lab09 - YOLO - Básico

YOLO Básico com Ultralytics¶

Neste notebook, vamos explorar os conceitos básicos do YOLO utilizando a biblioteca Ultralytics. Vamos aprender a:

1. Instalar e configurar a biblioteca
2. Carregar modelos pré-treinados
3. Realizar inferência em imagens e vídeos
4. Interpretar e visualizar resultados

1. Instalação e Setup¶

In [ ]:

# Instalação da biblioteca Ultralytics
# !pip install ultralytics
# !pip install opencv-python matplotlib

# Instalação da biblioteca Ultralytics # !pip install ultralytics # !pip install opencv-python matplotlib

In [2]:

# Importação das bibliotecas necessárias
from ultralytics import YOLO
import cv2
import matplotlib.pyplot as plt
import numpy as np

# Importação das bibliotecas necessárias from ultralytics import YOLO import cv2 import matplotlib.pyplot as plt import numpy as np

2. Carregando um modelo pré-treinado¶

In [3]:

# Carregando o modelo YOLOv8n (nano - versão mais leve)
model = YOLO('model/yolov8n.pt')

# Também podemos carregar diferentes tamanhos de modelo:
# model = YOLO('model/yolov8s.pt')  # small
# model = YOLO('model/yolov8m.pt')  # medium
# model = YOLO('model/yolov8l.pt')  # large
# model = YOLO('model/yolov8x.pt')  # extra large

# Carregando o modelo YOLOv8n (nano - versão mais leve) model = YOLO('model/yolov8n.pt') # Também podemos carregar diferentes tamanhos de modelo: # model = YOLO('model/yolov8s.pt') # small # model = YOLO('model/yolov8m.pt') # medium # model = YOLO('model/yolov8l.pt') # large # model = YOLO('model/yolov8x.pt') # extra large

3. Função auxiliar para visualização¶

In [4]:

def visualize_detection(image, results):
    """
    Visualiza a imagem com as detecções
    """
    # Se a imagem for uma string (caminho), carregamos ela
    if isinstance(image, str):
        image = cv2.imread(image)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
    # Plotamos a imagem com as detecções
    plt.figure(figsize=(10, 10))
    plt.imshow(cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB))
    plt.axis('off')
    plt.show()
    
    # Mostramos informações sobre as detecções
    boxes = results[0].boxes
    print(f"Foram detectados {len(boxes)} objetos")
    
    # Exibimos cada detecção
    for i, box in enumerate(boxes):
        cls = int(box.cls[0])
        name = model.names[cls]
        conf = float(box.conf[0])
        print(f"Detecção {i+1}: {name} com confiança {conf:.2f}")

def visualize_detection(image, results): """ Visualiza a imagem com as detecções """ # Se a imagem for uma string (caminho), carregamos ela if isinstance(image, str): image = cv2.imread(image) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Plotamos a imagem com as detecções plt.figure(figsize=(10, 10)) plt.imshow(cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)) plt.axis('off') plt.show() # Mostramos informações sobre as detecções boxes = results[0].boxes print(f"Foram detectados {len(boxes)} objetos") # Exibimos cada detecção for i, box in enumerate(boxes): cls = int(box.cls[0]) name = model.names[cls] conf = float(box.conf[0]) print(f"Detecção {i+1}: {name} com confiança {conf:.2f}")

4. Detecção em uma imagem local¶

In [ ]:

# Baixar uma imagem de exemplo

# no linux ou Google Colab
# !wget -O sample_image.jpg https://ultralytics.com/images/bus.jpg
# no macOS
# !curl -o sample_image.jpg https://ultralytics.com/images/bus.jpg

# pode usar requests para baixar a imagem independente do sistema operacional

import requests
import os

def baixar_arquivo(url, destino):
    """
    Baixa um arquivo a partir de uma URL e salva com o nome especificado.

    Parâmetros:
    - url (str): URL do arquivo a ser baixado.
    - destino (str): Caminho e nome do arquivo onde será salvo.

    Retorna:
    - True se o download foi bem-sucedido, False caso contrário.
    """
    try:
        if os.path.exists(destino):
            print(f"Arquivo já existe: {destino}")
            return True

        resposta = requests.get(url, timeout=10)
        resposta.raise_for_status()  # Levanta erro se status != 200

        with open(destino, "wb") as f:
            f.write(resposta.content)

        print(f"Download concluído: {destino}")
        return True
    except Exception as e:
        print(f"Erro ao baixar o arquivo: {e}")
        return False

url = "https://ultralytics.com/images/bus.jpg"
destino = "lab_images/sample_image.jpg"

baixar_arquivo(url, destino)

