65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
import numpy as np
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import torch
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import torchvision.transforms.functional as F
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import glob
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import os
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from PIL import Image
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from model import create_model
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from confic import NUM_CLASSES, DEVICE, CLASSES, OUTDIR, TRAIN_DIR
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from datasets import create_train_or_test_dataset, create_valid_loader
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from IPython import embed
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from tqdm.auto import tqdm
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import matplotlib.pyplot as plt
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from matplotlib.patches import Rectangle
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def show_sample(img_tensor, outputs, detection_threshold):
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fig, ax = plt.subplots()
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ax.imshow(img_tensor.squeeze().permute(1, 2, 0), aspect='auto')
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for (x0, y0, x1, y1), l, score in zip(outputs[0]['boxes'].cpu(), outputs[0]['labels'].cpu(), outputs[0]['scores'].cpu()):
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if score < detection_threshold:
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continue
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# print(x0, y0, x1, y1, l)
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ax.text(x0, y0, f'{score:.2f}', ha='left', va='bottom', fontsize=12, color='white')
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ax.add_patch(
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Rectangle((x0, y0),
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(x1 - x0),
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(y1 - y0),
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fill=False, color="white", linewidth=2, zorder=10)
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)
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plt.show()
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def infere_model(test_loader, model, detection_th=0.8)
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if __name__ == '__main__':
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model = create_model(num_classes=NUM_CLASSES)
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checkpoint = torch.load(f'{OUTDIR}/best_model.pth', map_location=DEVICE)
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model.load_state_dict(checkpoint["model_state_dict"])
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model.to(DEVICE).eval()
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# test_data = create_train_or_test_dataset(TRAIN_DIR, train=False)
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# test_loader = create_valid_loader(test_data)
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# infere_model(test_loader, model)
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detection_threshold = 0.8
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frame_count = 0
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total_fps = 0
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test_images = glob.glob(f"{TRAIN_DIR}/*.png")
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for i in tqdm(np.arange(len(test_images))):
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image_name = test_images[i].split(os.path.sep)[-1].split('.')[0]
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img = Image.open(test_images[i])
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img_tensor = F.to_tensor(img.convert('RGB')).unsqueeze(dim=0)
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with torch.inference_mode():
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outputs = model(img_tensor.to(DEVICE))
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print(len(outputs[0]['boxes']))
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# show_sample(img_tensor, outputs, detection_threshold) |