Transformar “máscara” em vídeo

[PH1005]

Boa tarde , me chamo Pedro Henrique, sou assinante da IA Expert (inclusive, gostaria de comentar que o curso é excelente), venho por meio deste fórum perguntar se alguém poderia me auxiliar com uma dúvida. No caso, estou tentando mesclar codificações das aulas de detecção de movimento e de rastreamento de objetos, utilizando python e openCV. Bom , minha dúvida é o seguinte, queria saber se é possível aplicar os parâmetros de shitomasi e lucas kanade em uma máscara retirada de um algoritmo de subtração de background, já estou tentando fazer essa junção, porém não consigo fazer com que o algoritmo shitomasi-kanade “leia” a máscara obtida como um vídeo.

cap = cv2.VideoCapture("result")

(sendo “result” a máscara do algoritmo de subtração)

Para um entendimento mais fácil da situação, estou enviando o código.

import numpy as np
import cv2
import sys

##################################################################################

# Escolha do Vídeo
VIDEO_SOURCE = "videos/Video_5.mp4"

#Definindo as variáveis de cor/fonte

TEXT_COLOR = (0, 255, 0)
TRACKER_COLOR = (255, 0, 0)
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)

cap = cv2.VideoCapture(VIDEO_SOURCE)
bg_subtractor = getBGSubtractor(BGS_TYPE)
minArea = 150

##################################################################################

# Configuração dos parametros Shitomasi e Lucas Kanade
def SK(result):
cap = cv2.VideoCapture("result")
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, frameSK = cap.read()
frame_gray_init = cv2.cvtColor(frameSK, cv2.COLOR_BGR2GRAY)

edges = cv2.goodFeaturesToTrack(frame_gray_init, mask=None, **parameters_shitomasi)
# print(edges) #localização dos pontos inicias marcados
print(len(edges)) # contagem de pontos indentificados

maskSK = np.zeros_like(frameSK)
# print(maskSK)
# print(np.shape(maskSK)) #dimensão do vídeo

while True:
ret, frameSK = cap.read()
frame_gray = cv2.cvtColor(frameSK, 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()

a, b, c, d = int(a), int(b), int(c), int(d)

maskSK = cv2.line(maskSK, (a, b), (c, d), colors[i].tolist(), 2)

frameSK = cv2.circle(frameSK, (a, b), 5, colors[i].tolist(), -1)

img = cv2.add(frameSK, maskSK)

cv2.imshow('Optical flow', img)
# cv2.imshow('maskSK',maskSK)
# cv2.imshow('frameSK',frameSK)
if cv2.waitKey(1) == 13:
break

frame_gray_init = frame_gray.copy()
edges = news.reshape(-1, 1, 2)

cv2.destroyAllWindows()
cap.release()


##################################################################################

def main():
while (cap.isOpened):

ok,frameBG = cap.read()
if not ok:
print("ERRO")
break

frameBG = cv2.resize(frameBG, (0,0), fx=0.50, fy=0.50)

bg_mask = bg_subtractor.apply(frameBG)
bg_mask = getFilter(bg_mask,'combine')
bg_mask = cv2.medianBlur(bg_mask, 5)

####################################################################################################

# Aplicação dos contornos
(contours, hierarchy) = cv2.findContours(bg_mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

for cnt in contours:
area = cv2.contourArea(cnt)
if area>= minArea:
x,y,w,h = cv2.boundingRect(cnt)
cv2.drawContours(frameBG, [cnt], -1, TRACKER_COLOR, 2)
cv2.drawContours(frameBG, [cnt], -1, (255, 255, 255), 1)

####################################################################################################

result = cv2.bitwise_and(frameBG,frameBG,mask=bg_mask)
#cv2.imshow('frameBG', frameBG)
#cv2.imshow('Mask', result)

####################################################################################################

# Aplicação dos parametros shitomasi e kanade
SK(result)
####################################################################################################

# Configuração da tecla de saída
#if cv2.waitKey(1) & 0xFF == ord("q"):
# break
if cv2.waitKey(1) == 13:
break

# Liberação das janelas de vídeo
cv2.destroyAllWindows()
cap.release()

# Acionamento do main()
main()

[Dalton Vargas]

Olá Pedro!

