import argparse
import cv2
import numpy as np
# When the four corners are identified, we will do a four-point
# perspective transform such that the outmost points of each
# corner map to a TRANSF_SIZE x TRANSF_SIZE square image.
TRANSF_SIZE = 512
#
# Answer sheet properties.
#
N_QUESTIONS = 10
ANSWER_SHEET_WIDTH = 740
ANSWER_SHEET_HEIGHT = 1049
ANSWER_PATCH_HEIGHT = 50
ANSWER_PATCH_HEIGHT_WITH_MARGIN = 80
ANSWER_PATCH_LEFT_MARGIN = 200
ANSWER_PATCH_RIGHT_MARGIN = 90
FIRST_ANSWER_PATCH_TOP_Y = 200
ALTERNATIVE_HEIGHT = 50
ALTERNATIVE_WIDTH = 50
ALTERNATIVE_WIDTH_WITH_MARGIN = 100
def calculate_contour_features(contour):
"""Calculates interesting properties (features) of a contour.
We use these features to match shapes (contours). In this script,
we are interested in finding shapes in our input image that look like
a corner. We do that by calculating the features for many contours
in the input image and comparing these to the features of the corner
contour. By design, we know exactly what the features of the real corner
contour look like - check out the calculate_corner_features function.
It is crucial for these features to be invariant both to scale and rotation.
In other words, we know that a corner is a corner regardless of its size
or rotation. In the past, this script implemented its own features, but
OpenCV offers much more robust scale and rotational invariant features
out of the box - the Hu moments.
"""
moments = cv2.moments(contour)
return cv2.HuMoments(moments)
def calculate_corner_features():
"""Calculates the array of features for the corner contour.
In practice, this can be pre-calculated, as the corners are constant
and independent from the inputs.
We load the img/corner.png file, which contains a single corner, so we
can reliably extract its features. We will use these features to look for
contours in our input image that look like a corner.
"""
corner_img = cv2.imread('img/corner.png')
corner_img_gray = cv2.cvtColor(corner_img, cv2.COLOR_BGR2GRAY)
contours, hierarchy = cv2.findContours(
corner_img_gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# We expect to see only two contours:
# - The "outer" contour, which wraps the whole image, at hierarchy level 0
# - The corner contour, which we are looking for, at hierarchy level 1
# If in trouble, one way to check what's happening is to draw the found contours
# with cv2.drawContours(corner_img, contours, -1, (255, 0, 0)) and try and find
# the correct corner contour by drawing one contour at a time. Ideally, this
# would not be done at runtime.
if len(contours) != 2:
raise RuntimeError(
'Did not find the expected contours when looking for the corner')
# Following our assumptions as stated above, we take the contour that has a parent
# contour (that is, it is _not_ the outer contour) to be the corner contour.
# If in trouble, verify that this contour is the corner contour with
# cv2.drawContours(corner_img, [corner_contour], -1, (255, 0, 0))
corner_contour = next(ct
for i, ct in enumerate(contours)
if hierarchy[0][i][3] != -1)
return calculate_contour_features(corner_contour)
def normalize(im):
"""Converts `im` to black and white.
Applying a threshold to a grayscale image will make every pixel either
fully black or fully white. Before doing so, a common technique is to
get rid of noise (or super high frequency color change) by blurring the
grayscale image with a Gaussian filter."""
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# Filter the grayscale image with a 3x3 kernel
blurred = cv2.GaussianBlur(im_gray, (3, 3), 0)
# Applies a Gaussian adaptive thresholding. In practice, adaptive thresholding
# seems to work better than appling a single, global threshold to the image.
# This is particularly important if there could be shadows or non-uniform
# lighting on the answer sheet. In those scenarios, using a global thresholding
# technique might yield paricularly bad results.
# The choice of the parameters blockSize = 77 and C = 10 is as much as an art
# as a science and domain-dependand.
# In practice, you might want to try different values for your specific answer
# sheet.
return cv2.adaptiveThreshold(
blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 77, 10)
def get_approx_contour(contour, tol=.01):
"""Gets rid of 'useless' points in the contour."""
epsilon = tol * cv2.arcLength(contour, True)
return cv2.approxPolyDP(contour, epsilon, True)
def get_contours(image_gray):
contours, _ = cv2.findContours(
image_gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
return map(get_approx_contour, contours)
def get_corners(contours):
"""Returns the 4 contours that look like a corner the most.
In the real world, we cannot assume that the corners will always be present,
and we likely need to decide how good is good enough for contour to
look like a corner.
