113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
import glob
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import os.path
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from os.path import isdir
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from PIL import Image
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import torch
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from torchvision import transforms
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from torchvision.transforms import Resize
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from .utils import load_weight
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from .model import Model
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# CONSTANT
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MEAN = [0.485, 0.456, 0.406]
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STD = [0.229, 0.224, 0.225]
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SIZE = [224, 224]
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CLASSES = {0: "non-occluded",
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1: "occluded"}
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def test_image(opt):
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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model = Model(opt.model, 2, False).to(device)
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model = load_weight(model, opt.weight)
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model.eval()
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# transform data
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transform = transforms.Compose([
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transforms.Resize(SIZE),
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transforms.ToTensor(),
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transforms.Normalize(MEAN, STD)
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])
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# Image
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if isdir(opt.image):
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imgs = glob.glob(os.path.join(opt.image,"*.png"))
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for path in imgs:
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img = Image.open(path).convert("RGB")
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img = transform(img).to(device)
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output = model(img.unsqueeze(0))
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output = torch.softmax(output, 1)
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prob, pred = torch.max(output, 1)
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print("Image {} is {} - {:.2f} %".format(
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path, CLASSES[pred.item()], prob.item() * 100
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))
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else:
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img = Image.open(opt.image).convert("RGB")
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img = transform(img).to(device)
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output = model(img.unsqueeze(0))
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output = torch.softmax(output, 1)
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prob, pred = torch.max(output, 1)
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print("Image {} is {} - {:.2f} %".format(
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opt.image, CLASSES[pred.item()], prob.item() * 100
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))
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def test_node(image:torch.Tensor,length=10,thres=95,model_name="convnext_tiny"):
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weight_dic = {
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"convnext_tiny":"best_convnext_tiny.pth",
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"convnext_base":"best_convnext_base.pth"
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}
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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model = Model(model_name, 2, False).to(device)
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weight = os.path.join(os.path.dirname(os.path.abspath(__file__)),weight_dic[model_name])
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model = load_weight(model, weight)
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model.eval()
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image = image.permute(0,3,1,2)
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torch_resize = Resize([224, 224])
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output = model(torch_resize(image).to(device))
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output = torch.softmax(output, 1)
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prob, pred = torch.max(output, 1)
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probs, preds = [round(i.item() * 100,2) for i in prob], [CLASSES[i.item()] for i in pred]
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print("Image is {} - {} %".format(
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preds, probs
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))
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nums = []
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for idx, a,b in zip(range(len(probs)),preds,probs):
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if a=="non-occluded" and b > thres:
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nums.append(idx)
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start = -1
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end = -1
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period = []
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for idx in range(len(nums)):
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if idx == 0:
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start = nums[idx]
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end = nums[idx]
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else:
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if nums[idx] == end + 1:
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end = nums[idx]
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else:
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if end - start + 1 >= length:
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period.append([start, end])
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start = nums[idx]
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end = nums[idx]
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if end - start + 1 >= length:
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period.append([start, end])
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return (image.permute(0,2,3,1), image.permute(0,2,3,1)[nums,:,:,:], str(preds), str(probs), str(nums), period)
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if __name__ == "__main__":
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from argparse import ArgumentParser
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parser = ArgumentParser()
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parser.add_argument("--model", type=str, help="Model name")
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parser.add_argument("--weight", type=str, help="Weight path (.pth)")
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parser.add_argument("--image", type=str, help="Image path")
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args = parser.parse_args()
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test_image(args)
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