79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
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import cv2
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import numpy as np
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def check_within(imgSize, baseMultiplier, currentX, currentY, currentW, currentH):
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baseYbottom = imgSize[0] * baseMultiplier
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baseXRight = imgSize[1] * baseMultiplier
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upperMultiplier = 1 - baseMultiplier
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baseYTop = imgSize[0] * upperMultiplier
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baseXLeft = imgSize[1] * upperMultiplier
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currentBottomY = currentY + currentH
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currentRightX = currentX + currentW
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print(str(baseYbottom) + " " + str(currentY))
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if baseYbottom > currentY:
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return (255, 255, 255)
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elif baseYTop < currentBottomY:
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return (0, 0, 0)
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elif baseXRight > currentX:
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return (255, 0, 0)
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elif baseXLeft < currentRightX:
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return (0, 0, 255)
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else:
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return(0, 255, 0)
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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"C:\Users\Owner\Desktop\Face Detection\haar_frontface.xml") # CHECK THIS FIRST TROUBLE SHOOTING
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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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print("Found {0} faces!".format(len(faces)))
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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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color = check_within([height, width], .1, x, y, w, h)
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cv2.rectangle(frame, (int(width*.1), int(height*.1)), (int(width*.9), int(height*.9)), (150, 0 , 150))
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cv2.rectangle(frame, (x, y), (x+w, y+h), color)
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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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cap.destroyAllWindows()
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# you can save the image with
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# cv2.imwrite('watchgray.png',img)
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'''
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instead of the above code you can replace everything below
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img = cv2....
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with
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plt.imshow(img, cmap = 'gray', interpolation = 'bicubic')
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plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis
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plt.plot([200,300,400],[100,200,300],'c', linewidth=5)
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plt.show()
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for a matplotlib chart
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'''
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