#! /usr/bin/env python
'''Image/Video utilities'''

import Image, ImageDraw # PIL
try:
    import cv2
    opencvExists = True
except ImportError:
    print('OpenCV library could not be loaded')
    opencvExists = False
from sys import stdout

import utils

#import aggdraw # agg on top of PIL (antialiased drawing)
#import utils

__metaclass__ = type

cvRed = (0,0,255)
cvGreen = (0,255,0)
cvBlue = (255,0,0)
cvColors = utils.PlottingPropertyValues([cvRed,
                                         cvGreen,
                                         cvBlue])

cvKeyNumbers = {'a':1048673,
                'n': 1048686,
                'y': 1048697}

def drawLines(filename, origins, destinations, w = 1, resultFilename='image.png'):
    '''Draws lines over the image '''
    
    img = Image.open(filename)

    draw = ImageDraw.Draw(img)
    #draw = aggdraw.Draw(img)
    #pen = aggdraw.Pen("red", width)
    for p1, p2 in zip(origins, destinations):
        draw.line([p1.x, p1.y, p2.x, p2.y], width = w, fill = (256,0,0))
        #draw.line([p1.x, p1.y, p2.x, p2.y], pen)
    del draw 

    #out = utils.openCheck(resultFilename)
    img.save(resultFilename)

def matlab2PointCorrespondences(filename):
    '''Loads and converts the point correspondences saved 
    by the matlab camera calibration tool'''
    from numpy.lib.io import loadtxt, savetxt
    from numpy.lib.function_base import append
    points = loadtxt(filename, delimiter=',')
    savetxt(utils.removeExtension(filename)+'-point-correspondences.txt',append(points[:,:2].T, points[:,3:].T, axis=0))

def loadPointCorrespondences(filename):
    '''Loads and returns the corresponding points in world (first 2 lines) and image spaces (last 2 lines)'''
    from numpy.lib.io import loadtxt
    from numpy import float32
    points = loadtxt(filename, dtype=float32)
    return  (points[:2,:].T, points[2:,:].T) # (world points, image points)

def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=0.0):
    '''Returns the homography matrix mapping from srcPoints to dstPoints (dimension Nx2)'''
    #cvSrcPoints = arrayToCvMat(srcPoints);
    #cvDstPoints = arrayToCvMat(dstPoints);
    #H = cv.CreateMat(3, 3, cv.CV_64FC1)
    H, mask = cv2.findHomography(srcPoints, dstPoints, method, ransacReprojThreshold)
    return H

def cvMatToArray(cvmat):
    '''Converts an OpenCV CvMat to numpy array.'''
    from numpy.core.multiarray import zeros
    a = zeros((cvmat.rows, cvmat.cols))#array([[0.0]*cvmat.width]*cvmat.height)
    for i in xrange(cvmat.rows):
        for j in xrange(cvmat.cols):
            a[i,j] = cvmat[i,j]
    return a

if opencvExists:
    def arrayToCvMat(a, t = cv2.cv.CV_64FC1):
        '''Converts a numpy array to an OpenCV CvMat, with default type CV_64FC1.'''
        cvmat = cv2.cv.CreateMat(a.shape[0], a.shape[1], t)
        for i in range(cvmat.rows):
            for j in range(cvmat.cols):
                cvmat[i,j] = a[i,j]
        return cvmat

    def playVideo(filename):
        '''Plays the video'''
        capture = cv2.VideoCapture(filename)
        if capture.isOpened():
            key = -1
            while key!= 1048689: # 'q'
                ret, img = capture.read()
                if ret:
                    cv2.imshow('frame', img)
                    key = cv2.waitKey(5)

    def getImagesFromVideo(filename, nImages = 1):
        '''Returns nImages images from the video sequence'''
        images = []
        capture = cv2.VideoCapture(filename)
        if capture.isOpened():        
            ret = False
            while len(images)<nImages:
                while not ret:
                    ret, img = capture.read()
                    if img.size>0:
                        images.append(img)
        return images

def printCvMat(cvmat, out = stdout):
    '''Prints the cvmat to out'''
    for i in xrange(cvmat.rows):
        for j in xrange(cvmat.cols):
            out.write('{0} '.format(cvmat[i,j]))
        out.write('\n')

def projectArray(homography, points):
    '''Returns the coordinates of the projected points (format 2xN points)
    through homography'''
    from numpy.core._dotblas import dot
    from numpy.core.multiarray import array
    from numpy.lib.function_base import append

    if points.shape[0] != 2:
        raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1]))

    if (homography!=None) and homography.size>0:
        augmentedPoints = append(points,[[1]*points.shape[1]], 0)
        prod = dot(homography, augmentedPoints)
        return prod[0:2]/prod[2]
    else:
        return p

def project(homography, p):
    '''Returns the coordinates of the projection of the point p
    through homography'''
    from numpy.core.multiarray import array
    return projectArray(homography, array([[p[0]],p[1]]))

def projectTrajectory(homography, trajectory):
    '''Projects a series of points in the format
    [[x1, x2, ...],
    [y1, y2, ...]]'''
    from numpy.core.multiarray import array
    return projectArray(homography, array(trajectory))

def invertHomography(homography):
    'Returns an inverted homography'
    from numpy.linalg.linalg import inv
    invH = inv(homography)
    invH /= invH[2,2]
    return invH

if opencvExists:
    def computeTranslation(img1, img2, img1Points, maxTranslation2, minNMatches, windowSize = (5,5), level = 5, criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)):
        '''Computes the translation of img2 with respect to img1
        (loaded using OpenCV as numpy arrays)
        img1Points are used to compute the translation

        TODO add diagnostic if data is all over the place, and it most likely is not a translation (eg zoom, other non linear distortion)'''
        from numpy.core.multiarray import array
        from numpy.lib.function_base import median
        from numpy.core.fromnumeric import sum

        nextPoints = array([])
        (img2Points, status, track_error) = cv2.calcOpticalFlowPyrLK(img1, img2, img1Points, nextPoints, winSize=windowSize, maxLevel=level, criteria=criteria)
        # calcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, derivLambda[, flags]]]]]]]]) -> nextPts, status, err
        delta = []
        for (k, (p1,p2)) in enumerate(zip(img1Points, img2Points)):
            if status[k] == 1:
                dp = p2-p1
                d = sum(dp**2)
                if d < maxTranslation2:
                    delta.append(dp)
        if len(delta) >= minNMatches:
            return median(delta, axis=0)
        else:
            print(dp)
            return None