# Baixar uma imagem de exemplo # no linux ou Google Colab # !wget -O sample_image.jpg https://ultralytics.com/images/bus.jpg # no macOS # !curl -o sample_image.jpg https://ultralytics.com/images/bus.jpg # pode usar requests para baixar a imagem independente do sistema operacional import requests import os def baixar_arquivo(url, destino): """ Baixa um arquivo a partir de uma URL e salva com o nome especificado. Parâmetros: - url (str): URL do arquivo a ser baixado. - destino (str): Caminho e nome do arquivo onde será salvo. Retorna: - True se o download foi bem-sucedido, False caso contrário. """ try: if os.path.exists(destino): print(f"Arquivo já existe: {destino}") return True resposta = requests.get(url, timeout=10) resposta.raise_for_status() # Levanta erro se status != 200 with open(destino, "wb") as f: f.write(resposta.content) print(f"Download concluído: {destino}") return True except Exception as e: print(f"Erro ao baixar o arquivo: {e}") return False url = "https://ultralytics.com/images/bus.jpg" destino = "lab_images/sample_image.jpg" baixar_arquivo(url, destino)

Download concluído: sample_image.jpg

Out[ ]:

True

In [5]:

# Realizar inferência na imagem
results = model('lab_images/bus.jpg')

# Visualizar os resultados
visualize_detection('lab_images/bus.jpg', results)

# Realizar inferência na imagem results = model('lab_images/bus.jpg') # Visualizar os resultados visualize_detection('lab_images/bus.jpg', results)

image 1/1 /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign, 74.2ms
Speed: 1.8ms preprocess, 74.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 480)

Foram detectados 6 objetos
Detecção 1: bus com confiança 0.87
Detecção 2: person com confiança 0.87
Detecção 3: person com confiança 0.85
Detecção 4: person com confiança 0.83
Detecção 5: person com confiança 0.26
Detecção 6: stop sign com confiança 0.26

5. Explorando os resultados¶

In [7]:

# Examinando o objeto de resultados, no library ultralytics

# Exibindo informações específicas
print("\nInformações específicas do resultado:")

print(f"Tipo: {type(results[0])}")
print(f"Quantidade de boxes: {len(results[0].boxes)}")
# Exibindo informações de cada box
print("\nInformações de cada box:")
for i, box in enumerate(results[0].boxes):
    print(f"Box {i}:")
    print(f"  Coordenadas: {box.xyxy}")
    print(f"  Classe: {box.cls}")
    print(f"  Confiança: {box.conf}")

result = results[0] 

# Bounding boxes (coordenadas no formato [x1, y1, x2, y2])
print("Bounding boxes:")
print(result.boxes.xyxy)

# Classes detectadas
print("\nClasses detectadas:")
print(result.boxes.cls)

# Mapeando classes para nomes
print("\nNomes das classes:")
for c in result.boxes.cls:
    print(model.names[int(c)])

# Scores de confiança
print("\nScores de confiança:")
print(result.boxes.conf)

# Examinando o objeto de resultados, no library ultralytics # Exibindo informações específicas print("\nInformações específicas do resultado:") print(f"Tipo: {type(results[0])}") print(f"Quantidade de boxes: {len(results[0].boxes)}") # Exibindo informações de cada box print("\nInformações de cada box:") for i, box in enumerate(results[0].boxes): print(f"Box {i}:") print(f" Coordenadas: {box.xyxy}") print(f" Classe: {box.cls}") print(f" Confiança: {box.conf}") result = results[0] # Bounding boxes (coordenadas no formato [x1, y1, x2, y2]) print("Bounding boxes:") print(result.boxes.xyxy) # Classes detectadas print("\nClasses detectadas:") print(result.boxes.cls) # Mapeando classes para nomes print("\nNomes das classes:") for c in result.boxes.cls: print(model.names[int(c)]) # Scores de confiança print("\nScores de confiança:") print(result.boxes.conf)

Informações específicas do resultado:
Tipo: <class 'ultralytics.engine.results.Results'>
Quantidade de boxes: 6

Informações de cada box:
Box 0:
  Coordenadas: tensor([[ 22.8713, 231.2773, 805.0027, 756.8405]])
  Classe: tensor([5.])
  Confiança: tensor([0.8734])
Box 1:
  Coordenadas: tensor([[ 48.5504, 398.5523, 245.3456, 902.7027]])
  Classe: tensor([0.])
  Confiança: tensor([0.8657])
Box 2:
  Coordenadas: tensor([[669.4729, 392.1861, 809.7202, 877.0355]])
  Classe: tensor([0.])
  Confiança: tensor([0.8528])
Box 3:
  Coordenadas: tensor([[221.5173, 405.7986, 344.9706, 857.5366]])
  Classe: tensor([0.])
  Confiança: tensor([0.8252])
Box 4:
  Coordenadas: tensor([[  0.0000, 550.5250,  63.0069, 873.4429]])
  Classe: tensor([0.])
  Confiança: tensor([0.2611])
Box 5:
  Coordenadas: tensor([[5.8161e-02, 2.5446e+02, 3.2557e+01, 3.2487e+02]])
  Classe: tensor([11.])
  Confiança: tensor([0.2551])
Bounding boxes:
tensor([[2.2871e+01, 2.3128e+02, 8.0500e+02, 7.5684e+02],
        [4.8550e+01, 3.9855e+02, 2.4535e+02, 9.0270e+02],
        [6.6947e+02, 3.9219e+02, 8.0972e+02, 8.7704e+02],
        [2.2152e+02, 4.0580e+02, 3.4497e+02, 8.5754e+02],
        [0.0000e+00, 5.5053e+02, 6.3007e+01, 8.7344e+02],
        [5.8161e-02, 2.5446e+02, 3.2557e+01, 3.2487e+02]])

Classes detectadas:
tensor([ 5.,  0.,  0.,  0.,  0., 11.])

