def problem2():
"""Solution ot part 2 of ps1."""
# Load the input image.
src = cv2.imread('./input/ps1-input0.png')
# Compute the edge image using canny edge method.
edge_img = cv2.Canny(src, 100, 200)
# Compute the Hough line transformation.
H = hough.hough_lines_acc(edge_img)
# Perform a histogram equalization to enchance the image.
H_ench = hough.enchance_acc(H)
# Save the result.
cv2.imwrite('./output/ps1-2-a-1.png', H_ench)
# Find peaks in accumulator array H.
peaks = hough.hough_peaks(H, edge_img.shape, 150, 0)
# Convert enchanced Hough image from gray to color to draw the peaks.
H_ench = cv2.cvtColor(H_ench, cv2.COLOR_GRAY2RGB)
# For each peak, draw a red dot in Hough accumulator array.
H_peak = H_ench.copy()
for param, pixel in peaks:
cv2.circle(H_peak, (pixel[1], pixel[0]), 2, (0, 0, 255), -1)
# Save the result.
cv2.imwrite('./output/ps1-2-b-1.png', H_peak)
# Draw the lines in the original image according to peaks List.
line_img = hough.hough_lines_draw(src, peaks)
# Save the result.
cv2.imwrite('./output/ps1-2-c-1.png', line_img)
# Uncoment the next 5 lines for display perposes.
cv2.imshow('Hough Accumulator', H_ench)
cv2.imshow('Hough Accumulator peaks', H_peak)
cv2.imshow('line image', line_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def problem3():
"""Solution ot part 3 of ps1."""
# Load the input image.
src = cv2.imread('./input/ps1-input0-noise.png')
# Remove noise with gaussian filter.
src_smooth = cv2.GaussianBlur(src, (19, 19), 5)
# Save smouthed image.
cv2.imwrite('./output/ps1-3-a-1.png', src_smooth)
# Compute the edge images using canny edge method.
edge_img = cv2.Canny(src, 100, 200)
edge_img_smoothed = cv2.Canny(src_smooth, 0, 50)
# Save the original and smoothed images.
cv2.imwrite('./output/ps1-3-b-1.png', edge_img)
cv2.imwrite('./output/ps1-3-b-2.png', edge_img_smoothed)
# Compute the Hough line transformation.
H = hough.hough_lines_acc(edge_img_smoothed)
# Perform a histogram equalization to enchance the image.
H_ench = hough.enchance_acc(H)
# Find peaks in accumulator array H.
peaks = hough.hough_peaks(H, edge_img.shape, 140, 0)
# Convert enchanced Hough image from gray to color to draw the peaks.
H_ench = cv2.cvtColor(H_ench, cv2.COLOR_GRAY2RGB)
# For each peak, draw a red dot in Hough accumulator array.
H_peak = H_ench.copy()
for param, pixel in peaks:
cv2.circle(H_peak, (pixel[1], pixel[0]), 2, (0, 0, 255), -1)
# Save the result.
cv2.imwrite('./output/ps1-3-c-1.png', H_peak)
# Draw the lines in the original image according to peaks List.
line_img = hough.hough_lines_draw(src, peaks)
# Save the result.
cv2.imwrite('./output/ps1-3-c-2.png', line_img)
def problem6():
"""Solution to part 5 of ps1."""
# Load image.
src = cv2.imread('./input/ps1-input2.png')
# Create a grayscale image from original.
src_gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
# smooth image to find edges.
smoothed = cv2.GaussianBlur(src_gray, (9, 9), 1)
# extract edges using canny edge algorithm.
edge_img = auto_canny(smoothed, 2)
H = hough.hough_lines_acc(edge_img, )
# Find peak points in Hough accumulator.
peaks = hough.hough_peaks(H, edge_img.shape, 140)
# perform histogram equalizaton to Hough accumulator to diplay it.
H_ench = hough.enchance_acc(H)
# Convert enchanced Hough image from gray to color to draw the peaks.
H_ench = cv2.cvtColor(H_ench, cv2.COLOR_GRAY2RGB)
src_gray = cv2.cvtColor(src_gray, cv2.COLOR_GRAY2RGB)
# For each peak, draw a red dot in Hough accumulator array.
H_peak = H_ench.copy()
# Delete lines with 0 d.
peaks = [(param, pixel) for param, pixel in peaks if param[0] > 1]
for param, pixel in peaks:
cv2.circle(H_peak, (pixel[1], pixel[0]), 1, (0, 0, 255), -1)
# Draw found lines in the original image.
line_img = hough.hough_lines_draw(src_gray, peaks)
# Save the result.
cv2.imwrite('./test/ps1-4-a-1.png', smoothed)
cv2.imwrite('./test/ps1-4-b-1.png', edge_img)
cv2.imwrite('./test/ps1-4-c-1.png', H_peak)
cv2.imwrite('./test/ps1-4-c-2.png', line_img)
cv2.waitKey(0)
# img_dir = np.arctan2(img_sobely,img_sobelx)
# plt.imshow(img_sobelx, cmap = 'gray', interpolation = 'bicubic')
# plt.xticks([]), plt.yticks([])
# plt.show()
img_edges = cv2.Canny(img, 100, 200)
cv2.imwrite(os.path.join('output', 'ps1-1-a-1.png'), img_edges)
# Problem 2: Hough Line
H, theta, rho = hough.hough_lines_acc(img_edges)
H_norm = (H/np.amax(H)) * 255
cv2.imwrite(os.path.join('output', 'ps1-2-a-1.png'), H_norm)
H1 = H_norm.copy()
peaks = hough.hough_peaks(H1, 8, nHoodSize = (50,50))
for x,y in peaks:
cv2.circle(H1, (int(x), int(y)), 10, (255,0,0), 1)
cv2.imwrite(os.path.join('output', 'ps1-2-b-1.png'), H1)
hough.hough_lines_draw(img, 'ps1-2-c-1.png', peaks, rho, theta)
# Problem 3
img = cv2.imread(os.path.join('input', 'ps1-input0-noise.png'), 0)
img_clean = cv2.GaussianBlur(img,(15,15),1)
cv2.imwrite(os.path.join('output', 'ps1-3-a-1.png'), img_clean)
img_edges1 = cv2.Canny(img, 200, 400)
cv2.imwrite(os.path.join('output', 'ps1-3-b-1.png'), img_edges1)
img_edges2 = cv2.Canny(img_clean, 200, 400)