def cv2_imread_buffer(buffer):
    buffer = io.BytesIO(buffer)
    arr = np.frombuffer(buffer.getvalue(), np.uint8)
    img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
    return img

def preprocess_red_image(img):
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    lower_red1 = np.array([0, 120, 70])
    upper_red1 = np.array([10, 255, 255])
    lower_red2 = np.array([170, 120, 70])
    upper_red2 = np.array([180, 255, 255])
    mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
    mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
    mask = cv2.bitwise_or(mask1, mask2)
    result = np.zeros_like(img)
    result[mask > 0] = img[mask > 0]
    return result

def split_image_tag(img, tag_pos):
    x, y = tag_pos
    img_ = img[0:35, y - 37:y]
    return img_
# 多线程识别
def process_tag(tag_pos):

    new_template = split_image_tag(img_2, tag_pos)
    new_size = 75
    new_template = cv2.resize(new_template, (new_size, new_size))

    ocr_infos = []
    angel_size = 6

    for angle in range(-180, 180, angel_size):
        template_ = rotate_image(new_template, angle)
        max_val, max_loc = template_match(template_, img_1)
        ocr_infos.append([angle, max_val, max_loc])

    max_info = max(ocr_infos, key=lambda x: x[1])

    return max_info

with ThreadPoolExecutor() as executor:
    results = list(executor.map(process_tag, [(37, 37), (37, 74), (37, 111), (37, 148)]))

for max_info in results:
    match_tag_list.append(list(max_info[-1]))

return match_tag_list

# 旋转图片
def rotate_image(template, angle):
    center = (template.shape[1] // 2, template.shape[0] // 2)
    rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
    rotated_image = cv2.warpAffine(template, rotation_matrix, (template.shape[1], template.shape[0]),
                                   flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
    return rotated_image


# 模板匹配
def template_match(template, img):
    template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    res = cv2.matchTemplate(img_gray, template_gray, cv2.TM_CCOEFF_NORMED)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    return max_val, max_loc