b,g,r = cv2.split(bgr_img) # get b,g,r
rgb_img = cv2.merge([r,g,b]) # switch it to rgb
(w, h, na) = rgb_img.shape
xstops = [0, 225, 450, h]
ystops = [0, 150, w]
images = []
i = 0
for x in range(3):
for y in range(2):
sub_img = rgb_img[ystops[y]:ystops[y+1], xstops[x]:xstops[x+1], :]
ax = plt.subplot(2, 3, i+1)
images.append(sub_img)
my_imshow(sub_img, ax=ax)
i += 1
def plot_color_histogram(rbg_img):
color = ('r','g','b')
for i, col in enumerate(color):
histr = cv2.calcHist([rbg_img], [i], None, [256], [0,256])
plt.plot(histr, color=col)
plt.xlim([0,256])
def test_for_red(rgb_img):
red_boundaries = ([100, 15, 17],
[255, 56, 50])
# create NumPy arrays from the boundaries
lower = np.array(red_boundaries[0], dtype = "uint8")
__author__ = 'Johnson'
import cv2
import numpy as np
from utilities import my_imshow
import matplotlib.pyplot as plt
import matplotlib.cm as cm
filename = "./prob12/prob12.bmp"
image = cv2.imread(filename)
gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
(w, h) = gray_img.shape
ystops = [0, 280, 580, w]
images = []
for i in range(3):
img = gray_img[ystops[i]:ystops[i+1],:]
img[img>100] = 0
images.append(img)
plt.subplot(2,1,1)
my_imshow(img, cmap=cm.Greys_r)
# get edges of the image
filter_image = cv2.blur(img, ksize=(5,5))
edge_image = cv2.Canny(filter_image, threshold1=0, threshold2=60, apertureSize=3)
plt.subplot(2,1,2)
my_imshow(edge_image, cmap=cm.Greys_r)
plt.show()
