import numpy as np
import cv2

def translate(image, x, y):
    M = np.float32([[1, 0, x], [0, 1, y]])
    shifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))

    return shifted

def rotate(image, deg, center=None, scale=1.0):
    (h, w) = image.shape[:2]
    if center is None:
        center = (w/2, h/2)

    M = cv2.getRotationMatrix2D(center, deg,scale)
    rotated = cv2.warpAffine(image, M, (w,h))

    return rotated

def resize(image, width=None, height=None):

    (h, w) = image.shape[:2]

    if width is None:
        r = height / h
        dim = (int(w*r), int(height))

    if height is None:
        r = width / w
        dim = (int(width), int(h*r))
        # print(h, w, r, dim)
    resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)

    return resized

def show_image(image):
    cv2.imshow("image", image)
    cv2.waitKey(0)

def order_points(pts):
    rect = np.zeros((4,2), dtype="float32")

    sum = pts.sum(axis=1)
    rect[0] = pts[np.argmin(sum)]
    rect[2] = pts[np.argmax(sum)]

    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]

    return rect

def four_point_transform(image, pts):
    rect = order_points(pts)
    (tl, tr, br, bl) = rect

    widthA = np.sqrt(((tr[0] - tl[0])**2) + ((tr[1] - tl[1])**2))
    widthB = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    max_width = 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))
    max_height = max(int(heightA), int(heightB))

    dst = np.array([[0,0],
                    [max_width - 1, 0],
                    [max_width -1 , max_height - 1],
                    [0, max_height - 1]], dtype="float32")

    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (max_width, max_height))

    return warped


def order_points1(pts):
    # initialzie a list of coordinates that will be ordered
    # such that the first entry in the list is the top-left,
    # the second entry is the top-right, the third is the
    # bottom-right, and the fourth is the bottom-left
    rect = np.zeros((4, 2), dtype="float32")

    # the top-left point will have the smallest sum, whereas
    # the bottom-right point will have the largest sum
    s = pts.sum(axis=1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]

    # now, compute the difference between the points, the
    # top-right point will have the smallest difference,
    # whereas the bottom-left will have the largest difference
    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]

    # return the ordered coordinates
    return rect


def four_point_transform1(image, pts):
    # obtain a consistent order of the points and unpack them
    # individually
    rect = order_points1(pts)
    (tl, tr, br, bl) = rect

    # compute the width of the new image, which will be the
    # maximum distance between bottom-right and bottom-left
    # x-coordiates or the top-right and top-left x-coordinates
    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))

    # now that we have the dimensions of the new image, construct
    # the set of destination points to obtain a "birds eye view",
    # (i.e. top-down view) of the image, again specifying points
    # in the top-left, top-right, bottom-right, and bottom-left
    # order
    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(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))

    # return the warped image
    return warped
