Histogram and Back Projection – OpenCV 3.4 with python 3 Tutorial 28

[python]
import cv2
import numpy as np
from matplotlib import pyplot as plt

original_image = cv2.imread("goalkeeper.jpg")
hsv_original = cv2.cvtColor(original_image, cv2.COLOR_BGR2HSV)

roi = cv2.imread("pitch_ground.jpg")
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)

hue, saturation, value = cv2.split(hsv_roi)

# Histogram ROI
roi_hist = cv2.calcHist([hsv_roi], [0, 1], None, [180, 256], [0, 180, 0, 256])
mask = cv2.calcBackProject([hsv_original], [0, 1], roi_hist, [0, 180, 0, 256], 1)

# Filtering remove noise
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
mask = cv2.filter2D(mask, -1, kernel)
_, mask = cv2.threshold(mask, 100, 255, cv2.THRESH_BINARY)

mask = cv2.merge((mask, mask, mask))
result = cv2.bitwise_and(original_image, mask)

cv2.imshow("Mask", mask)
cv2.imshow("Original image", original_image)
cv2.imshow("Result", result)
cv2.imshow("Roi", roi)
cv2.waitKey(0)
cv2.destroyAllWindows()
[/python]

 

Files:

  1. goalkeeper.jpg
  2. pitch_ground.jpg
Blueprint

Learn to build Computer Vision Software easily and efficiently.

This is a FREE Workshop where I'm going to break down the 4 steps that are necessary to build software to detect and track any object.

Sign UP for FREE