import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
import matplotlib.image as mpimg
from moviepy.editor import VideoFileClip

nx = 9
ny = 6

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(nx,ny,0)
objp = np.zeros((nx*ny,3), np.float32)
objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2)

# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.

# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')

# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
    img = cv2.imread(fname)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Find the chessboard corners
    ret, corners = cv2.findChessboardCorners(gray, (nx,ny), None)

    # If found, add object points, image points
    if ret == True:
        objpoints.append(objp)
        imgpoints.append(corners)

        #draw the chessboard corners and plot to show
        cv2.drawChessboardCorners(img, (nx,ny), corners, ret)
        plt.imshow(img)
        plt.show()
        
    
def save_calibration(objpoints, imgpoints):
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img.shape[0:2], None, None)
    calib = [mtx, dist]
    pickle.dump(calib, open("calibration.pkl", "wb"))

# save the calibration to load in successive runs, as this calibration is slow.
save_calibration(objpoints, imgpoints)
print("Calibration Saved")
