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preprocess.py
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preprocess.py
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import numpy as np
import cv2
import os
PREPROCESSED_PATH = "preprocessed_imgs"
IM_PATH = "./data/train"
def read_im(index:int, preprocessed = True) -> np.array:
"""
Read image with given index
Args:
index:int
Returns:
np.array - image read
"""
base_path = IM_PATH
if preprocessed:
base_path = PREPROCESSED_PATH
return cv2.imread(os.path.join(base_path, str(int(index))+'.jpg'))
def apply_filter(image:np.array) -> np.array:
"""
Define a 5X5 kernel and apply the filter to gray scale image
Args:
image: np.array
Returns:
filtered: np.array
"""
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
kernel = np.ones((5, 5), np.float64) / 15
filtered = cv2.filter2D(gray, -1, kernel)
return filtered
def apply_threshold(filtered:np.array) -> np.array:
"""
Apply OTSU threshold
Args:
filtered: np.array
Returns:
thresh: np.array
"""
thresh = cv2.threshold(filtered, 127, 255,
cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
return thresh
def cut_image(
img:np.array,
padding:int
) -> np.array:
"""
Center crop of image with given boundaries
Args:
img: np.array
padding: int
Returns:
img: np.array
"""
return img[
padding: img.shape[0] - padding,
padding: img.shape[1] - padding,
]
def cut_borders(img:np.array) -> np.array:
"""
Automaticly crop black rectangle in the photo
Args:
img: np.array - initial image
Returns:
img: np.array - cropped image
"""
x_left = 0
x_right = img.shape[0] - 1
y_left = 0
y_right = img.shape[1] - 1
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
thresh = apply_threshold(gray)
while thresh[x_left][y_left] == 255:
x_left += 1
y_left += 1
while thresh[x_right][y_right] == 255:
x_right -= 1
y_right -= 1
while thresh[x_left][y_left] == 0:
x_left += 1
y_left += 1
while thresh[x_right][y_right] == 0:
x_right -= 1
y_right -= 1
return img[x_left:x_right, y_left:y_right]
def detect_contour(img:np.array, image_shape:tuple) -> (np.array, list):
"""
Detect image contours and draw them on empty image with given shape
Returns new image and list of contours
Args:
img: np.array()
image_shape: tuple
Returns:
canvas: np.array() - empty image with contours drawn
cnt: list - list of contours
"""
canvas = np.zeros(image_shape, np.uint8)
contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# second biggest contour is our goal rectangle
cnt = sorted(contours, key=cv2.contourArea, reverse=True)[1]
cv2.drawContours(canvas, cnt, -1, (0, 255, 255), 3)
return canvas, cnt
def detect_corners_from_contour(canvas:np.array, cnt:np.array) -> list:
"""
Detecting corner points from contours using cv2.approxPolyDP()
Args:
canvas: np.array()
cnt: list
Returns:
approx_corners: list
"""
epsilon = 0.01 * cv2.arcLength(cnt, True)
approx_corners = cv2.approxPolyDP(cnt, epsilon, True)
cv2.drawContours(canvas, approx_corners, -1, (255, 255, 0), 10)
approx_corners = sorted(np.concatenate(approx_corners).tolist())
return approx_corners
def order_points(pts:list) -> list:
"""
Rearrange the corner points so that first entry is top-left,
second is top-right, third is bottom-right, fourth is bottom-left.
Args:
pts: list - corner points
Returns:
rect: list - rearranged points
"""
rect = np.zeros((4, 2), dtype="float32")
# the top-left point will have the smallest sum, whereas
# the bottom-right point will have the largest sum
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
# now, compute the difference between the points, the
# top-right point will have the smallest difference,
# whereas the bottom-left will have the largest difference
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
# return the ordered coordinates
return rect
def four_point_transform(image:np.array, pts:list) -> np.array:
"""
Apply perspective transform to image
Args:
image: np.array - initial image
pts: list - corner points of rectangle
Returns:
warped: np.array - perspective transformed image
"""
# obtain a consistent order of the points and unpack them
# individually
rect = order_points(pts)
(tl, tr, br, bl) = rect
# compute the width of the new image, which will be the
# maximum distance between bottom-right and bottom-left
# x-coordiates or the top-right and top-left x-coordinates
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
# compute the height of the new image, which will be the
# maximum distance between the top-right and bottom-right
# y-coordinates or the top-left and bottom-left y-coordinates
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
# now that we have the dimensions of the new image, construct
# the set of destination points to obtain a "birds eye view",
# (i.e. top-down view) of the image, again specifying points
# in the top-left, top-right, bottom-right, and bottom-left
# order
dst = np.array(
[[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]],
dtype="float32",
)
# compute the perspective transform matrix and then apply it
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
# return the warped image
return warped
def automatic_brightness_and_contrast(image:np.array, clip_hist_percent:float = 1) -> (np.array, float, float):
"""
Brightness and contrast normalization of the image
by clipping grays in histogram
Args:
image: np.array
clip_hist_percent: float
Returns:
auto_result: np.array - balanced image
alpha: float - alpha used in cv2.convertScaleAbs
beta: float - beta used in cv2.convertScaleAbs
"""
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Calculate grayscale histogram
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
hist_size = len(hist)
# Calculate cumulative distribution from the histogram
accumulator = []
accumulator.append(float(hist[0]))
for index in range(1, hist_size):
accumulator.append(accumulator[index - 1] + float(hist[index]))
# Locate points to clip
maximum = accumulator[-1]
clip_hist_percent *= maximum / 100.0
clip_hist_percent /= 2.0
# Locate left cut
minimum_gray = 0
while accumulator[minimum_gray] < clip_hist_percent:
minimum_gray += 1
# Locate right cut
maximum_gray = hist_size - 1
while accumulator[maximum_gray] >= (maximum - clip_hist_percent):
maximum_gray -= 1
# Calculate alpha and beta values
alpha = 255 / (maximum_gray - minimum_gray)
beta = -minimum_gray * alpha
auto_result = cv2.convertScaleAbs(image, alpha=alpha, beta=beta)
return (auto_result, alpha, beta)
def full_pipeline(img:np.array) -> np.array:
"""
Full preprocssing of the image
Args:
img: np.array - Initial Image
Returns:
img: np.array - cropped by rectangle image
"""
# rawly cut image so it does find contour of the paper itself
cutted_img = cut_image(img, 500)
filtered = apply_filter(cutted_img)
thresholded = apply_threshold(filtered)
canvas, contour = detect_contour(thresholded, thresholded.shape)
corners = detect_corners_from_contour(canvas, contour)
cropped = four_point_transform(cutted_img, np.array(corners))
# cut black borders
cutted = cut_borders(cropped)
# improve brightness
res = automatic_brightness_and_contrast(cutted, clip_hist_percent=1)[0]
img = np.rot90(res) if img.shape[0] > img.shape[1] else res
return img