通过OpenCV实现对指定颜色的物体追踪

import argparse
from collections import deque
import cv2
import numpy as np

def grab_contours(cnts):
    # 如果 cv2.findContours 返回的轮廓元组的长度为“2”，那么我们使用的是 OpenCV v2.4、v4-beta 或 v4-official
    if len(cnts) == 2:
        cnts = cnts[0]
    # 如果轮廓元组的长度为“3”，那么我们使用的是 OpenCV v3、v4-pre 或 v4-alpha
    elif len(cnts) == 3:
        cnts = cnts[1]
    else:
        raise Exception(("Contours tuple must have length 2 or 3, "
            "otherwise OpenCV changed their cv2.findContours return "
            "signature yet again. Refer to OpenCV's documentation "
            "in that case"))
    return cnts
def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    dim = None
    (h, w) = image.shape[:2]
    # 如果高和宽为None则直接返回
    if width is None and height is None:
        return image
    # 检查宽是否是None
    if width is None:
        # 计算高度的比例并并按照比例计算宽度
        r = height / float(h)
        dim = (int(w * r), height)
    # 高为None
    else:
        # 计算宽度比例，并计算高度
        r = width / float(w)
        dim = (width, int(h * r))
    resized = cv2.resize(image, dim, interpolation=inter)
    # return the resized image
    return resized

 命令行参数
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to video")
ap.add_argument("-b", "--buffer", type=int, default=64, help="max buffer size")
args = vars(ap.parse_args())
# 绿色树叶的HSV色域空间范围
greenLower = (29, 86, 6)
greenUpper = (64, 255, 255)
pts = deque(maxlen=args["buffer"])
vs = cv2.VideoCapture(0)
fps = 30    #保存视频的FPS，可以适当调整
size=(600,450)
fourcc=cv2.VideoWriter_fourcc(*'XVID')
videowrite=cv2.VideoWriter('output.avi',fourcc,fps,size)

while True:
    ret_val, frame = vs.read()
    if ret_val is False:
        break
    frame = resize(frame, width=600)
    # 通过高斯滤波去除掉一些高频噪声，使得重要的数据更加突出
    blurred = cv2.GaussianBlur(frame, (11, 11), 0)
    # 将图片转为HSV
    hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
    # inRange的作用是根据阈值进行二值化:阈值内的像素设置为白色(255)，阈值外的设置为黑色(0)
    mask = cv2.inRange(hsv, greenLower, greenUpper)
    # 腐蚀(erode)和膨胀(dilate)的作用:
    # 1. 消除噪声;
    # 2. 分割(isolate)独立的图像元素，以及连接(join)相邻的元素;
    # 3. 寻找图像中的明显的极大值区域或极小值区域
    mask = cv2.erode(mask, None, iterations=2)
    mask = cv2.dilate(mask, None, iterations=2)
   

 # 寻找轮廓，不同opencv的版本cv2.findContours返回格式有区别，所以调用了一下imutils.grab_contours做了一些兼容性处理
    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = grab_contours(cnts)
    center = None
    # only proceed if at least one contour was found
    if len(cnts) > 0:
        # find the largest contour in the mask, then use it to compute the minimum enclosing circle
        # and centroid
        c = max(cnts, key=cv2.contourArea)
        ((x, y), radius) = cv2.minEnclosingCircle(c)
        M = cv2.moments(c)
        # 对于01二值化的图像，m00即为轮廓的面积, 一下公式用于计算中心距
        center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
        # only proceed if the radius meets a minimum size
        if radius > 10:
            # draw the circle and centroid on the frame, then update the list of tracked points
            cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), 2)
            cv2.circle(frame, center, 5, (0, 0, 255), -1)

        pts.appendleft(center)

    for i in range(1, len(pts)):
        # if either of the tracked points are None, ignore them
        if pts[i - 1] is None or pts[i] is None:
            continue

        # compute the thickness of the line and draw the connecting line
        thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
        cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)

    cv2.imshow("Frame", frame)
    videowrite.write(frame)
    key = cv2.waitKey(1) & 0xFF

    if key == ord("q"):
        break
videowrite.release()
vs.release()
cv2.destroyAllWindows()