initial commit

test_single_image.py

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