def get_shape_from_contour(contour, contour_box):
# uppack bounding box of contour
x, y, w, h = contour_box
# list of contours (only contains the first contour, really) shifted so
# that they can be drawn in a box the size of the bounding box
contours_shifted = [np.array([[[j[0] - x, j[1] - y] for j in i] for i in contour])]
# create image with filled contour
shape_img = util.draw_contour(contours_shifted, 0, h, w)
# for each card in trainings set, find one with most similarity
diffs = []
training_set = get_training_set()
for i, shape_ref in training_set.items():
# resize image
shape_img_res = util.resize(shape_img, shape_ref.shape)
# find diff and its sum
d = cv2.absdiff(shape_img_res, shape_ref)
sum_diff = np.sum(d)
diffs.append(sum_diff)
return diffs.index(min(diffs)) + 1
def get_shape_contour(img):
rect = get_shape_bounding_rect(img)
if rect is None:
return None
y1, y2, x1, x2, contours = rect
shape_img = util.draw_contour(contours, 1)
cropped = shape_img[y1:y2, x1:x2]
return cropped
