代码实现参考官方开源C代码，仅实现了对灰度图的运动目标检测，即对应C代码中三个核心函数：
libvibeModel_Sequential_AllocInit_8u_C1R
libvibeModel_Sequential_Segmentation_8u_C1R
libvibeModel_Sequential_Update_8u_C1R
其中，Segmentation部分循环进行了相应的修改，没有对像素进行遍历逐一修改，而是通过获取mask，直接对图像矩阵进行修改；Update部分完全照着C代码改的python，判断语句太多了。
官网截图
全部代码：https://github.com/232525/ViBe.python
ViBe类文件
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 18 17:43:58 2020
@filename: lib_vibe.py
@author: curya
"""
import numpy as np
import time
class vibe_gray:
def __init__(self, ):
self.width = 0
self.height = 0
self.numberOfSamples = 20
self.matchingThreshold = 20
self.matchingNumber = 2
self.updateFactor = 16
# Storage for the history
self.historyImage = None
self.historyBuffer = None
self.lastHistoryImageSwapped = 0
self.numberOfHistoryImages = 2
# Buffers with random values
self.jump = None
self.neighbor = None
self.position = None
def AllocInit(self, image):
print('AllocInit!')
height, width = image.shape[:2]
# set the parametors
self.width = width
self.height = height
print(self.height, self.width)
# create the historyImage
self.historyImage = np.zeros((self.height, self.width, self.numberOfHistoryImages), np.uint8)
for i in range(self.numberOfHistoryImages):
self.historyImage[:, :, i] = image
# create and fill the historyBuffer
self.historyBuffer = np.zeros((self.height, self.width, self.numberOfSamples-self.numberOfHistoryImages), np.uint8)
for i in range(self.numberOfSamples-self.numberOfHistoryImages):
image_plus_noise = image + np.random.randint(-10, 10, (self.height, self.width))
image_plus_noise[image_plus_noise > 255] = 255
image_plus_noise[image_plus_noise < 0] = 0
self.historyBuffer[:, :, i] = image_plus_noise.astype(np.uint8)
# fill the buffers with random values
size = 2 * self.width + 1 if (self.width > self.height) else 2 * self.height + 1
self.jump = np.zeros((size), np.uint32)
self.neighbor = np.zeros((size), np.int)
self.position = np.zeros((size), np.uint32)
for i in range(size):
self.jump[i] = np.random.randint(1, 2*self.updateFactor+1)
self.neighbor[i] = np.random.randint(-1, 3-1) + np.random.randint(-1, 3-1) * self.width
self.position[i] = np.random.randint(0, self.numberOfSamples)
def Segmentation(self, image):
# segmentation_map init
segmentation_map = np.zeros((self.height, self.width)) + (self.matchingNumber - 1)
# first history image
mask = np.abs(image - self.historyImage[:, :, 0]) > self.matchingThreshold
segmentation_map[mask] = self.matchingNumber
# next historyImages
for i in range(1, self.numberOfHistoryImages):
mask = np.abs(image - self.historyImage[:, :, i]) <= self.matchingThreshold
segmentation_map[mask] = segmentation_map[mask] - 1
# for swapping
self.lastHistoryImageSwapped = (self.lastHistoryImageSwapped + 1) % self.numberOfHistoryImages
swappingImageBuffer = self.historyImage[:, :, self.lastHistoryImageSwapped]
# now, we move in the buffer and leave the historyImage
numberOfTests = self.numberOfSamples - self.numberOfHistoryImages
mask = segmentation_map > 0
for i in range(numberOfTests):
mask_ = np.abs(image - self.historyBuffer[:, :, i]) <= self.matchingThreshold
mask_ = mask * mask_
segmentation_map[mask_] = segmentation_map[mask_] - 1
# Swapping: Putting found value in history image buffer
temp = swappingImageBuffer[mask_].copy()
swappingImageBuffer[mask_] = self.historyBuffer[:, :, i][mask_].copy()
self.historyBuffer[:, :, i][mask_] = temp
# simulate the exit inner loop
mask_ = segmentation_map <= 0
mask_ = mask * mask_
segmentation_map[mask_] = 0
# Produces the output. Note that this step is application-dependent
mask = segmentation_map > 0
segmentation_map[mask] = 255
return segmentation_map.astype(np.uint8)
def Update(self, image, updating_mask):
numberOfTests = self.numberOfSamples - self.numberOfHistoryImages
inner_time = 0
t0 = time.time()
# updating
for y in range(1, self.height-1):
t1 = time.time()
shift = np.random.randint(0, self.width)
indX = self.jump[shift]
t2 = time.time()
inner_time += (t2 - t1)
#""" too slow
while indX < self.width - 1:
t3 = time.time()
index = indX + y * self.width
t4 = time.time()
inner_time += (t4 - t3)
if updating_mask[y, indX] == 255:
t5 = time.time()
value = image[y, indX]
index_neighbor = index + self.neighbor[shift]
y_, indX_ = int(index_neighbor / self.width), int(index_neighbor % self.width)
if self.position[shift] < self.numberOfHistoryImages:
