import torch
import glob
import os
import pathlib
from ultralytics import YOLO
model_name='MyBest.pt'
model = torch.hub.load(<?>, 'custom', source='local', path = model_name, force_reload = True)
results = model(person.png) # predict on an image
results.save()
yoloV8: how I can to predict and save the image with boxes on the objects with pytorch
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The code you posted is not complete and there are some missing pieces. To predict and save an image with boxes on the objects using YOLOv8 in PyTorch, you need to follow these steps:
torch.hub.load()function.Here is an example implementation of the code to perform the steps mentioned above:
import torch import cv2 import numpy as np # Load the YOLOv8 model model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) model.eval(); # Load an image img = cv2.imread('person.png') # Preprocess the image img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = img.transpose(2, 0, 1) img = np.ascontiguousarray(img, dtype=np.float32) img /= 255.0 img = torch.from_numpy(img) img = img.unsqueeze(0) # Pass the image to the model for prediction output = model(img) # Extract the predictions from the model output predictions = output[0]['boxes'].data.cpu().numpy() scores = output[0]['scores'].data.cpu().numpy() # Draw boxes around the objects in the image for i, box in enumerate(predictions): if scores[i] < 0.5: continue x1, y1, x2, y2 = box cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2) # Save the image with the boxes cv2.imwrite('person_with_boxes.png', img)