Nomes das classes:
bus
person
person
person
person
stop sign

Scores de confiança:
tensor([0.8734, 0.8657, 0.8528, 0.8252, 0.2611, 0.2551])

6. Detecção em vídeo¶

In [ ]:

# Baixar um vídeo de exemplo
# - https://drive.google.com/file/d/1RMkFUVCeuuU76-9IDNbVoQr8R1NUS0Di/view?usp=sharing

# # Instale o gdown se necessário (no Colab já costuma estar instalado)
# !pip install -q gdown

# # Baixe o vídeo
# !gdown 1RMkFUVCeuuU76-9IDNbVoQr8R1NUS0Di -O lab_images/race_car.mp4

# Baixar um vídeo de exemplo # - https://drive.google.com/file/d/1RMkFUVCeuuU76-9IDNbVoQr8R1NUS0Di/view?usp=sharing # # Instale o gdown se necessário (no Colab já costuma estar instalado) # !pip install -q gdown # # Baixe o vídeo # !gdown 1RMkFUVCeuuU76-9IDNbVoQr8R1NUS0Di -O lab_images/race_car.mp4

Download concluído: sample_video.mp4

Out[ ]:

True

In [ ]:

# Processar o vídeo
results_video = model('lab_images/pombos.mp4', 
                      conf=0.3, 
                      classes=[13, 14],
                      save=True)


# results_custom = model(
#     'lab_images/bus.jpg',
#     conf=0.5,        # Limiar de confiança (padrão 0.25)
#     iou=0.7,         # Limiar IoU para NMS (padrão 0.45)
#     max_det=20,      # Número máximo de detecções
#     classes=[13, 14]   # Filtrar apenas pessoas (0) e carros (2)
# )

# O vídeo processado estará disponível na pasta 'runs/detect/predict/'

# Processar o vídeo results_video = model('lab_images/pombos.mp4', conf=0.3, classes=[13, 14], save=True) # results_custom = model( # 'lab_images/bus.jpg', # conf=0.5, # Limiar de confiança (padrão 0.25) # iou=0.7, # Limiar IoU para NMS (padrão 0.45) # max_det=20, # Número máximo de detecções # classes=[13, 14] # Filtrar apenas pessoas (0) e carros (2) # ) # O vídeo processado estará disponível na pasta 'runs/detect/predict/'

WARNING ⚠️ 
inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory
errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.

Example:
    results = model(source=..., stream=True)  # generator of Results objects
    for r in results:
        boxes = r.boxes  # Boxes object for bbox outputs
        masks = r.masks  # Masks object for segment masks outputs
        probs = r.probs  # Class probabilities for classification outputs

