#!/usr/bin/python
# -*- coding: utf-8 -*-
from pprint import pprint
from bson.objectid import ObjectId
from pymongo import MongoClient
from PIL import Image
import gridfs, os
import collections
import cv2
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import image as mpimg

#/ creating connections for communicating with Mongo DB
client = MongoClient('localhost', 27017)
db = client.knots
fs = gridfs.GridFS(db)
fm = db.marked_up_image
data_dict = dict.fromkeys(['image', 'type', 'coordinates'])
data = np.zeros(50,)


for gridout in fm.find({"users_polygons.polygons.type": "knot_defect"}).limit(20):

    images = []
    plist = gridout['users_polygons']
    data_dict['image'] = gridout['image']
    for usrpoly in plist:
        poly = usrpoly['polygons']
        for p in poly:
            points = p['points']
            data_dict['type'] = p['type']
            for point in points:
                data_dict['coordinates'] = [point['x'], point['y']]



    try:
        with open('tmp.png', 'wb') as fi:
            fi.write(fs.get( ObjectId(gridout['image']) ).read() )
            img = cv2.imread('tmp.png', 0)
            hist, bins = np.histogram(img.ravel(), 50, [0,50], density=True)
            width = np.diff(bins)
            center = (bins[:-1] + bins[1:])/2
            tmp = data


            data += hist
            tmp +=  hist
            pprint(np.array_equal(data, tmp)) #True
            pprint ('========================')

            data += hist
            tmp = tmp + hist
            pprint(np.array_equal(data, tmp))  # False
            pprint('========================')

            data = data + hist
            tmp += hist
            pprint(np.array_equal(data, tmp))  # True
            pprint('========================')

            data = data + hist
            tmp = tmp + hist
            pprint(np.array_equal(data, tmp))  # True
            pprint('========================')

            #pprint (data)

            #pprint (tmp)


            if img is not None:
                images.append(img)




    except StopIteration:
        print("zero cursor")
#pprint (data)
meanhist = np.zeros([50,])

#for items in np.nditer(data, op_flags=['readwrite']):

#meanhist.append([sum(i) for i in zip(*data)])

#meanhist = np.array(meanhist)/50


for gridout in fm.find({"users_polygons.polygons.type": "knot_defect"}).limit(20):

    images = []
    plist = gridout['users_polygons']
    data_dict['image'] = gridout['image']
    for usrpoly in plist:
        poly = usrpoly['polygons']
        for p in poly:
            points = p['points']
            data_dict['type'] = p['type']
            for point in points:
                data_dict['coordinates'] = [point['x'], point['y']]

    try:
        with open('tmp.png', 'wb') as fi:
            fi.write(fs.get(ObjectId(gridout['image'])).read())
            img = cv2.imread('tmp.png', 0)
            hist, bins = np.histogram(img.ravel(), 50, [0, 50], density=True)
            width = np.diff(bins)
            center = (bins[:-1] + bins[1:]) / 2



            #plt.hist(meanhist, bins, alpha=0.5, label='mean')
            #plt.hist(hist, bins, alpha=0.5, label='current')
            #plt.legend(loc='upper right')
            #plt.show()

            if img is not None:
                images.append(img)


    except StopIteration:
        print("zero cursor")

#pprint (hist)
#pprint (meanhist)