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import cv2
import numpy as np
import torch
import torchvision.transforms as transforms
from torchvision.models import mobilenet_v3_small
from torch.nn.functional import cosine_similarity
# Use GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Lightweight feature extractor using MobileNetV3
class FastFeatureExtractor:
def __init__(self):
model = mobilenet_v3_small(pretrained=True).features
self.model = torch.nn.Sequential(*list(model.children())[:-1]).to(device).eval()
self.transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((96, 96)),
transforms.ToTensor()
])
def extract(self, image):
try:
tensor = self.transform(image).unsqueeze(0).to(device)
with torch.no_grad():
feat = self.model(tensor).mean([2, 3]).squeeze()
return feat / feat.norm()
except:
return None
# Simple memory with similarity threshold
class ObjectMemory:
def __init__(self, threshold=0.88):
self.memory = {}
self.next_id = 1
self.threshold = threshold
def match(self, feat):
best_id, best_sim = None, 0.0
for obj_id, ref_feat in self.memory.items():
sim = cosine_similarity(feat, ref_feat, dim=0).item()
if sim > best_sim and sim > self.threshold:
best_id, best_sim = obj_id, sim
return best_id, best_sim
def add(self, feat):
obj_id = self.next_id
self.memory[obj_id] = feat
self.next_id += 1
return obj_id
# Main app
def main():
cap = cv2.VideoCapture(0)
fgbg = cv2.createBackgroundSubtractorMOG2()
extractor = FastFeatureExtractor()
memory = ObjectMemory()
while True:
ret, frame = cap.read()
if not ret:
break
fg = fgbg.apply(frame)
_, thresh = cv2.threshold(fg, 200, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
if cv2.contourArea(cnt) < 1200:
continue
x, y, w, h = cv2.boundingRect(cnt)
roi = frame[y:y+h, x:x+w]
feat = extractor.extract(roi)
if feat is None:
continue
matched_id, similarity = memory.match(feat)
if matched_id:
label = f"Known #{matched_id} ({similarity*100:.1f}%)"
color = (0, 255, 0)
else:
new_id = memory.add(feat)
label = f"New Object #{new_id}"
color = (0, 0, 255)
cv2.rectangle(frame, (x, y), (x+w, y+h), color, 2)
cv2.putText(frame, label, (x, y-8), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
cv2.imshow("Fast Object Understanding", frame)
if cv2.waitKey(1) & 0xFF == 27: # ESC to exit
break
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
main()
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