video 1/1 (frame 1/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 408.2ms
video 1/1 (frame 2/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 19 birds, 389.1ms
video 1/1 (frame 3/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 381.8ms
video 1/1 (frame 4/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 21 birds, 397.8ms
video 1/1 (frame 5/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 394.9ms
video 1/1 (frame 6/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 394.5ms
video 1/1 (frame 7/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 364.9ms
video 1/1 (frame 8/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 366.8ms
video 1/1 (frame 9/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 369.1ms
video 1/1 (frame 10/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 11 birds, 362.9ms
video 1/1 (frame 11/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 364.8ms
video 1/1 (frame 12/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 360.9ms
video 1/1 (frame 13/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 359.1ms
video 1/1 (frame 14/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 14 birds, 364.3ms
video 1/1 (frame 15/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 358.1ms
video 1/1 (frame 16/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 360.6ms
video 1/1 (frame 17/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 359.8ms
video 1/1 (frame 18/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 363.5ms
video 1/1 (frame 19/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 368.4ms
video 1/1 (frame 20/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 372.2ms
video 1/1 (frame 21/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 364.5ms
video 1/1 (frame 22/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 366.7ms
video 1/1 (frame 23/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 367.7ms
video 1/1 (frame 24/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 361.5ms
video 1/1 (frame 25/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 363.3ms
video 1/1 (frame 26/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 361.4ms
video 1/1 (frame 27/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 19 birds, 367.7ms
video 1/1 (frame 28/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 369.9ms
video 1/1 (frame 29/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 19 birds, 361.7ms
video 1/1 (frame 30/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 371.0ms
video 1/1 (frame 31/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 365.2ms
video 1/1 (frame 32/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 23 birds, 401.3ms
video 1/1 (frame 33/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 22 birds, 379.3ms
video 1/1 (frame 34/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 22 birds, 366.8ms
video 1/1 (frame 35/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 22 birds, 364.0ms
video 1/1 (frame 36/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 19 birds, 364.7ms
video 1/1 (frame 37/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 20 birds, 372.0ms
video 1/1 (frame 38/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 361.8ms
video 1/1 (frame 39/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 372.5ms
video 1/1 (frame 40/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 365.2ms
video 1/1 (frame 41/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 363.0ms
video 1/1 (frame 42/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 372.0ms
video 1/1 (frame 43/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 367.6ms
video 1/1 (frame 44/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 362.6ms
video 1/1 (frame 45/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 19 birds, 360.6ms
video 1/1 (frame 46/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 14 birds, 359.4ms
video 1/1 (frame 47/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 364.2ms
video 1/1 (frame 48/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 14 birds, 362.0ms
video 1/1 (frame 49/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 362.8ms
video 1/1 (frame 50/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 371.1ms
video 1/1 (frame 51/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 361.3ms
video 1/1 (frame 52/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 379.4ms
video 1/1 (frame 53/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 367.8ms
video 1/1 (frame 54/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 363.1ms
video 1/1 (frame 55/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 455.8ms
video 1/1 (frame 56/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 368.6ms
video 1/1 (frame 57/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 370.1ms
video 1/1 (frame 58/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 359.3ms
video 1/1 (frame 59/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 366.5ms
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video 1/1 (frame 1238/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 391.7ms
video 1/1 (frame 1239/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 11 birds, 393.0ms
video 1/1 (frame 1240/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 393.0ms
video 1/1 (frame 1241/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 398.6ms
video 1/1 (frame 1242/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 388.6ms
video 1/1 (frame 1243/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 394.0ms
video 1/1 (frame 1244/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 11 birds, 392.5ms
video 1/1 (frame 1245/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 11 birds, 392.6ms
video 1/1 (frame 1246/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 395.2ms
video 1/1 (frame 1247/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 10 birds, 396.6ms
video 1/1 (frame 1248/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 11 birds, 396.3ms
video 1/1 (frame 1249/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 11 birds, 402.3ms
video 1/1 (frame 1250/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 393.5ms
video 1/1 (frame 1251/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 393.8ms
video 1/1 (frame 1252/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 390.0ms
video 1/1 (frame 1253/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 386.3ms
video 1/1 (frame 1254/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 392.7ms
video 1/1 (frame 1255/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 385.7ms
video 1/1 (frame 1256/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 388.2ms
video 1/1 (frame 1257/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 10 birds, 388.6ms
video 1/1 (frame 1258/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 11 birds, 399.0ms
video 1/1 (frame 1259/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 381.5ms
video 1/1 (frame 1260/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 14 birds, 412.8ms
video 1/1 (frame 1261/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 516.4ms
video 1/1 (frame 1262/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 392.5ms
video 1/1 (frame 1263/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 387.9ms
video 1/1 (frame 1264/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 387.9ms
video 1/1 (frame 1265/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 14 birds, 409.4ms
video 1/1 (frame 1266/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 389.7ms
video 1/1 (frame 1267/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 400.4ms
video 1/1 (frame 1268/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 21 birds, 385.3ms
video 1/1 (frame 1269/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 391.0ms
video 1/1 (frame 1270/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 385.9ms
video 1/1 (frame 1271/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 382.5ms
video 1/1 (frame 1272/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 14 birds, 387.0ms
video 1/1 (frame 1273/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 396.3ms
video 1/1 (frame 1274/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 387.6ms
video 1/1 (frame 1275/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 395.2ms
video 1/1 (frame 1276/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 397.0ms
video 1/1 (frame 1277/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 394.2ms
video 1/1 (frame 1278/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 387.3ms
video 1/1 (frame 1279/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 390.5ms
video 1/1 (frame 1280/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 390.3ms
video 1/1 (frame 1281/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 14 birds, 455.8ms
video 1/1 (frame 1282/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 14 birds, 412.4ms
video 1/1 (frame 1283/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 14 birds, 403.6ms
video 1/1 (frame 1284/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 11 birds, 387.9ms
video 1/1 (frame 1285/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 399.4ms
video 1/1 (frame 1286/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 391.9ms
video 1/1 (frame 1287/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 398.8ms
video 1/1 (frame 1288/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 12 birds, 388.7ms
video 1/1 (frame 1289/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 394.6ms
video 1/1 (frame 1290/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 16 birds, 393.4ms
video 1/1 (frame 1291/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 397.5ms
video 1/1 (frame 1292/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 18 birds, 389.5ms
video 1/1 (frame 1293/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 13 birds, 393.2ms
video 1/1 (frame 1294/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 17 birds, 399.4ms
video 1/1 (frame 1295/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 526.4ms
video 1/1 (frame 1296/1296) /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/pombos.mp4: 384x640 15 birds, 603.0ms
Speed: 1.6ms preprocess, 397.0ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)
Results saved to runs/detect/predict3

O Kernel deu pane ao executar o código na célula atual ou em uma célula anterior. 

Analise o código nas células para identificar uma possível causa da pane. 

Clique <a href='https://aka.ms/vscodeJupyterKernelCrash'>aqui</a> para obter mais informações. 

Consulte Jupyter <a href='command:jupyter.viewOutput'>log</a> para obter mais detalhes.

