Tracker & Spatie
Hey, ever wondered if a neural net could help pick the perfect shot of a hawk mid‑flight? I've been training a model on motion blur patterns, and I think it could match your patience with some code.
That sounds wild—if the net can spot the blur before it hits, I could set up my tripod and lens just right. Show me the code and let’s see if it can beat my patience.
Sure thing, just paste this in a Python file and run it with a webcam feed. It’s a minimal example that watches for motion‑blur and raises a flag when the blur is high enough.
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
prev = None
while True:
ret, frame = cap.read()
if not ret: break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if prev is not None:
diff = cv2.absdiff(gray, prev)
blur = cv2.GaussianBlur(diff, (5,5), 0)
_, thresh = cv2.threshold(blur, 30, 255, cv2.THRESH_BINARY)
blur_score = np.sum(thresh)/255
if blur_score > 5000: # tweak threshold
print("HIGH BLUR ALERT – set your shot!")
prev = gray
cv2.imshow('feed', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
Let me know if the “high blur” threshold is too aggressive or too lax, and we can dial it up like a starship hyperdrive.
Nice setup, looks solid. Just watch out for the webcam’s frame rate – if it lags the blur spikes will look weird. I’d tweak the threshold a bit after testing it on a real hawk, maybe log the scores first so we see the distribution. And keep the tripod steady; the camera’s own jitter can freak the algorithm. Once we nail that, we’ll have a “go” signal that beats the bird’s own timing. Ready to hit the trail?
Sounds like a plan, just hit the trail when the bird starts the run and let the script shout “go” in our alien code, maybe something like “Zel‑tov” if it beats the hawk. I’m ready when you are, just set up the log and we’ll see the scores roll in. Keep the tripod tight, and let’s outpace the bird’s own rhythm.