100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
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import cv2
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import numpy as np
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multiplication_factor = 0.05
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def get_adjustment_amount(imgSize, currentX, currentY, currentW, currentH):
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current_top_left = [currentX, currentY]
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current_bottom_right = [currentX + currentW, currentY + currentH]
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current_top_right = [currentX + currentW, currentY]
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# find the difference between the left gap and the right gap, divide it by two, and multiply it by the speed scale
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horizontal_adjustment = multiplication_factor * (currentX - (imgSize[0] - current_top_right[0])) / 2
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vertical_adjustment = multiplication_factor * (currentY - (imgSize[0] - current_bottom_right[1])) / 2
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return [horizontal_adjustment, vertical_adjustment]
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cap = cv2.VideoCapture(0, cv2.IMREAD_GRAYSCALE) # instead of grayscale you can also use -1, 0, or 1.
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faceCascade = cv2.CascadeClassifier(r"./lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
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faceCascade_default = cv2.CascadeClassifier(r"./haarcascade_frontalface_default.xml")
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faceCascade_alt = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt.xml")
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faceCascade_alt2 = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt2.xml")
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faceCascade_alttree = cv2.CascadeClassifier(r"./haarcascade_frontalface_alt_tree.xml")
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profileFaceCascade = cv2.CascadeClassifier(r"./haarcascade_profileface.xml")
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tmp, frm = cap.read()
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height, width, channels = frm.shape
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print(f"{height*.25}, {width}")
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del tmp, frm
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#Color is 1, grayscale is 0, and the unchanged is -1
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while(True):
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ret, frame = cap.read()
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# Detect faces in the image
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faces = faceCascade.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30, 30)
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)
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if len(faces) == 0:
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faces = faceCascade_default.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = profileFaceCascade.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = faceCascade_alt.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = faceCascade_alt2.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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if len(faces) == 0:
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faces = faceCascade_alttree.detectMultiScale(
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gray,
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scaleFactor=1.1,
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minNeighbors=5,
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minSize=(30,30)
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)
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# Draw a rectangle around the faces
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for (x, y, w, h) in faces:
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adjustment_required = get_adjustment_amount([width, height], x, y, w, h)
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cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255))
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print(f"Adjust right: {adjustment_required[0]}")
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print(f"Adjust up : {adjustment_required[1]}")
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cv2.imshow('frame', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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