99 lines
3.3 KiB
Python
99 lines
3.3 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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import argparse
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from model import create_model
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from confic import NUM_CLASSES, DEVICE, CLASSES, OUTDIR, DATA_DIR, INFERENCE_OUTDIR, IMG_DPI, IMG_SIZE
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from datasets import InferenceDataset, create_inference_loader
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from IPython import embed
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from pathlib import Path
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from tqdm.auto import tqdm
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from matplotlib.patches import Rectangle
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def plot_inference(img_tensor, img_name, output, detection_threshold, dataset_name):
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fig = plt.figure(figsize=IMG_SIZE, num=img_name)
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gs = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1) #
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ax = fig.add_subplot(gs[0, 0])
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ax.imshow(img_tensor.cpu().squeeze().permute(1, 2, 0), aspect='auto')
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for (x0, y0, x1, y1), l, score in zip(output['boxes'].cpu(), output['labels'].cpu(), output['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="tab:green", linestyle='--', linewidth=2, zorder=10)
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)
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ax.set_axis_off()
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plt.savefig(Path(INFERENCE_OUTDIR)/dataset_name/(os.path.splitext(img_name)[0] +'_inferred.png'), dpi=IMG_DPI)
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plt.close()
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# plt.show()
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def infere_model(inference_loader, model, dataset_name, detection_th=0.8):
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print(f'Inference on dataset: {dataset_name}')
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prog_bar = tqdm(inference_loader, total=len(inference_loader))
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for samples, img_names in prog_bar:
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images = list(image.to(DEVICE) for image in samples)
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# img_names = [t['image_name'] for t in targets]
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with torch.inference_mode():
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outputs = model(images)
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for image, img_name, output in zip(images, img_names, outputs):
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plot_inference(image, img_name, output, detection_th, dataset_name)
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def main(args):
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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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inference_data = InferenceDataset(args.folder)
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inference_loader = create_inference_loader(inference_data)
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dataset_name = Path(args.folder).name
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infere_model(inference_loader, model, dataset_name)
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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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#
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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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#
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# with torch.inference_mode():
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# outputs = model(img_tensor.to(DEVICE))
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#
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# print(len(outputs[0]['boxes']))
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# show_sample(img_tensor, outputs, detection_threshold)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Evaluated electrode array recordings with multiple fish.')
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parser.add_argument('folder', type=str, help='folder to infer picutes', default='')
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args = parser.parse_args()
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main(args)
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