7. Detecção com webcam (se estiver disponível)¶

In [12]:

# Código para executar em ambiente local (não funciona no notebook remoto)

# Inicializar a câmera
cap = cv2.VideoCapture(1)  # 0 para a webcam padrão
# cap = cv2.VideoCapture("race_car.mp4")  # Para usar um vídeo específico
model = YOLO('model/yolov8x.pt')  # Carregar o modelo YOLOv8n
# Verificar se a câmera foi aberta corretamente
if not cap.isOpened():
    print("Erro ao abrir a câmera")
else:
    while True:
        # Capturar frame
        ret, frame = cap.read()
        
        if not ret:
            print("Erro ao capturar o frame")
            break
            
        # Realizar detecção
        results = model(frame)
        # results = model(frame, conf=0.5)
        
        # Desenhar as detecções
        annotated_frame = results[0].plot()
        
        # Exibir a imagem
        cv2.imshow('YOLOv8 Detection', annotated_frame)
        
        # Sair se pressionar 'q'
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
            
    # Liberar recursos
    cap.release()
    cv2.destroyAllWindows()

# Código para executar em ambiente local (não funciona no notebook remoto) # Inicializar a câmera cap = cv2.VideoCapture(1) # 0 para a webcam padrão # cap = cv2.VideoCapture("race_car.mp4") # Para usar um vídeo específico model = YOLO('model/yolov8x.pt') # Carregar o modelo YOLOv8n # Verificar se a câmera foi aberta corretamente if not cap.isOpened(): print("Erro ao abrir a câmera") else: while True: # Capturar frame ret, frame = cap.read() if not ret: print("Erro ao capturar o frame") break # Realizar detecção results = model(frame) # results = model(frame, conf=0.5) # Desenhar as detecções annotated_frame = results[0].plot() # Exibir a imagem cv2.imshow('YOLOv8 Detection', annotated_frame) # Sair se pressionar 'q' if cv2.waitKey(1) & 0xFF == ord('q'): break # Liberar recursos cap.release() cv2.destroyAllWindows()

Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x.pt to 'model/yolov8x.pt'...

100%|██████████| 131M/131M [00:12<00:00, 10.6MB/s] 

0: 384x640 1 person, 409.1ms
Speed: 2.2ms preprocess, 409.1ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

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Speed: 1.5ms preprocess, 391.8ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 369.9ms
Speed: 1.6ms preprocess, 369.9ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

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Speed: 1.8ms preprocess, 373.0ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 374.9ms
Speed: 1.7ms preprocess, 374.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

