cv2.imshow(f"Contour {index}", clone)
wait_and_destroy_all_windows()
def show_external_contours(image, image_gray):
clone = image.copy()
contours = get_contours(image_gray, cv2.RETR_EXTERNAL)
cv2.drawContours(clone, contours, -1, (0, 255, 0), 2)
cv2.imshow("External contours", clone)
wait_and_destroy_all_windows()
def show_contour_masking(image, image_gray):
contours = get_contours(image_gray, cv2.RETR_EXTERNAL)
for idx, contour in enumerate(contours):
mask = np.zeros(image_gray.shape, dtype="uint8")
cv2.drawContours(mask, [contour], -1, (255), -1)
cv2.imshow("Original", image)
cv2.imshow("Mask", mask)
cv2.imshow(f"Masked contours: {idx}",
cv2.bitwise_and(image, image, mask=mask))
wait_and_destroy_all_windows()
image, image_gray = image_with_gray_arg()
contours = get_contours(image_gray, cv2.RETR_LIST)
show_all_contours(image, contours)
show_each_contour(image, contours)
show_external_contours(image, image_gray)
show_contour_masking(image, image_gray)
def show_2d_br_histogram(figure, image, channels):
ax = figure.add_subplot(133)
hist = cv2.calcHist([channels[0], channels[2]], [0, 1], None, [32, 32],
[0, 256, 0, 256])
p = ax.imshow(hist, interpolation="nearest")
plt.colorbar(p)
plt.xlabel("B")
plt.ylabel("R")
ax.set_title("BR Histogram")
print(f"BR 2D Histogram shape: {hist.shape}, {hist.flatten().shape[0]}")
def compute_3d_histogram(image, channels):
hist = cv2.calcHist(channels, [0, 1, 2], None, [8, 16, 9],
[0, 256, 0, 256, 0, 256])
print(f"3d histogram shape: {hist.shape}, "
f"with {hist.flatten().shape[0]} values")
image, gray_image = image_with_gray_arg()
channels = cv2.split(image)
cv2.imshow("Original", image)
show_bgr_histogram(image, channels)
fig = plt.figure()
show_2d_gb_histogram(fig, image, channels)
show_2d_gr_histogram(fig, image, channels)
show_2d_br_histogram(fig, image, channels)
compute_3d_histogram(image, channels)
plt.show()
import cv2
import matplotlib
matplotlib.use('macosx')
from matplotlib import pyplot as plt
from imageutils import image_with_gray_arg
def plot_histogram(histogram, name, y_label):
plt.figure()
plt.title(name)
plt.xlabel("Bins")
plt.ylabel(y_label)
plt.plot(hist)
plt.xlim([0, 256])
image, image_grayscale = image_with_gray_arg()
hist = cv2.calcHist([image_grayscale], [0], None, [256], [0, 256])
cv2.imshow("Grayscale", image_grayscale)
plot_histogram(hist, "Grayscale Unnormalized Histogram", "# of pixels")
hist /= hist.sum()
plot_histogram(hist, "Grayscale L1-Normalized Histogram", "% of pixels")
plt.show()