Inconsistent loading of images

[matskv]

The reformat_input function in file easyocr/utils.py is inconsistent in the way it computes greyscale image-array img_cv_grey, and the format (RGB or BGR) of the 3-channel img image-array, depending on the type of the input image to reformat_input

1. If image is a Path-like object or URL, the img will be an RGB image-array and img_cv_grey will be computed using cv2.imread(image, cv2.IMREAD_GRAYSCALE). So, these two are consistent. And furthermore, looking at the way the detector CRAFT is used, I assume that the image-array is supposed to be an RGB-ordered array.

2. If image is of type bytes. Then the code reads
   nparr = np.frombuffer(image, np.uint8)
   img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
   img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
   img_cv_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
   Here imgwill be an image-array ordered as RGB exactly as above. But the last line is clearly wrong, and should read
   img_cv_grey = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
   since img has been transforms to an RGB ordered image-array in the line above.

3. If image is an numpy array, then img is ordered as BGR, which is different to the cases above.

4. For PIL-JPEG image, img is again an BGR ordered image-array

If it is as I assume, that detector expects an RGB ordered image-array, I think that the cases of image being a numpy array of PIL-JPEG are both wrong. In any case, it is non-consistent to the cases of image being Path/URL-like or a byte-array.

Furthermore in case 1, the img_cv_grey is computed using the cv2.imread functionality, which produces a slightly different output than cv2.cvtColor(img, cv2.COLOR_RGB2GRAY). Also, since the image is read once more in the function loadImage in file easyocr/imgproc.py using the skimage.io.imread, I think it is unnecessary to read the image path twice. Better to load the image and then convert to img_cv_grey using cvtColor. I assume that that is cheaper computationally.