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import cv2
import numpy as np
import torch
# AI-based Visual Memory
class ObjectMemory:
def __init__(self):
self.memory = {} # object_id: feature_vector
self.next_id = 1
def extract_features(self, crop):
try:
crop_resized = cv2.resize(crop, (32, 32)) # Resize to fixed size
crop_tensor = torch.tensor(crop_resized.transpose(2, 0, 1), dtype=torch.float32).unsqueeze(0) / 255.0
return crop_tensor.view(-1) # Flatten
except:
return None
def memorize(self, crop):
vec = self.extract_features(crop)
if vec is None:
return None
obj_id = self.next_id
self.memory[obj_id] = vec
self.next_id += 1
return obj_id
def find_match(self, crop, threshold=0.95):
vec = self.extract_features(crop)
if vec is None:
return None, 0.0
best_id = None
best_sim = 0.0
for obj_id, stored_vec in self.memory.items():
sim = torch.cosine_similarity(vec, stored_vec, dim=0).item()
if sim > best_sim and sim > threshold:
best_sim = sim
best_id = obj_id
return best_id, best_sim
# Video object tracker
def main():
cap = cv2.VideoCapture(0) # Use webcam; change to "video.mp4" for a file
fgbg = cv2.createBackgroundSubtractorMOG2()
memory = ObjectMemory()
while True:
ret, frame = cap.read()
if not ret:
break
fgmask = fgbg.apply(frame)
_, thresh = cv2.threshold(fgmask, 200, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
if cv2.contourArea(cnt) < 800:
continue
x, y, w, h = cv2.boundingRect(cnt)
crop = frame[y:y+h, x:x+w]
match_id, sim = memory.find_match(crop)
if match_id is not None:
label = f"Seen before (ID {match_id})"
color = (0, 255, 0)
else:
new_id = memory.memorize(crop)
label = f"New Object (ID {new_id})"
color = (255, 0, 0)
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
cv2.putText(frame, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.6, (255, 255, 255), 2)
cv2.imshow("Object Tracker with Memory", frame)
if cv2.waitKey(1) & 0xFF == 27: # ESC to quit
break
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
main()
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