self.historyImage[y, indX, self.position[shift]] = value
self.historyImage[y_, indX_, self.position[shift]] = value
else:
pos = self.position[shift] - self.numberOfHistoryImages
self.historyBuffer[y, indX, pos] = value
self.historyBuffer[y_, indX_, pos] = value
t6 = time.time()
inner_time += (t6 - t5)
t7 = time.time()
shift = shift + 1
indX = indX + self.jump[shift]
t8 = time.time()
inner_time += (t8 - t7)
#"""
t9 = time.time()
# print('update: %.4f, inner time: %.4f' % (t9 - t0, inner_time))
# First row
y = 0
shift = np.random.randint(0, self.width)
indX = self.jump[shift]
while indX <= self.width - 1:
index = indX + y * self.width
if updating_mask[y, indX] == 0:
if self.position[shift] < self.numberOfHistoryImages:
self.historyImage[y, indX, self.position[shift]] = image[y, indX]
else:
pos = self.position[shift] - self.numberOfHistoryImages
self.historyBuffer[y, indX, pos] = image[y, indX]
shift = shift + 1
indX = indX + self.jump[shift]
# Last row
y = self.height - 1
shift = np.random.randint(0, self.width)
indX = self.jump[shift]
while indX <= self.width - 1:
index = indX + y * self.width
if updating_mask[y, indX] == 0:
if self.position[shift] < self.numberOfHistoryImages:
self.historyImage[y, indX, self.position[shift]] = image[y, indX]
else:
pos = self.position[shift] - self.numberOfHistoryImages
self.historyBuffer[y, indX, pos] = image[y, indX]
shift = shift + 1
indX = indX + self.jump[shift]
# First column
x = 0
shift = np.random.randint(0, self.height)
indY = self.jump[shift]
while indY <= self.height - 1:
index = x + indY * self.width
if updating_mask[indY, x] == 0:
if self.position[shift] < self.numberOfHistoryImages:
self.historyImage[indY, x, self.position[shift]] = image[indY, x]
else:
pos = self.position[shift] - self.numberOfHistoryImages
self.historyBuffer[indY, x, pos] = image[indY, x]
shift = shift + 1
indY = indY + self.jump[shift]
# Last column
x = self.width - 1
shift = np.random.randint(0, self.height)
indY = self.jump[shift]
while indY <= self.height - 1:
index = x + indY * self.width
if updating_mask[indY, x] == 0:
if self.position[shift] < self.numberOfHistoryImages:
self.historyImage[indY, x, self.position[shift]] = image[indY, x]
else:
pos = self.position[shift] - self.numberOfHistoryImages
self.historyBuffer[indY, x, pos] = image[indY, x]
shift = shift + 1
indY = indY + self.jump[shift]
# The first pixel
if np.random.randint(0, self.updateFactor) == 0:
if updating_mask[0, 0] == 0:
position = np.random.randint(0, self.numberOfSamples)
if position < self.numberOfHistoryImages:
self.historyImage[0, 0, position] = image[0, 0]
else:
pos = position - self.numberOfHistoryImages
self.historyBuffer[0, 0, pos] = image[0, 0]
# TO DO
class vibe_rgb:
def __init__(self, ):
pass
def init(self, ):
pass
def segmentation(self, ):
pass
def update(self, ):
pass
调用主函数文件
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 18 17:43:58 2020
@filename: vibe_demo.py
@author: curya
"""
import numpy as np
import cv2
import time
from lib_vibe import vibe_gray
cap = cv2.VideoCapture('./TownCentreXVID.avi')
vibe = vibe_gray()
frame_index = 0
segmentation_time = 0
update_time = 0
t1 = time.time()
while True:
ret, frame = cap.read()
if not ret:
break
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if frame_index % 100 == 0:
print('Frame number: %d' % frame_index)
if frame_index == 0:
vibe.AllocInit(gray_frame)
t2 = time.time()
segmentation_map = vibe.Segmentation(gray_frame)
t3 = time.time()
vibe.Update(gray_frame, segmentation_map)
t4 = time.time()
segmentation_time += (t3-t2)
update_time += (t4-t3)
print('Frame %d, segmentation: %.4f, updating: %.4f' % (frame_index, t3-t2, t4-t3))
segmentation_map = cv2.medianBlur(segmentation_map, 3)
cv2.imshow('Actual Frame!', frame)
cv2.imshow('Gray Frame!', gray_frame)
cv2.imshow('Segmentation Frame!', segmentation_map)
frame_index += 1
if cv2.waitKey(1) & 0xFF == ord('q'): # Break while loop if video ends
break
t5 = time.time()
print('All time cost %.3f' % (t5-t1))
print('segmentation time cost: %.3f, update time cost: %.3f' % (segmentation_time, update_time))
cap.release()
cv2.waitKey()
cv2.destroyAllWindows()
运行
python vibe_demo.py
问题
和C代码比较，效率就是个弟弟，跑分辨率不是太高的视频勉强凑合，分辨率太高的视频就真的干看着了，不过和我找的几份别人用python实现的ViBe代码比较，感觉还算是快的。
其次就是发现效率的硬伤之后，也就没去检查代码逻辑是不是和C代码一致，反正是可以检测目标。
还是想办法用Cython复用C代码？我太难了。。。。。。好不容易照着别人的东西造了个轮子，结果轮子不是很圆。:-( Sad