Histogram Backprojection

16636401:

  Histogram Backprojection is a technique used in computer vision for object detection and image segmentation. Its principle is based on the color distribution of the image, allowing us to find areas in an image that match the color distribution of a given object. This technology is often used for tasks such as target tracking, object recognition and image segmentation in images.
  Principle:
  The principle of histogram backprojection is based on the following concept: We first build a histogram of a "target color model" that describes the color distribution of the object we wish to detect. We then compare this target color model to the input image, assigning each pixel of the input image a score to represent the likelihood that the pixel belongs to the target object. Pixels with higher scores are considered more likely to belong to the target object.
  Mathematical formula:
  The mathematical formula for histogram backprojection is as follows:
$$\text{backProj}(x,y)=\text{histModel}(I(x,y))$$
where:

• $\text{backProj}(x,y)$ represents the coordinate$(x,$The backprojection value at$y$$)\; .$
• $\text{histModel}$ is the histogram of the target color model.
• $I(x,y)$ represents the pixel value in the input image.
    Applicable scenarios:
    Histogram back projection is suitable for situations where objects need to be detected and segmented based on their color distribution. For example, when looking for objects with a specific color or color distribution in an image, or for tracking moving objects, histogram backprojection can be used to improve the accuracy of object detection.
    Here is sample code for histogram backprojection using Python's OpenCV library:

import cv2
import numpy as np
import matplotlib.pyplot as plt

def Histogram_Backprojection(image):

    img=cv2.imread(image)
    if img is None:
        print('Unable to load image!')
    else:
        # 定义目标对象的区域（在这个例子中，我们使用一个矩形区域）
        (x1,y1)=(60,30)
        (x2,y2)=(120,60)
        roi=img[y1:y2,x1:x2]
        # 将目标对象的颜色模型转换为HSV颜色空间
        roi_hsv=cv2.cvtColor(roi,cv2.COLOR_BGR2HSV)
        # 计算目标对象的颜色直方图
        hist = cv2.calcHist([roi_hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
        # 归一化直方图
        cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX)
        # 计算图像的反向投影
        backProj = cv2.calcBackProject([img], [0, 1], hist, [0, 180, 0, 256], 1)

        plt.figure(figsize=(6, 4))

        plt.subplot(121), plt.title('Original image'), plt.axis('off')
        plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

        plt.subplot(122), plt.title('backProj image'), plt.axis('off')
        plt.imshow(cv2.cvtColor(backProj, cv2.COLOR_BGR2RGB))

        plt.tight_layout()
        plt.show()
imgfile1='./Images/cat.jpg'
Histogram_Backprojection(imgfile1)

  In this example, we first select a region of interest (ROI) from the input image and then convert it to HSV color space. Next, the color histogram of the target object is calculated and normalized. Finally, cv2.calcBackProjecta backprojection of the input image is calculated using a function to show the areas where the target object is likely to be present.