def prepare(filepath, mean_image, h, w):
width = w
height = h
img_array = cv2.imread(filepath) # read in the image
img_array = imgproc.toUINT8(img_array)
img_array = imgproc.process_image(img_array, (height, width))
img_array = np.float32(img_array)
new_array = cv2.resize(img_array, (width, height)) # resize image to match model's expected sizing
res = new_array.reshape(-1, height, width, 3)
res = res - mean_image
return res # return the image with shaping that TF wants.
def prepare(filepath, mean_image, h, w):
width = w
height = h
img_array = cv2.imread(
filepath,
cv2.IMREAD_UNCHANGED,
) # read in the image, convert to grayscale
img_array = imgproc.toUINT8(img_array)
img_array = imgproc.process_image(img_array, (height, width))
#cv2.imshow("window", img_array)
#cv2.waitKey()
img_array = np.float32(img_array)
new_array = cv2.resize(
img_array,
(width, height)) # resize image to match model's expected sizing
res = new_array.reshape(-1, height, width, 3)
res = res - mean_image
return res # return the image with shaping that TF wants.