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import cv2
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import numpy as np
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import torch
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import torchvision.transforms as transforms
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from torchvision.models import mobilenet_v3_small
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from torch.nn.functional import cosine_similarity
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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class FastFeatureExtractor:
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def __init__(self):
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model = mobilenet_v3_small(pretrained=True).features
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self.model = torch.nn.Sequential(*list(model.children())[:-1]).to(device).eval()
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self.transform = transforms.Compose([
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transforms.ToPILImage(),
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transforms.Resize((96, 96)),
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transforms.ToTensor()
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])
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def extract(self, image):
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try:
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tensor = self.transform(image).unsqueeze(0).to(device)
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with torch.no_grad():
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feat = self.model(tensor).mean([2, 3]).squeeze()
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return feat / feat.norm()
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except:
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return None
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class ObjectMemory:
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def __init__(self, threshold=0.88):
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self.memory = {}
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self.next_id = 1
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self.threshold = threshold
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def match(self, feat):
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best_id, best_sim = None, 0.0
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for obj_id, ref_feat in self.memory.items():
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sim = cosine_similarity(feat, ref_feat, dim=0).item()
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if sim > best_sim and sim > self.threshold:
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best_id, best_sim = obj_id, sim
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return best_id, best_sim
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def add(self, feat):
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obj_id = self.next_id
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self.memory[obj_id] = feat
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self.next_id += 1
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return obj_id
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def main():
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cap = cv2.VideoCapture(0)
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fgbg = cv2.createBackgroundSubtractorMOG2()
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extractor = FastFeatureExtractor()
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memory = ObjectMemory()
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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fg = fgbg.apply(frame)
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_, thresh = cv2.threshold(fg, 200, 255, cv2.THRESH_BINARY)
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contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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for cnt in contours:
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if cv2.contourArea(cnt) < 1200:
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continue
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x, y, w, h = cv2.boundingRect(cnt)
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roi = frame[y:y+h, x:x+w]
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feat = extractor.extract(roi)
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if feat is None:
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continue
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matched_id, similarity = memory.match(feat)
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if matched_id:
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label = f"Known #{matched_id} ({similarity*100:.1f}%)"
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color = (0, 255, 0)
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else:
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new_id = memory.add(feat)
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label = f"New Object #{new_id}"
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color = (0, 0, 255)
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cv2.rectangle(frame, (x, y), (x+w, y+h), color, 2)
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cv2.putText(frame, label, (x, y-8), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
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cv2.imshow("Fast Object Understanding", frame)
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if cv2.waitKey(1) & 0xFF == 27:
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break
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cap.release()
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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main()
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