from ImageUtils import MultiplePlot
from ImageUtils.hough_transform import hough_line, extract_hough_lines
import numpy as np
import matplotlib.pyplot as plt
original = np.zeros((200, 100))
original[30:170, 25:26] = original[30:170, 74:75] = 255
original[30:31, 26:74] = original[169:170, 26:74] = 255
drawer = MultiplePlot([10, 10], [1, 2])
drawer.add(original, "Original")
accumulator, thetas, rhos = hough_line(original)
hough_lines = extract_hough_lines(accumulator, thetas, rhos, 4, 5)
out = np.zeros_like(original)
for i in hough_lines:
i["rho"] = abs(int(i["rho"]))
if np.rad2deg(i["theta"]) == -90:
i["rho"] += 1
for x_idx in range(out.shape[1]):
out[i["rho"], x_idx] += 1
elif np.rad2deg(i["theta"]) == 0:
for y_idx in range(out.shape[0]):
out[y_idx, i["rho"]] += 1
corners = np.where(out == 2)
# corners = [(a, b) for (a, b) in zip(corners_temp[0], corners_temp[1])]
y_idx = corners[0]
from ImageUtils import MultiplePlot from ImageUtils.kernels import * from ImageUtils.utils import convolve from ImageUtils.utils import uniform_noise original = np.zeros((200, 200)) original[50:150, 50:150] = 255 drawer = MultiplePlot([20, 10], [1, 3]) drawer.add(original, "Original") noised = uniform_noise(original, 5) drawer.add(noised, "Uniform noise") cleaned = convolve(noised, kernel_noise_remove) averaged = convolve(cleaned, kernel_average) out = convolve(original, kernel_sobel) out2 = abs(out) drawer.add(out2, "After filter") drawer.show()
import cv2 as cv
import numpy as np
from ImageUtils import MultiplePlot
drawer = MultiplePlot([10, 10], [1, 2])
kernel_d = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], np.uint8)
origin = cv.imread("rise_hollow.jpg", 0)
origin[origin > 150] = 255
origin[origin < 150] = 0
invert = np.invert(origin)
filler = np.zeros_like(origin)
filler[0, 0] = 255
last = np.zeros_like(filler)
while not np.array_equal(last, filler):
last = filler
dilated = cv.dilate(filler, kernel_d, iterations=1)
filler = np.bitwise_and(dilated, invert)
filler = np.invert(filler)
filled = origin + filler
drawer.add(origin, "Origin")
drawer.add(filled, "Filled")
drawer.show()
import cv2 as cv
import numpy as np
from ImageUtils import MultiplePlot
kernel_erosion = np.ones((3, 3))
kernel_dilation = np.ones((5, 5))
drawer = MultiplePlot([10, 10], [1, 2])
original = cv.imread('sudoku.png', 0)
drawer.add(original, "Original")
erosion = cv.erode(original, kernel_erosion, iterations=1)
dilation = cv.dilate(original, kernel_dilation, iterations=1)
drawer.add(dilation, "After Dilation")
drawer.show()
def main():
for file in IMAGES:
print("Image: {0}".format(file))
drawer = MultiplePlot([20, 10], [1, 2])
image = cv2.imread('ImageInputs/{0}.tif'.format(file))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 80, 255, cv2.THRESH_BINARY)
opened = thresh
for i in range(35):
opened = cv2.morphologyEx(opened, cv2.MORPH_OPEN, np.ones((3, 3)))
filled = fill_blob(opened)
blurred = cv2.GaussianBlur(filled, (13, 13), 0)
edges = cv2.Canny(blurred, 100, 255, 7)
hough_line = cv2.HoughLines(edges, 1, np.pi / 180, 120)
y_size, x_size = np.shape(filled)
sorted_lines = sort_lines(hough_line, x_size, y_size)
print("Top:")
print(sorted_lines[TOP])
print('Bottom:')
print(sorted_lines[BOTTOM])
print('Left:')
print(sorted_lines[LEFT])
print('Right:')
print(sorted_lines[RIGHT])
if sorted_lines[TOP][0][RHO] > TOP_THRESH:
sorted_lines[TOP][0][RHO] = TOP_THRESH
sorted_lines[BOTTOM] = sorted_lines[BOTTOM][:1]
final_lines = [
sorted_lines[TOP][0], sorted_lines[BOTTOM][0],
sorted_lines[RIGHT][0]
]
if len(sorted_lines[LEFT]) > 1 and \
abs(sorted_lines[LEFT][0][RHO] - sorted_lines[LEFT][1][RHO]) > INTERNAL_THRESH:
final_lines.append(sorted_lines[LEFT][0])
else:
final_lines.append(sorted_lines[LEFT][-1])
points = get_line_points(final_lines, x_size, y_size)
pts_src = np.array(
[points[0][0], points[0][1], points[1][0], points[1][1]])
x_len = abs(points[0][0][0] - points[0][1][0])
y_len = abs(points[0][0][1] - points[1][0][1])
pts_dst = np.array([[0.0, 0.0], [x_len, 0.0], [0.0, y_len],
[x_len, y_len]])
im_dst = np.zeros((y_len, x_len, 3), np.uint8)
h, status = cv2.findHomography(pts_src, pts_dst)
im_out = cv2.warpPerspective(image, h,
(im_dst.shape[1], im_dst.shape[0]))
drawer.add(image, 'Image {0}: Original'.format(file))
drawer.add(im_out, 'Image {0}: Aligned'.format(file))
drawer.show()
print('\n\n')
from ImageUtils import MultiplePlot from ImageUtils.kernels import * from ImageUtils.utils import convolve original = np.zeros((200, 200)) original[50:150, 50:150] = 255 drawer = MultiplePlot([10, 10], [1, 2]) drawer.add(original, "Original") out = convolve(original, kernel_sobel) out2 = abs(out) drawer.add(out2, "After filter") drawer.show()
import cv2 as cv
import numpy as np
from ImageUtils import MultiplePlot
from ImageUtils.utils import threshold
drawer = MultiplePlot([10, 10], [1, 2])
original = cv.imread('rise.jpg', 0)
thresholded = threshold(original, 115)
opened = cv.morphologyEx(thresholded, cv.MORPH_OPEN, np.ones((7, 7)))
erosion = cv.erode(opened, np.ones((5, 5)), iterations=1)
res = opened - erosion
res[res == 1] = 200
drawer.add(original, "Boundary")
drawer.add(res, "After")
cv.imwrite("rise_hollow.jpg", res)
drawer.show()