import cv2
import 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 = None
class Node:
 def __init__(self):
  self.pixel_s = None
  self.pixel_r = None
  self.no_of_components = 0
  self.Next = None
class Node1:
 def __init__(self):
  self.gauss = None
  self.no_of_comp = 0
  self.Next = None
covariance0 = 11.0
def 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 ptr
def 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_ptr
List_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 frame
capture = cv2.VideoCapture('1.mp4')
ret, orig_frame = capture.read( )
if ret is True:
 value1 = Process(0.1, denoise(orig_frame))
 run = True
else:
 run = False
while (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()

import cv2
#構(gòu)造VideoCapture對象
cap = cv2.VideoCapture('1.mp4')
# 創(chuàng)建一個背景分割器
# createBackgroundSubtractorMOG2()函數(shù)里，可以指定detectShadows的值
# detectShadows=True，表示檢測陰影，反之不檢測陰影。默認是true
fgbg  = cv2.createBackgroundSubtractorMOG2()
while True :
 ret, frame = cap.read() # 讀取視頻
 fgmask = fgbg.apply(frame) # 背景分割
 cv2.imshow('frame', fgmask) # 顯示分割結(jié)果
 if cv2.waitKey(100) & 0xff == ord('q'):
  break
cap.release()
cv2.destroyAllWindows()