4 Histograms.py

# import the necessary packages
from matplotlib import pyplot as plt
import numpy as np
import argparse
import cv2
import os

# # construct the argument parser and parse the arguments
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", required=True, help="Path to the image")
# args = vars(ap.parse_args())
#
# # load the image and show it
# image = cv2.imread(args["image"]

CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
IMAGES_FOLDER_PATH = os.path.join(CURRENT_DIR, "Images")
IMAGE_FILE = os.path.join(IMAGES_FOLDER_PATH, "image2.jpg")
image = cv2.imread(IMAGE_FILE)
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
cv2.imshow("image", hsv)
# cv2.imshow("image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

##########################################################
# Gray color histogram
##########################################################
# # convert the image to grayscale and create a histogram
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# cv2.imshow("gray", gray)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
# plt.figure()
# plt.title("Grayscale Histogram")
# plt.xlabel("Bins")
# plt.ylabel("# of Pixels")
# plt.plot(hist)
# plt.xlim([0, 256])
# plt.show()

# grab the image channels, initialize the tuple of colors,
# the figure and the flattened feature vector
chans = cv2.split(image)
colors = ("b", "g", "r")
plt.figure()
plt.title("'Flattened' Color Histogram")
plt.xlabel("Bins")
plt.ylabel("# of Pixels")
features = []

# loop over the image channels
for (chan, color) in zip(chans, colors):
    # create a histogram for the current channel and
    # concatenate the resulting histograms for each
    # channel
    hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
    features.extend(hist)

    # plot the histogram
    plt.plot(hist, color=color)
    plt.xlim([0, 256])

plt.show()
# here we are simply showing the dimensionality of the
# flattened color histogram 256 bins for each channel
# x 3 channels = 768 total values -- in practice, we would
# normally not use 256 bins for each channel, a choice
# between 32-96 bins are normally used, but this tends
# to be application dependent
print("flattened feature vector size: %d" % (np.array(features).flatten().shape))