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from __future__ import annotations
from typing import Tuple
import cv2
import numpy as np
from easyocrlite.types import BoxTuple
def resize_aspect_ratio(
img: np.ndarray, max_size: int, interpolation: int, expand_ratio: float = 1.0
) -> Tuple[np.ndarray, float]:
height, width, channel = img.shape
# magnify image size
target_size = expand_ratio * max(height, width)
# set original image size
if max_size and max_size > 0 and target_size > max_size:
target_size = max_size
ratio = target_size / max(height, width)
target_h, target_w = int(height * ratio), int(width * ratio)
if target_h != height or target_w != width:
proc = cv2.resize(img, (target_w, target_h), interpolation=interpolation)
# make canvas and paste image
target_h32, target_w32 = target_h, target_w
if target_h % 32 != 0:
target_h32 = target_h + (32 - target_h % 32)
if target_w % 32 != 0:
target_w32 = target_w + (32 - target_w % 32)
resized = np.zeros((target_h32, target_w32, channel), dtype=np.float32)
resized[0:target_h, 0:target_w, :] = proc
target_h, target_w = target_h32, target_w32
else:
resized = img
return resized, ratio
def adjust_result_coordinates(
box: BoxTuple, inverse_ratio: int = 1, ratio_net: int = 2
) -> np.ndarray:
if len(box) > 0:
box = np.array(box)
for k in range(len(box)):
if box[k] is not None:
box[k] *= (inverse_ratio * ratio_net, inverse_ratio * ratio_net)
return box
def normalize_mean_variance(
in_img: np.ndarray,
mean: Tuple[float, float, float] = (0.485, 0.456, 0.406),
variance: Tuple[float, float, float] = (0.229, 0.224, 0.225),
) -> np.ndarray:
# should be RGB order
img = in_img.copy().astype(np.float32)
img -= np.array(
[mean[0] * 255.0, mean[1] * 255.0, mean[2] * 255.0], dtype=np.float32
)
img /= np.array(
[variance[0] * 255.0, variance[1] * 255.0, variance[2] * 255.0],
dtype=np.float32,
)
return img
def boxed_transform(image: np.ndarray, box: BoxTuple) -> np.ndarray:
(tl, tr, br, bl) = box
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
# compute the height of the new image, which will be the
# maximum distance between the top-right and bottom-right
# y-coordinates or the top-left and bottom-left y-coordinates
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
dst = np.array(
[[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]],
dtype="float32",
)
# compute the perspective transform matrix and then apply it
M = cv2.getPerspectiveTransform(box, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
return warped |