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Speed: 1.5ms preprocess, 371.4ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 368.6ms
Speed: 1.5ms preprocess, 368.6ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 366.0ms
Speed: 1.5ms preprocess, 366.0ms inference, 10.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 367.1ms
Speed: 1.4ms preprocess, 367.1ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 372.0ms
Speed: 1.5ms preprocess, 372.0ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 371.2ms
Speed: 1.3ms preprocess, 371.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 424.8ms
Speed: 1.3ms preprocess, 424.8ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 384.6ms
Speed: 1.3ms preprocess, 384.6ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 375.2ms
Speed: 1.9ms preprocess, 375.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 458.7ms
Speed: 1.4ms preprocess, 458.7ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 411.0ms
Speed: 1.4ms preprocess, 411.0ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 403.0ms
Speed: 1.8ms preprocess, 403.0ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 397.2ms
Speed: 1.4ms preprocess, 397.2ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 374.9ms
Speed: 1.8ms preprocess, 374.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 362.8ms
Speed: 1.4ms preprocess, 362.8ms inference, 0.9ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 359.6ms
Speed: 1.4ms preprocess, 359.6ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 373.9ms
Speed: 1.6ms preprocess, 373.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 369.4ms
Speed: 1.4ms preprocess, 369.4ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 371.2ms
Speed: 1.3ms preprocess, 371.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 392.5ms
Speed: 1.3ms preprocess, 392.5ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 toothbrush, 362.3ms
Speed: 1.4ms preprocess, 362.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 378.6ms
Speed: 1.3ms preprocess, 378.6ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 372.9ms
Speed: 1.4ms preprocess, 372.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 373.4ms
Speed: 1.6ms preprocess, 373.4ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 368.7ms
Speed: 1.6ms preprocess, 368.7ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 366.9ms
Speed: 1.5ms preprocess, 366.9ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 368.4ms
Speed: 1.4ms preprocess, 368.4ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 378.6ms
Speed: 1.6ms preprocess, 378.6ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 358.0ms
Speed: 1.7ms preprocess, 358.0ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 517.6ms
Speed: 1.7ms preprocess, 517.6ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 toothbrush, 373.9ms
Speed: 1.3ms preprocess, 373.9ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 2 toothbrushs, 364.7ms
Speed: 1.4ms preprocess, 364.7ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 368.4ms
Speed: 1.6ms preprocess, 368.4ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 369.3ms
Speed: 1.3ms preprocess, 369.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 remote, 1 toothbrush, 381.5ms
Speed: 1.6ms preprocess, 381.5ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 369.7ms
Speed: 1.6ms preprocess, 369.7ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 368.6ms
Speed: 1.9ms preprocess, 368.6ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 363.1ms
Speed: 1.7ms preprocess, 363.1ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 358.3ms
Speed: 2.0ms preprocess, 358.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 374.9ms
Speed: 4.3ms preprocess, 374.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 369.1ms
Speed: 1.5ms preprocess, 369.1ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 371.1ms
Speed: 1.3ms preprocess, 371.1ms inference, 1.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 356.9ms
Speed: 1.3ms preprocess, 356.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 375.9ms
Speed: 1.3ms preprocess, 375.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 366.4ms
Speed: 1.8ms preprocess, 366.4ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 392.1ms
Speed: 3.1ms preprocess, 392.1ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 368.5ms
Speed: 1.3ms preprocess, 368.5ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 363.2ms
Speed: 1.4ms preprocess, 363.2ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 376.6ms
Speed: 1.3ms preprocess, 376.6ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 371.9ms
Speed: 1.7ms preprocess, 371.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 371.2ms
Speed: 1.9ms preprocess, 371.2ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 362.4ms
Speed: 2.0ms preprocess, 362.4ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 362.8ms
Speed: 1.9ms preprocess, 362.8ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 363.3ms
Speed: 1.7ms preprocess, 363.3ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 365.8ms
Speed: 1.7ms preprocess, 365.8ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 366.3ms
Speed: 1.7ms preprocess, 366.3ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 369.4ms
Speed: 1.7ms preprocess, 369.4ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 363.5ms
Speed: 1.7ms preprocess, 363.5ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 1 cell phone, 375.6ms
Speed: 1.7ms preprocess, 375.6ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 368.4ms
Speed: 1.6ms preprocess, 368.4ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 403.6ms
Speed: 1.8ms preprocess, 403.6ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 385.0ms
Speed: 1.7ms preprocess, 385.0ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 401.1ms
Speed: 1.8ms preprocess, 401.1ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 397.8ms
Speed: 1.2ms preprocess, 397.8ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 373.2ms
Speed: 1.3ms preprocess, 373.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 372.5ms
Speed: 1.3ms preprocess, 372.5ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 368.1ms
Speed: 2.2ms preprocess, 368.1ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 374.2ms
Speed: 1.3ms preprocess, 374.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 370.9ms
Speed: 1.4ms preprocess, 370.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 cell phone, 372.5ms
Speed: 1.3ms preprocess, 372.5ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 1 cell phone, 369.3ms
Speed: 1.4ms preprocess, 369.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 1 bottle, 1 cell phone, 373.2ms
Speed: 1.3ms preprocess, 373.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 375.3ms
Speed: 1.2ms preprocess, 375.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 376.6ms
Speed: 1.4ms preprocess, 376.6ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 358.9ms
Speed: 1.6ms preprocess, 358.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 381.1ms
Speed: 1.7ms preprocess, 381.1ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 371.3ms
Speed: 1.3ms preprocess, 371.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 381.7ms
Speed: 1.4ms preprocess, 381.7ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 366.6ms
Speed: 1.6ms preprocess, 366.6ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 362.8ms
Speed: 1.5ms preprocess, 362.8ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 380.5ms
Speed: 1.6ms preprocess, 380.5ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 376.4ms
Speed: 1.4ms preprocess, 376.4ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 386.1ms
Speed: 1.3ms preprocess, 386.1ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 371.5ms
Speed: 1.6ms preprocess, 371.5ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 377.7ms
Speed: 1.3ms preprocess, 377.7ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 364.4ms
Speed: 1.9ms preprocess, 364.4ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 370.0ms
Speed: 1.8ms preprocess, 370.0ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 364.3ms
Speed: 1.7ms preprocess, 364.3ms inference, 1.0ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 372.5ms
Speed: 2.3ms preprocess, 372.5ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 362.7ms
Speed: 2.2ms preprocess, 362.7ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 369.0ms
Speed: 1.9ms preprocess, 369.0ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 368.7ms
Speed: 1.7ms preprocess, 368.7ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 375.2ms
Speed: 3.5ms preprocess, 375.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 421.0ms
Speed: 1.9ms preprocess, 421.0ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 370.4ms
Speed: 1.7ms preprocess, 370.4ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 374.0ms
