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Img = cv2.resize(Img, (512,512), interpolation = cv2.INTER_AREA) This is how it should be, cause current code is limiting bigger images than 512 res from processing which is very weird, not all 512 res image are actual 512 res updated func

def read_img_tensor(img_path=None, return_landmark=True): #rgb -1~1 Img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # BGR or G if Img.ndim == 2: Img = cv2.cvtColor(Img, cv2.COLOR_GRAY2RGB) # GGG else: Img = cv2.cvtColor(Img, cv2.COLOR_BGR2RGB) # RGB

Img = cv2.resize(Img, (512,512), interpolation = cv2.INTER_AREA)

ImgForLands = Img.copy()
Img = Img.transpose((2, 0, 1))/255.0
Img = torch.from_numpy(Img).float()
normalize(Img, [0.5,0.5,0.5], [0.5,0.5,0.5], inplace=True)
ImgTensor = Img.unsqueeze(0)
SelectPred = None
if return_landmark:
    try:
        PredsAll = FaceDetection.get_landmarks(ImgForLands)
    except:
        print('Error in detecting this face {}. Continue...'.format(img_path))
    if PredsAll is None:
        print('Warning: No face is detected in {}. Continue...'.format(img_path))
        return ImgTensor, None
    ins = 0
    if len(PredsAll)!=1:
        hights = []
        for l in PredsAll:
            hights.append(l[8,1] - l[19,1])
        ins = hights.index(max(hights))
        print('Warning: Too many faces are detected, only handle the largest one...')
    SelectPred = PredsAll[ins]
return ImgTensor, SelectPred