import numpy as np
import cv2
import sys
from random import randint
##################################################################################
# Escolha do Vídeo
VIDEO_SOURCE = "videos/persons.mp4"
#Definindo as variáveis de cor/fonte aleatoriamente (R,G,B)
TEXT_COLOR = (randint(0, 255), randint(0,255),randint(0,255))
TRACKER_COLOR = (randint(0, 255), randint(0,255),randint(0,255))
FONT = cv2.FONT_HERSHEY_SIMPLEX
# Escolha do Background
BGS_TYPES = ["MOG2", "KNN"]
BGS_TYPE = BGS_TYPES[1]
##################################################################################
# Escolha do Kernel
def getKernel(KERNEL_TYPE):
if KERNEL_TYPE == "dilation":
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
if KERNEL_TYPE == "opening":
kernel = np.ones((3,3), np.uint8)
if KERNEL_TYPE == "closing":
kernel = np.ones((3,3), np.uint8)
return kernel
##################################################################################
# Criação dos Filtros
def getFilter(img, filter):
if filter == 'closing':
return cv2.morphologyEx(img, cv2.MORPH_CLOSE, getKernel("closing"), iterations=2)
if filter == 'opening':
return cv2.morphologyEx(img, cv2.MORPH_OPEN, getKernel("opening"), iterations=2)
if filter == 'dilation':
return cv2.dilate(img, getKernel("dilation"), iterations=2)
if filter =='combine':
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, getKernel("closing"), iterations=2)
opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, getKernel("opening"), iterations=2)
dilation = cv2.dilate(opening, getKernel("dilation"), iterations=2)
return dilation
##################################################################################
def getBGSubtractor(BGS_TYPE):
if BGS_TYPE == "MOG2":
return cv2.createBackgroundSubtractorMOG2()
if BGS_TYPE == "KNN":
return cv2.createBackgroundSubtractorKNN()
print("Detector inválido")
sys.exit(1)
##################################################################################
# Carregando o vídeo
cap = cv2.VideoCapture(VIDEO_SOURCE)
bg_subtractor = getBGSubtractor(BGS_TYPE)
minArea = 150
##################################################################################
# Configuração dos parametros Shitomasi e Lucas Kanade
def SK():
parameters_shitomasi = dict(maxCorners=100, # máx de nós
qualityLevel=0.05, # aumenta pra achar mais e diminui pra achar menos
minDistance=30) # distancia entre pontos
parameters_lucas_kanade = dict(winSize=(15, 15),
maxLevel=2,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.05))
colors = np.random.randint(0, 255, (100, 3))
ret, frame = cap.read()
frame_gray_init = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
edges = cv2.goodFeaturesToTrack(frame_gray_init, mask=None, **parameters_shitomasi)
mask = np.zeros_like(frame)
# print(edges) #localização dos pontos inicias marcados
# print(len(edges)) # contagem de pontos indentificados
# print(mask)
# print(np.shape(mask)) #dimensão do vídeo
while (cap.isOpened):
ok, frame = cap.read()
if not ok:
print("ERRO...")
break
bg_mask = bg_subtractor.apply(frame)
fg_mask = getFilter(bg_mask, 'combine')
result = cv2.bitwise_and(frame, frame, mask=fg_mask)
frame_gray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
new_edges, status, errors = cv2.calcOpticalFlowPyrLK(frame_gray_init, frame_gray, edges, None, **parameters_lucas_kanade)
news = new_edges[status == 1]
olds = edges[status == 1]
for i, (new, old) in enumerate(zip(news, olds)):
a, b = new.ravel()
c, d = old.ravel()
# Não entendi esta parte
# a, b, c, d = int(a), int(b), int(c), int(d)
mask = cv2.line(mask, (a, b), (c, d), colors[i].tolist(), 2)
frame = cv2.circle(result, (a, b), 5, colors[i].tolist(), -1)
img = cv2.add(result, mask)
cv2.imshow('BGS + Optical flow', img)
if cv2.waitKey(1) == 13:
break
frame_gray_init = frame_gray.copy()
edges = news.reshape(-1, 1, 2)
# Acionamento do main()
SK()