Parabéns pela ideia do projeto, é bem interessante!

Pelo que entendi, a ideia é aplicar o algoritmo subtrator de fundo e depois usar o resultado para o algoritmo Optical Flow rastrear, seria isto?

Se sim, pelos fontes que você mandou, você estava chegando lá, só faltavam ajustar os parâmetros das imagens usadas entre os dois algoritmos.

Fiz uma implementação simples baseada nesta ideia, vou mandar na próxima resposta aqui. Provavelmente você terá que ajustar os parâmetros de cada algoritmo de acordo com seu projeto, pois deixei a parametrização padrão de cada um.

[Dalton Vargas]

import numpy as np
import cv2
import sys
from random import randint

# Criar um vetor para gerar cores aleatoriamente (R,G,B)
TEXT_COLOR = (randint(0, 255), randint(0, 255), randint(0, 255))
BORDER_COLOR = (randint(0, 255), randint(0, 255), randint(0, 255))
FONT = cv2.FONT_HERSHEY_SIMPLEX
VIDEO_SOURCE = "videos/walking.avi"

# vetor de background subtractors
BGS_TYPES = ["GMG", "MOG", "MOG2", "KNN", "CNT"]

# definir qual BGS usar, inserindo o número que corresponde ao BGS selecionado no vetor de background subtractors
# 0 = GMG, 1 = MOG, 2 = MOG2, 3 = KNN,  4 = CNT
BGS_TYPE = BGS_TYPES[2]

# Kernel: Elemento estruturante
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

# Filtros morfológicos para redução do ruído
def getFilter(img, filter):
    '''            
        Esses filtros são escolhidos a dedo, apenas com base em testes visuais
    '''
    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 == "GMG":
        return cv2.bgsegm.createBackgroundSubtractorGMG(initializationFrames=120, decisionThreshold=.8)  
    if BGS_TYPE == "MOG": 
        return cv2.bgsegm.createBackgroundSubtractorMOG(history=200, nmixtures=5, backgroundRatio=.7, noiseSigma=0)
    if BGS_TYPE == "MOG2":
        return cv2.createBackgroundSubtractorMOG2(history=500, detectShadows=True, varThreshold=100)  
    if BGS_TYPE == "KNN":
        return cv2.createBackgroundSubtractorKNN(history=500, dist2Threshold=400, detectShadows=True)    
    if BGS_TYPE == "CNT":
        return cv2.bgsegm.createBackgroundSubtractorCNT(minPixelStability=15, useHistory=True, maxPixelStability=15*60, isParallel=True)
    print("Unknown createBackgroundSubtractor type")
    sys.exit(1)

# Carregar o video
cap = cv2.VideoCapture(VIDEO_SOURCE)

bg_subtractor = getBGSubtractor(BGS_TYPE)

def SubtractorTracker():
    # Inicialização do TRACKER
    parameters_shitomasi = dict(maxCorners = 100,
                                qualityLevel = 0.3,
                                minDistance = 7)
    parameters_lucas_kanade = dict(winSize = (15, 15),
                                   maxLevel = 2,
                                   criteria = (cv2.TERM_CRITERIA_EPS | cv2. TERM_CRITERIA_COUNT, 10, 0.03))
    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)

    while (cap.isOpened):

        ok, frame = cap.read()
        
        if not ok:
            print("Frame capture failed, stopping...")
            break

        bg_mask = bg_subtractor.apply(frame)
        fg_mask = getFilter(bg_mask, 'combine')

        # Resultado da subtração de fundo
        result = cv2.bitwise_and(frame, frame, mask=fg_mask)

        # Passamos como parâmetro ao Tracker o resultado do BGS
        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()

            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)

SubtractorTracker()

• Esta resposta foi modificada 3 anos atrás por Dalton Vargas.