Speed: 1.4ms preprocess, 374.0ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 364.0ms
Speed: 1.4ms preprocess, 364.0ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 396.2ms
Speed: 1.4ms preprocess, 396.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 379.2ms
Speed: 1.7ms preprocess, 379.2ms inference, 1.0ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 373.9ms
Speed: 1.4ms preprocess, 373.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 370.6ms
Speed: 1.3ms preprocess, 370.6ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 363.8ms
Speed: 1.3ms preprocess, 363.8ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 397.7ms
Speed: 1.3ms preprocess, 397.7ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 368.3ms
Speed: 1.5ms preprocess, 368.3ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 375.7ms
Speed: 1.3ms preprocess, 375.7ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 372.4ms
Speed: 1.6ms preprocess, 372.4ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 391.3ms
Speed: 1.8ms preprocess, 391.3ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 379.3ms
Speed: 1.4ms preprocess, 379.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 386.6ms
Speed: 1.7ms preprocess, 386.6ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 backpack, 2 potted plants, 1 tv, 391.4ms
Speed: 1.8ms preprocess, 391.4ms inference, 1.0ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 tv, 1 teddy bear, 378.0ms
Speed: 1.5ms preprocess, 378.0ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 394.0ms
Speed: 1.4ms preprocess, 394.0ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 377.8ms
Speed: 1.9ms preprocess, 377.8ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 385.8ms
Speed: 1.6ms preprocess, 385.8ms inference, 1.1ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 giraffe, 1 handbag, 1 chair, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 379.4ms
Speed: 1.6ms preprocess, 379.4ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 giraffe, 1 handbag, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 375.8ms
Speed: 1.6ms preprocess, 375.8ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 380.8ms
Speed: 1.4ms preprocess, 380.8ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 remote, 1 teddy bear, 376.6ms
Speed: 1.3ms preprocess, 376.6ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 tv, 1 remote, 1 teddy bear, 373.8ms
Speed: 1.6ms preprocess, 373.8ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 giraffe, 1 handbag, 1 chair, 2 potted plants, 1 tv, 1 remote, 1 teddy bear, 386.0ms
Speed: 1.4ms preprocess, 386.0ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 372.4ms
Speed: 1.3ms preprocess, 372.4ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 giraffe, 1 handbag, 1 chair, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 379.3ms
Speed: 1.5ms preprocess, 379.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 373.9ms
Speed: 1.9ms preprocess, 373.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 375.5ms
Speed: 1.5ms preprocess, 375.5ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 371.7ms
Speed: 1.3ms preprocess, 371.7ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 giraffe, 1 handbag, 1 wine glass, 2 potted plants, 1 tv, 2 teddy bears, 381.5ms
Speed: 1.5ms preprocess, 381.5ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 386.4ms
Speed: 1.4ms preprocess, 386.4ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 370.9ms
Speed: 1.7ms preprocess, 370.9ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 378.9ms
Speed: 1.7ms preprocess, 378.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 giraffe, 1 handbag, 1 chair, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 368.0ms
Speed: 1.4ms preprocess, 368.0ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 378.5ms
Speed: 1.5ms preprocess, 378.5ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 371.9ms
Speed: 1.6ms preprocess, 371.9ms inference, 1.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 373.0ms
Speed: 1.9ms preprocess, 373.0ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 363.4ms
Speed: 1.7ms preprocess, 363.4ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 tv, 1 teddy bear, 372.1ms
Speed: 2.1ms preprocess, 372.1ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 tv, 2 teddy bears, 395.4ms
Speed: 1.5ms preprocess, 395.4ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 367.0ms
Speed: 1.5ms preprocess, 367.0ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 378.5ms
Speed: 1.4ms preprocess, 378.5ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 tv, 1 teddy bear, 372.4ms
Speed: 1.5ms preprocess, 372.4ms inference, 0.9ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 chair, 2 potted plants, 1 tv, 1 teddy bear, 377.8ms
Speed: 2.2ms preprocess, 377.8ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 tv, 1 teddy bear, 426.5ms
Speed: 1.4ms preprocess, 426.5ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 375.4ms
Speed: 1.7ms preprocess, 375.4ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 378.0ms
Speed: 2.4ms preprocess, 378.0ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 367.2ms
Speed: 1.5ms preprocess, 367.2ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 380.1ms
Speed: 1.4ms preprocess, 380.1ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 giraffe, 1 handbag, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 369.5ms
Speed: 1.3ms preprocess, 369.5ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 giraffe, 1 handbag, 2 potted plants, 1 bed, 1 tv, 3 teddy bears, 387.4ms
Speed: 1.5ms preprocess, 387.4ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 giraffe, 1 handbag, 1 chair, 2 potted plants, 1 tv, 1 teddy bear, 382.1ms
Speed: 1.4ms preprocess, 382.1ms inference, 2.7ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 2 potted plants, 1 bed, 1 tv, 1 teddy bear, 373.2ms
Speed: 2.0ms preprocess, 373.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 bottle, 2 potted plants, 1 bed, 1 tv, 2 teddy bears, 381.7ms
Speed: 1.5ms preprocess, 381.7ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 bottle, 1 potted plant, 1 bed, 1 tv, 365.9ms
Speed: 1.4ms preprocess, 365.9ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 handbag, 1 potted plant, 1 bed, 1 tv, 405.2ms
Speed: 1.4ms preprocess, 405.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 378.1ms
Speed: 1.7ms preprocess, 378.1ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 384.8ms
Speed: 1.8ms preprocess, 384.8ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 385.1ms
Speed: 1.3ms preprocess, 385.1ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 378.3ms
Speed: 1.4ms preprocess, 378.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 395.5ms
Speed: 2.3ms preprocess, 395.5ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 380.5ms
Speed: 1.6ms preprocess, 380.5ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 379.3ms
Speed: 1.7ms preprocess, 379.3ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 383.6ms
Speed: 1.4ms preprocess, 383.6ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 383.1ms
Speed: 1.6ms preprocess, 383.1ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 377.3ms
Speed: 1.5ms preprocess, 377.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 389.0ms
Speed: 1.7ms preprocess, 389.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 376.7ms
Speed: 1.5ms preprocess, 376.7ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 388.6ms
Speed: 1.8ms preprocess, 388.6ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 384.8ms
Speed: 1.4ms preprocess, 384.8ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 377.1ms
Speed: 1.5ms preprocess, 377.1ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 375.8ms
Speed: 1.6ms preprocess, 375.8ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 365.2ms
Speed: 1.7ms preprocess, 365.2ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 386.0ms
Speed: 2.0ms preprocess, 386.0ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 2 persons, 540.7ms
Speed: 1.4ms preprocess, 540.7ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 386.7ms
Speed: 1.7ms preprocess, 386.7ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 378.1ms
Speed: 1.3ms preprocess, 378.1ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 388.9ms
Speed: 1.7ms preprocess, 388.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 377.6ms
Speed: 1.6ms preprocess, 377.6ms inference, 0.9ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 372.0ms
Speed: 1.4ms preprocess, 372.0ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 374.1ms
Speed: 1.4ms preprocess, 374.1ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 374.9ms
Speed: 1.7ms preprocess, 374.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 375.9ms
Speed: 1.4ms preprocess, 375.9ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 374.6ms
Speed: 2.0ms preprocess, 374.6ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)

