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import numpy as np
import cv2
from helper import pil_cv2_image_converter
class ColorDescriptor:
def __init__(self, bins):
# store the number of bins for the 3D histogram
self.bins = bins
def histogram(self, image, mask):
# extract a 3D color histogram from the masked region of the
# image, using the supplied number of bins per channel
hist = cv2.calcHist([image], [0, 1, 2], mask, self.bins,
[0, 180, 0, 256, 0, 256])
hist = cv2.normalize(hist, hist).flatten()
# return the histogram
return hist
def describe(self, image):
# first, convert image to cv2 from pil
# TODO: Add check, if already cv2 image
image = pil_cv2_image_converter(image)
# convert the image to the HSV color space and initialize
# the features used to quantify the image
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
features = []
# grab the dimensions and compute the center of the image
(h, w) = image.shape[:2]
(cX, cY) = (int(w * 0.5), int(h * 0.5))
# divide the image into four rectangles/segments (top-left,
# top-right, bottom-right, bottom-left)
segments = [(0, cX, 0, cY), (cX, w, 0, cY), (cX, w, cY, h),
(0, cX, cY, h)]
# construct an elliptical mask representing the center of the
# image
(axesX, axesY) = (int(w * 0.75) // 2, int(h * 0.75) // 2)
ellipMask = np.zeros(image.shape[:2], dtype = "uint8")
cv2.ellipse(ellipMask, (cX, cY), (axesX, axesY), 0, 0, 360, 255, -1)
# loop over the segments
for (startX, endX, startY, endY) in segments:
# construct a mask for each corner of the image, subtracting
# the elliptical center from it
cornerMask = np.zeros(image.shape[:2], dtype = "uint8")
cv2.rectangle(cornerMask, (startX, startY), (endX, endY), 255, -1)
cornerMask = cv2.subtract(cornerMask, ellipMask)
# extract a color histogram from the image, then update the
# feature vector
hist = self.histogram(image, cornerMask)
features.extend(hist)
# extract a color histogram from the elliptical region and
# update the feature vector
hist = self.histogram(image, ellipMask)
features.extend(hist)
# return the feature vector
return features
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