[PH1005]

Olá Dalton, com o seu algoritmo consegui avançar um pouco, consigo até visualizar a tela preta com as linhas formadas, porém a janela trava e não consigo rodar o vídeo, acabei obtendo outro problema que foge do meu conhecimento, normalmente eu pesquiso mas não entendi oque realmente é esse erro. No caso o erro é o seguinte:

Traceback (most recent call last):
File “C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.9_3.9.2800.0_x64__qbz5n2kfra8p0\lib\code.py”, line 90, in runcode
exec(code, self.locals)
File “<input>”, line 1, in <module>
File “C:\Program Files\JetBrains\PyCharm 2021.3.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py”, line 198, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File “C:\Program Files\JetBrains\PyCharm 2021.3.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py”, line 18, in execfile
exec(compile(contents+”\n”, file, ‘exec’), glob, loc)
File “C:/Users/pedro/PycharmProjects/pythonProject1/TESTE IA.py”, line 132, in <module>
SubtractorTracker()
File “C:/Users/pedro/PycharmProjects/pythonProject1/TESTE IA.py”, line 109, in SubtractorTracker
news = new_edges[status == 1]
TypeError: ‘NoneType’ object is not subscriptable

Desculpe ficar pedindo ajuda, mas se puder me auxiliar, ficarei muito agradecido.

[Dalton Vargas]

Pedro,

poderia compartilhar o fonte completo? Assim consigo analisar e testar aqui também.

Ah e quando for mandar o fonte, por favor não esqueça de mandar em uma mensagem separada, adicionando como bloco de código, assim o script não perde a formatação original.

 

• Esta resposta foi modificada 3 anos atrás por Dalton Vargas.

[PH1005]

Oi Dalton, consigo sim.

import numpy as np
import cv2
import sys
from random import randint

##################################################################################

# Escolha do Vídeo
VIDEO_SOURCE = "videos/Video_5.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')

# Resultado da subtração de fundo
result = cv2.bitwise_and(frame, frame, mask=fg_mask)

# Passamos como parâmetro ao Tracker o resultado do BGS
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()

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(frame, (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)

cv2.destroyAllWindows()
cap.release()

# Acionamento do main()

SK()

##################################################################################


ERRO: 

Traceback (most recent call last):
File "C:\Users\Pedro Henrique\PycharmProjects\IC\main.py", line 137, in <module>
SK()
File "C:\Users\Pedro Henrique\PycharmProjects\IC\main.py", line 109, in SK
news = new_edges[status == 1]
TypeError: 'NoneType' object is not subscriptable

 

[Dalton Vargas]

Opa!

Fiz a correção e teste com seu script, segue o link de um vídeo de demontração:

Demonstração

Na próxima mensagem vou postar o script.

 

[Dalton Vargas]

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()

[PH1005]

Olá Dalton, vi seu vídeo e de fato está rodando, mas no meu vídeo teste eu não consigo rodar esse código, fico com o mesmo erro que mandei anteriormente. Vou mandar meu vídeo para ver se você consegue encontrar o problema, o algoritmo é o mesmo que você mandou.

https://drive.google.com/file/d/1WXv7sMaxNtVae3rQkQfoc4gn7amuIFGR/view?usp=sharing

Ah, e a linha que eu passo as variáveis para inteiros foi só uma passagem porque anteriormente estava dando erro também, acho que para o vídeo que estou usando existem alguns problemas e não tô sabendo resolver. Vou mandar o Erro de novo também.

Traceback (most recent call last):
File “C:\Users\Pedro Henrique\PycharmProjects\IC\teste1.py”, line 131, in <module>
SK()
File “C:\Users\Pedro Henrique\PycharmProjects\IC\teste1.py”, line 107, in SK
news = new_edges[status == 1]
TypeError: ‘NoneType’ object is not subscriptable

Process finished with exit code 1

[Dalton Vargas]

Olá Pedro!

Fiz o teste aplicando somente o Optical Flow e parece que devido aos insetos estarem desfocados desde o início do vídeo, o algoritmo não consegue indetificar os contornos/arestas. Já nos objetos que estão mais nítidos (marca da câmera, tempo de vídeo, camisa branca ao fundo) ele consegue identificar. Segue o vídeo .

Minha sugestão seria gravar um vídeo mais focado nos insetos (se estes forem o objetivo).

Também se puder compartilhar a ideia do projeto, assim podemos pensar em mais soluções. Caso não queira compartilhar aqui no fórum, pode mandar no meu e-mail: daltonluizvargas@hotmail.com

• Esta resposta foi modificada 2 anos, 12 meses atrás por Dalton Vargas.

[Autor]

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