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python: 'cv2.' -> 'cv.' via 'import cv2 as cv'
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@@ -94,13 +94,13 @@ First we need to load the required XML classifiers. Then load our input image (o
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grayscale mode.
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@code{.py}
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import numpy as np
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import cv2
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import cv2 as cv
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face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
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eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
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face_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')
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eye_cascade = cv.CascadeClassifier('haarcascade_eye.xml')
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img = cv2.imread('sachin.jpg')
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img = cv.imread('sachin.jpg')
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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@endcode
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Now we find the faces in the image. If faces are found, it returns the positions of detected faces
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as Rect(x,y,w,h). Once we get these locations, we can create a ROI for the face and apply eye
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@@ -108,16 +108,16 @@ detection on this ROI (since eyes are always on the face !!! ).
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@code{.py}
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faces = face_cascade.detectMultiScale(gray, 1.3, 5)
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for (x,y,w,h) in faces:
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cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
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cv.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
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roi_gray = gray[y:y+h, x:x+w]
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roi_color = img[y:y+h, x:x+w]
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eyes = eye_cascade.detectMultiScale(roi_gray)
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for (ex,ey,ew,eh) in eyes:
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cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
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cv.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
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cv2.imshow('img',img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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cv.imshow('img',img)
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cv.waitKey(0)
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cv.destroyAllWindows()
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@endcode
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Result looks like below:
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