This is essentially a classification problem. A good approach would be
to train a statistical classifier model and apply it here. In our little
exercise, we assume the corners are necessarily there."""
corner_features = calculate_corner_features()
return sorted(
contours,
key=lambda c: features_distance(
corner_features,
calculate_contour_features(c)))[:4]
def get_bounding_rect(contour):
rect = cv2.minAreaRect(contour)
box = cv2.boxPoints(rect)
return np.int0(box)
def features_distance(f1, f2):
return np.linalg.norm(np.array(f1) - np.array(f2))
def draw_point(point, img, radius=5, color=(0, 0, 255)):
cv2.circle(img, tuple(point), radius, color, -1)
def get_centroid(contour):
m = cv2.moments(contour)
x = int(m["m10"] / m["m00"])
y = int(m["m01"] / m["m00"])
return (x, y)
def sort_points_counter_clockwise(points):
origin = np.mean(points, axis=0)
def positive_angle(p):
x, y = p - origin
ang = np.arctan2(y, x)
return 2 * np.pi + ang if ang < 0 else ang
return sorted(points, key=positive_angle)
def get_outmost_points(contours):
all_points = np.concatenate(contours)
return get_bounding_rect(all_points)
def perspective_transform(img, points):
"""Applies a 4-point perspective transformation in `img` so that `points`
are the new corners."""
source = np.array(
points,
dtype="float32")
dest = np.array([
[TRANSF_SIZE, TRANSF_SIZE],
[0, TRANSF_SIZE],
[0, 0],
[TRANSF_SIZE, 0]],
dtype="float32")
transf = cv2.getPerspectiveTransform(source, dest)
warped = cv2.warpPerspective(img, transf, (TRANSF_SIZE, TRANSF_SIZE))
return warped
def sheet_coord_to_transf_coord(x, y):
return list(map(lambda n: int(np.round(n)), (
TRANSF_SIZE * x / ANSWER_SHEET_WIDTH,
TRANSF_SIZE * y / ANSWER_SHEET_HEIGHT
)))
def get_question_patch(transf, question_index):
"""Exracts a region of interest (ROI) of a single question."""
# Top left of question patch q_number
tl = sheet_coord_to_transf_coord(
ANSWER_PATCH_LEFT_MARGIN,
FIRST_ANSWER_PATCH_TOP_Y + ANSWER_PATCH_HEIGHT_WITH_MARGIN * question_index
)
# Bottom right of question patch q_number
br = sheet_coord_to_transf_coord(
ANSWER_SHEET_WIDTH - ANSWER_PATCH_RIGHT_MARGIN,
FIRST_ANSWER_PATCH_TOP_Y +
ANSWER_PATCH_HEIGHT +
ANSWER_PATCH_HEIGHT_WITH_MARGIN * question_index
)
return transf[tl[1]:br[1], tl[0]:br[0]]
def get_question_patches(transf):
for i in range(N_QUESTIONS):
yield get_question_patch(transf, i)
def get_alternative_patches(question_patch):
for i in range(5):
x0, _ = sheet_coord_to_transf_coord(ALTERNATIVE_WIDTH_WITH_MARGIN * i, 0)
x1, _ = sheet_coord_to_transf_coord(ALTERNATIVE_WIDTH +
ALTERNATIVE_WIDTH_WITH_MARGIN * i, 0)
yield question_patch[:, x0:x1]
def draw_marked_alternative(question_patch, index):
cx, cy = sheet_coord_to_transf_coord(
ALTERNATIVE_WIDTH * (2 * index + .5),
ALTERNATIVE_HEIGHT / 2)
draw_point((cx, cy), question_patch, radius=5, color=(255, 0, 0))
def get_marked_alternative(alternative_patches):
"""Decides which alternative is marked, if any.
Given a list of alternative patches, we need to decide which one,
if any, is marked. Here, we do a simple, hacky heuristic: the
alternative patch with lowest brightness (darker), is marked if
it is sufficiently darker than the _second_ darker alternative
patch.
In practice, a more robust, data-driven model is necessary."""
means = list(map(np.mean, alternative_patches))
sorted_means = sorted(means)
# Simple heuristic
if sorted_means[0]/sorted_means[1] > .7:
return None
return np.argmin(means)
def get_letter(alt_index):
return ["A", "B", "C", "D", "E"][alt_index] if alt_index is not None else "N/A"
def get_answers(source_file):
"""Runs the full pipeline:
- Loads input image
- Normalizes image
- Finds contours
- Finds corners among all contours
- Finds 'outmost' points of all corners
- Applies perpsective transform to get a bird's eye view
- Scans each line for the marked alternative
"""
im_orig = cv2.imread(source_file)
im_normalized = normalize(im_orig)
contours = get_contours(im_normalized)
corners = get_corners(contours)
cv2.drawContours(im_orig, corners, -1, (0, 255, 0), 3)
outmost = sort_points_counter_clockwise(get_outmost_points(corners))
color_transf = perspective_transform(im_orig, outmost)
normalized_transf = perspective_transform(im_normalized, outmost)
answers = []
for i, q_patch in enumerate(get_question_patches(normalized_transf)):
alt_index = get_marked_alternative(get_alternative_patches(q_patch))
if alt_index is not None:
color_q_patch = get_question_patch(color_transf, i)
draw_marked_alternative(color_q_patch, alt_index)
answers.append(get_letter(alt_index))
return answers, color_transf
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--input",
help="Input image filename",
required=True,
type=str)
parser.add_argument(
"--output",
help="Output image filename",
type=str)
parser.add_argument(
"--show",
action="store_true",
help="Displays annotated image")
args = parser.parse_args()
answers, im = get_answers(args.input)
for i, answer in enumerate(answers):
print("Q{}: {}".format(i + 1, answer))
if args.output:
cv2.imwrite(args.output, im)
print('Wrote image to {}.'.format(args.output))
if args.show:
cv2.imshow('Annotated image', im)
cv2.waitKey(0)
if __name__ == '__main__':
main()
