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05-24-2024, 01:19 AM | #1 |
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real time object detection?
Using old haar cascades with opencv, but getting lots of false face detections. Got any other recommendations?
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05-24-2024, 12:19 PM | #2 |
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Do you use a threshold?
If yes be more strict |
05-24-2024, 01:24 PM | #3 |
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with a proper settings of tresholds you can get some better results.
but it is better to choose other methods if equipment afford that
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05-24-2024, 03:07 PM | #4 |
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If you aren't doing small objects, YOLO is a good object detection algorithm
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05-24-2024, 08:21 PM | #5 |
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Try using Dlib’s face detector, MTCNN, or deep learning-based models such as OpenCV’s DNN module with pre-trained models (e.g., ResNet-based face detectors). Check scaleFactor and minNeighbors settings.
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05-24-2024, 11:39 PM | #6 | |
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Thanks for all responses so far.
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05-24-2024, 11:46 PM | #7 | |
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Thanks for all responses so far. I used the example facedetect.py . Add a target box, when a face enters the box moves a servo. Polulu Maestro servo controller. Code:
import cv2 import os import time cmdpull = "/home/walter/maestro-linux/UscCmd --servo 5,7000" cmdreturn = "/home/walter/maestro-linux/UscCmd --servo 5,6000" # Load the cascade face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') # To capture video from webcam. cap = cv2.VideoCapture(2) # To use a video file as input # cap = cv2.VideoCapture('filename.mp4') while True: # Read the frame _, img = cap.read() # Convert to grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Detect the faces faces = face_cascade.detectMultiScale(gray, 1.1, 4) #create region of interest box and dot cv2.rectangle(img, (100, 100), (180, 180), (0, 0, 255), 2) cv2.line(img, (140, 140), (140, 140), (0, 255, 0), 5) # Draw the rectangle around each face for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2) #if face is in middle of the screen - region of interest if x > 100 and x < 180 and y > 100 and y < 180: print ("x: ",x) print ("y: ",y) time.sleep(.1) os.system(cmdpull) time.sleep(.1) os.system(cmdreturn) # Display cv2.imshow('img', img) # Stop if escape key is pressed k = cv2.waitKey(30) & 0xff if k==27: break # Release the VideoCapture object cap.release()
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05-31-2024, 03:15 AM | #8 | |
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Scratch that for now. Will need a gpu. Having fun watching tutorials though. Now I know what Elon and that other guy were talking about when they mention Distributed Neural Network.
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