import base64
import cv2
import numpy as np

def cv2_to_base64(image):
    data = cv2.imencode('.jpg', image)[1]
    return base64.b64encode(data.tobytes()).decode('utf8')

def base64_to_cv2(b64str):
    data = base64.b64decode(b64str.encode('utf8'))
    data = np.fromstring(data, np.uint8)
    data = cv2.imdecode(data, cv2.IMREAD_COLOR)
    return data

def expand_crop(image, box, expand_ratio=0.0):
    imgh, imgw, c = image.shape
    xmin, ymin, xmax, ymax = box

    org_rect = [xmin, ymin, xmax, ymax]
    h_half = (ymax - ymin) * (1 + expand_ratio) / 2.
    w_half = (xmax - xmin) * (1 + expand_ratio) / 2.
    if h_half > w_half * 4 / 3:
        w_half = h_half * 0.75
    center = [(ymin + ymax) / 2., (xmin + xmax) / 2.]
    ymin = max(0, int(center[0] - h_half))
    ymax = min(imgh - 1, int(center[0] + h_half))
    xmin = max(0, int(center[1] - w_half))
    xmax = min(imgw - 1, int(center[1] + w_half))
    return image[ymin:ymax, xmin:xmax, :].copy(), [xmin, ymin, xmax, ymax], org_rect

def crop_image_with_box(image, boxes):

    crop_res = []
 
    for box in boxes:
        crop_image, new_box, ori_box = expand_crop(image, box)
        # import cv2
        # cv2.imshow('1',crop_image)
        # cv2.waitKey(0)
        if crop_image is not None:
            crop_res.append(crop_image)
     
    return crop_res


def four_point_transform(image, pts):

    # # 获取输入坐标点
    # (tl, tr, br, bl) = pts

    # # 计算输入的w和h的值
    # 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))

    # 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))

    maxWidth = 960
    maxHeight = 960
    # 变化后对应坐标位置
    dst = np.array([[0, 0],
                    [maxWidth - 1, 0],
                    [maxWidth - 1, maxHeight - 1],
                    [0, maxHeight - 1]],
                    dtype='float32')   

    # 计算变换矩阵
    warp_mat = cv2.getPerspectiveTransform(np.float32(pts), dst)
    warped = cv2.warpPerspective(image, warp_mat, (maxWidth, maxHeight))

    return warped

def rect_intersect_area(rect1, rect2):  
    """  
    计算两个矩形的相交面积。  
    rect1, rect2: 形状为(4,)的数组，包含[x1, y1, x2, y2]  
    """  
    x1, y1, x2, y2 = rect1  
    X1, Y1, X2, Y2 = rect2  
      
    # 计算相交矩形的坐标  
    ix1 = max(x1, X1)  
    iy1 = max(y1, Y1)  
    ix2 = min(x2, X2)  
    iy2 = min(y2, Y2)  
      
    # 检查是否有相交  
    if ix1 < ix2 and iy1 < iy2:  
        # 计算相交面积  
        area = (ix2 - ix1) * (iy2 - iy1)  
        return area  
    else:  
        return 0  
    
def rect_intersect(rect1, rect2):  
    """  
    计算两个矩形的相交面积。  
    rect1, rect2: 形状为(4,)的数组，包含[x1, y1, x2, y2]  
    """  
    x1, y1, x2, y2 = rect1  
    X1, Y1, X2, Y2 = rect2  
      
    # 计算相交矩形的坐标  
    ix1 = max(x1, X1)  
    iy1 = max(y1, Y1)  
    ix2 = min(x2, X2)  
    iy2 = min(y2, Y2)  
      
    # 检查是否有相交  
    # if ix1 < ix2 and iy1 < iy2:  
        # 计算相交面积  
    if (ix2 - ix1) > 10 and (iy2 - iy1) > 10:
        return True  
    else:  
        return False