save_path)
# write the letter image to a file
count = counts.get(letter_text, 1)
p = os.path.join(save_path, "{}.png".format(str(count).zfill(6)))
cv2.imwrite(p, letter_image)
# increment the count for the current key
counts[letter_text] = count + 1
When I try to run the code I get the following error:
[INFO] processing image 1/9955
Traceback (most recent call last):
File "extract_single_letters_from_captchas.py", line 47, in <module>
(x, y, w, h) = cv2.boundingRect(contour)
cv2.error: OpenCV(4.0.0) /Users/travis/build/skvark/opencv-python/opencv/modules/imgproc/src/shapedescr.cpp:741: error: (-215:Assertion failed) npoints >= 0 && (depth == CV_32F || depth == CV_32S) in function 'pointSetBoundingRect'
I've tried searching for a solution on StackOverflow, but I didn't find anything remotely similar.
EDIT (see comments):
type(contour[0]) = <class 'numpy.ndarray'>
len(contour) = 4
This is doing the wrong thing:
contours = contours[0] if imutils.is_cv2() else contours[1]
imutils.is_cv2() is returning False even though it should return True. If you don't mind to remove this dependency, change to:
contours = contours[0]
I found out the reason. Probably, the tutorial you are following was published before OpenCV 4 was released. OpenCV 3 changed cv2.findContours(...) to return image, contours, hierarchy, while OpenCV 2's cv2.findContours(...) and OpenCV 4's cv2.findContours(...) return contours, hierarchy. Therefore, before OpenCV 4, it was correct to say that if you use OpenCV 2 it should be contours[0] else contours[1]. If you still want to have this "compatibility", you can change to:
contours = contours[1] if imutils.is_cv3() else contours[0]