详解Python人工智能混合高斯模型运动目标检测
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本篇内容主要讲解“详解Python人工智能混合高斯模型运动目标检测”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“详解Python人工智能混合高斯模型运动目标检测”吧!
高斯算法提取工作
import cv2import numpy as np# 高斯算法class gaussian: def __init__(self): self.mean = np.zeros((1, 3)) self.covariance = 0 self.weight = 0; self.Next = None self.Previous = Noneclass Node: def __init__(self): self.pixel_s = None self.pixel_r = None self.no_of_components = 0 self.Next = Noneclass Node1: def __init__(self): self.gauss = None self.no_of_comp = 0 self.Next = Nonecovariance0 = 11.0def Create_gaussian(info1, info2, info3): ptr = gaussian() if (ptr is not None): ptr.mean[1, 1] = info1 ptr.mean[1, 2] = info2 ptr.mean[1, 3] = info3 ptr.covariance = covariance0 ptr.weight = 0.002 ptr.Next = None ptr.Previous = None return ptrdef Create_Node(info1, info2, info3): N_ptr = Node() if (N_ptr is not None): N_ptr.Next = None N_ptr.no_of_components = 1 N_ptr.pixel_s = N_ptr.pixel_r = Create_gaussian(info1, info2, info3) return N_ptrList_node = []def Insert_End_Node(n): List_node.append(n)List_gaussian = []def Insert_End_gaussian(n): List_gaussian.append(n)def Delete_gaussian(n): List_gaussian.remove(n);class Process: def __init__(self, alpha, firstFrame): self.alpha = alpha self.background = firstFrame def get_value(self, frame): self.background = frame * self.alpha + self.background * (1 - self.alpha) return cv2.absdiff(self.background.astype(np.uint8), frame)def denoise(frame): frame = cv2.medianBlur(frame, 5) frame = cv2.GaussianBlur(frame, (5, 5), 0) return framecapture = cv2.VideoCapture('1.mp4')ret, orig_frame = capture.read( )if ret is True: value1 = Process(0.1, denoise(orig_frame)) run = Trueelse: run = Falsewhile (run): ret, frame = capture.read() value = False; if ret is True: cv2.imshow('input', denoise(frame)) grayscale = value1.get_value(denoise(frame)) ret, mask = cv2.threshold(grayscale, 15, 255, cv2.THRESH_BINARY) cv2.imshow('mask', mask) key = cv2.waitKey(10) & 0xFF else: break if key == 27: break if value == True: orig_frame = cv2.resize(orig_frame, (340, 260), interpolation=cv2.INTER_CUBIC) orig_frame = cv2.cvtColor(orig_frame, cv2.COLOR_BGR2GRAY) orig_image_row = len(orig_frame) orig_image_col = orig_frame[0] bin_frame = np.zeros((orig_image_row, orig_image_col)) value = [] for i in range(0, orig_image_row): for j in range(0, orig_image_col): N_ptr = Create_Node(orig_frame[i][0], orig_frame[i][1], orig_frame[i][2]) if N_ptr is not None: N_ptr.pixel_s.weight = 1.0 Insert_End_Node(N_ptr) else: print("error") exit(0) nL = orig_image_row nC = orig_image_col dell = np.array((1, 3)); mal_dist = 0.0; temp_cov = 0.0; alpha = 0.002; cT = 0.05; cf = 0.1; cfbar = 1.0 - cf; alpha_bar = 1.0 - alpha; prune = -alpha * cT; cthr = 0.00001; var = 0.0 muG = 0.0; muR = 0.0; muB = 0.0; dR = 0.0; dB = 0.0; dG = 0.0; rval = 0.0; gval = 0.0; bval = 0.0; while (1): duration3 = 0.0; count = 0; count1 = 0; List_node1 = List_node; counter = 0; duration = cv2.getTickCount( ); for i in range(0, nL): r_ptr = orig_frame[i] b_ptr = bin_frame[i] for j in range(0, nC): sum = 0.0; sum1 = 0.0; close = False; background = 0; rval = r_ptr[0][0]; gval = r_ptr[0][0]; bval = r_ptr[0][0]; start = List_node1[counter].pixel_s; rear = List_node1[counter].pixel_r; ptr = start; temp_ptr = None; if (List_node1[counter].no_of_component > 4): Delete_gaussian(rear); List_node1[counter].no_of_component = List_node1[counter].no_of_component - 1; for k in range(0, List_node1[counter].no_of_component): weight = List_node1[counter].weight; mult = alpha / weight; weight = weight * alpha_bar + prune; if (close == False): muR = ptr.mean[0]; muG = ptr.mean[1]; muB = ptr.mean[2]; dR = rval - muR; dG = gval - muG; dB = bval - muB; var = ptr.covariance; mal_dist = (dR * dR + dG * dG + dB * dB); if ((sum < cfbar) and (mal_dist < 16.0 * var * var)): background = 255; if (mal_dist < (9.0 * var * var)): weight = weight + alpha; if mult < 20.0 * alpha: mult = mult; else: mult = 20.0 * alpha; close = True; ptr.mean[0] = muR + mult * dR; ptr.mean[1] = muG + mult * dG; ptr.mean[2] = muB + mult * dB; temp_cov = var + mult * (mal_dist - var); if temp_cov < 5.0: ptr.covariance = 5.0 else: if (temp_cov > 20.0): ptr.covariance = 20.0 else: ptr.covariance = temp_cov; temp_ptr = ptr; if (weight < -prune): ptr = Delete_gaussian(ptr); weight = 0; List_node1[counter].no_of_component = List_node1[counter].no_of_component - 1; else: sum += weight; ptr.weight = weight; ptr = ptr.Next; if (close == False): ptr = gaussian( ); ptr.weight = alpha; ptr.mean[0] = rval; ptr.mean[1] = gval; ptr.mean[2] = bval; ptr.covariance = covariance0; ptr.Next = None; ptr.Previous = None; Insert_End_gaussian(ptr); List_gaussian.append(ptr); temp_ptr = ptr; List_node1[counter].no_of_components = List_node1[counter].no_of_components + 1; ptr = start; while (ptr != None): ptr.weight = ptr.weight / sum; ptr = ptr.Next; while (temp_ptr != None and temp_ptr.Previous != None): if (temp_ptr.weight <= temp_ptr.Previous.weight): break; else: next = temp_ptr.Next; previous = temp_ptr.Previous; if (start == previous): start = temp_ptr; previous.Next = next; temp_ptr.Previous = previous.Previous; temp_ptr.Next = previous; if (previous.Previous != None): previous.Previous.Next = temp_ptr; if (next != None): next.Previous = previous; else: rear = previous; previous.Previous = temp_ptr; temp_ptr = temp_ptr.Previous; List_node1[counter].pixel_s = start; List_node1[counter].pixel_r = rear; counter = counter + 1;capture.release()cv2.destroyAllWindows()
createBackgroundSubtractorMOG2
背景减法 (BS) 是一种常用且广泛使用的技术，用于通过使用静态相机生成前景蒙版（即，包含属于场景中运动物体的像素的二值图像）。
顾名思义，BS 计算前景蒙版，在当前帧和背景模型之间执行减法运算，其中包含场景的静态部分，或者更一般地说，根据观察到的场景的特征，可以将所有内容视为背景。
背景建模包括两个主要步骤：
后台初始化；
背景更新。
在第一步中，计算背景的初始模型，而在第二步中，更新该模型以适应场景中可能的变化。
import cv2#构造VideoCapture对象cap = cv2.VideoCapture('1.mp4')# 创建一个背景分割器# createBackgroundSubtractorMOG2()函数里，可以指定detectShadows的值# detectShadows=True，表示检测阴影，反之不检测阴影。默认是truefgbg = cv2.createBackgroundSubtractorMOG2()while True : ret, frame = cap.read() # 读取视频 fgmask = fgbg.apply(frame) # 背景分割 cv2.imshow('frame', fgmask) # 显示分割结果 if cv2.waitKey(100) & 0xff == ord('q'): breakcap.release()cv2.destroyAllWindows()
到此，相信大家对“详解Python人工智能混合高斯模型运动目标检测”有了更深的了解，不妨来实际操作一番吧！这里是编程网网站，更多相关内容可以进入相关频道进行查询，关注我们，继续学习！
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