0: 384x640 1 person, 376.4ms
Speed: 1.7ms preprocess, 376.4ms inference, 0.5ms postprocess per image at shape (1, 3, 384, 640)

8. Ajustando os parâmetros de inferência¶

In [ ]:

# Realizar inferência com parâmetros personalizados
results_custom = model(
    'lab_images/bus.jpg',
    conf=0.5,        # Limiar de confiança (padrão 0.25)
    iou=0.7,         # Limiar IoU para NMS (padrão 0.45)
    max_det=20,      # Número máximo de detecções
    classes=[13, 14]   # Filtrar apenas pessoas (0) e carros (2)
)

# Visualizar os resultados
visualize_detection('lab_images/bus.jpg', results_custom)

# Realizar inferência com parâmetros personalizados results_custom = model( 'lab_images/bus.jpg', conf=0.5, # Limiar de confiança (padrão 0.25) iou=0.7, # Limiar IoU para NMS (padrão 0.45) max_det=20, # Número máximo de detecções classes=[13, 14] # Filtrar apenas pessoas (0) e carros (2) ) # Visualizar os resultados visualize_detection('lab_images/bus.jpg', results_custom)

image 1/1 /Users/arnaldoalvesvianajunior/shift-fiap/10-lab18-yolo/lab_images/bus.jpg: 640x480 3 persons, 75.2ms
Speed: 2.7ms preprocess, 75.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 480)

Foram detectados 3 objetos
Detecção 1: person com confiança 0.87
Detecção 2: person com confiança 0.85
Detecção 3: person com confiança 0.83

9. Lista de classes COCO (conjunto de dados padrão)¶

In [13]:

# Listar todas as classes que o modelo pode detectar
for idx, class_name in model.names.items():
    print(f"{idx}: {class_name}")

# Listar todas as classes que o modelo pode detectar for idx, class_name in model.names.items(): print(f"{idx}: {class_name}")

0: person
1: bicycle
2: car
3: motorcycle
4: airplane
5: bus
6: train
7: truck
8: boat
9: traffic light
10: fire hydrant
11: stop sign
12: parking meter
13: bench
14: bird
15: cat
16: dog
17: horse
18: sheep
19: cow
20: elephant
21: bear
22: zebra
23: giraffe
24: backpack
25: umbrella
26: handbag
27: tie
28: suitcase
29: frisbee
30: skis
31: snowboard
32: sports ball
33: kite
34: baseball bat
35: baseball glove
36: skateboard
37: surfboard
38: tennis racket
39: bottle
40: wine glass
41: cup
42: fork
43: knife
44: spoon
45: bowl
46: banana
47: apple
48: sandwich
49: orange
50: broccoli
51: carrot
52: hot dog
53: pizza
54: donut
55: cake
56: chair
57: couch
58: potted plant
59: bed
60: dining table
61: toilet
62: tv
63: laptop
64: mouse
65: remote
66: keyboard
67: cell phone
68: microwave
69: oven
70: toaster
71: sink
72: refrigerator
73: book
74: clock
75: vase
76: scissors
77: teddy bear
78: hair drier
79: toothbrush

In [ ]:

In [ ]:

In [5]:

from ultralytics import YOLO
import ultralytics
import cv2
import matplotlib.pyplot as plt
import numpy as np

from ultralytics import YOLO import ultralytics import cv2 import matplotlib.pyplot as plt import numpy as np

In [2]:

ultralytics.checks()

ultralytics.checks()

Ultralytics 8.3.134 🚀 Python-3.9.6 torch-2.3.0 CPU (Apple M2)
Setup complete ✅ (8 CPUs, 16.0 GB RAM, 294.2/460.4 GB disk)

In [7]:

imagem= cv2.imread("lab_images/bus.jpg", cv2.IMREAD_COLOR)


model = YOLO('model/yolov8n.pt')
results = model.predict(imagem, conf=0.25)

image_result = results[0].plot()
image_result = cv2.cvtColor(image_result, cv2.COLOR_BGR2RGB)

plt.imshow(image_result)

imagem= cv2.imread("lab_images/bus.jpg", cv2.IMREAD_COLOR) model = YOLO('model/yolov8n.pt') results = model.predict(imagem, conf=0.25) image_result = results[0].plot() image_result = cv2.cvtColor(image_result, cv2.COLOR_BGR2RGB) plt.imshow(image_result)

0: 640x480 4 persons, 1 bus, 1 stop sign, 67.2ms
Speed: 2.8ms preprocess, 67.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 480)

Out[7]:

<matplotlib.image.AxesImage at 0x135d5ee50>