bit-guber commited on
Commit
5f8444c
1 Parent(s): 868779a

Update app.py

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Files changed (1) hide show
  1. app.py +8 -3
app.py CHANGED
@@ -171,14 +171,19 @@ def image_inference(image):
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  # faces = [ image[top:bottom, left:right].copy() for (top, left, bottom, right) in rawfaces ]
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  # faces = RetinaFace.extract_faces( image, align = True)
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  # faces_mean = [ x.mean() for x in faces ]
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- faces = [ x['face'] for x in DeepFace.extract_faces( image )]
 
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  # faces = [ image[x:w+x, y:h+y].copy() for (x, y, w, h) in rawfaces ]
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  faces = [ Image.fromarray(x, mode = 'RGB') for x in faces ]
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  ages, genders, beards, blurs, ethncities, masks = AnalysisFeatures( faces )
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  annotatedImage = image.copy()
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- for (top, left, bottom, right) in rawfaces:
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- cv2.rectangle(annotatedImage, (top, left), (left, right), (255, 0, 0), 5)
 
 
 
 
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  return Image.fromarray(annotatedImage, mode = 'RGB'), {'facesLength':len(faces), 'faceMean':faces_mean }#frameWrapper( rawfaces, ages, genders, beards, blurs, ethncities, masks )
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  # return frameWrapper( rawfaces, ages, genders, beards, blurs, ethncities, masks )
 
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  # faces = [ image[top:bottom, left:right].copy() for (top, left, bottom, right) in rawfaces ]
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  # faces = RetinaFace.extract_faces( image, align = True)
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  # faces_mean = [ x.mean() for x in faces ]
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+ rawfaces = DeepFace.extract_faces( image )
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+ faces = [ x['face'] for x in rawfaces]
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  # faces = [ image[x:w+x, y:h+y].copy() for (x, y, w, h) in rawfaces ]
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  faces = [ Image.fromarray(x, mode = 'RGB') for x in faces ]
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  ages, genders, beards, blurs, ethncities, masks = AnalysisFeatures( faces )
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  annotatedImage = image.copy()
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+ for x in rawfaces:
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+ x = x['facial_area']
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+ top, left, bottom, right = x['x'], x['y'], x['w'], x['h']
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+ bottom += top
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+ right += left
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+ cv2.rectangle(annotatedImage, (top, bottom), (left, right), (255, 0, 0), 5)
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  return Image.fromarray(annotatedImage, mode = 'RGB'), {'facesLength':len(faces), 'faceMean':faces_mean }#frameWrapper( rawfaces, ages, genders, beards, blurs, ethncities, masks )
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  # return frameWrapper( rawfaces, ages, genders, beards, blurs, ethncities, masks )