import numpy as np
import cv2
import imutils

from kneed import KneeLocator
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt

class Layer:
    #bounds = [[(x,y,w,h), ],]

    startFrame = None
    lastFrame = None
    length = None

    def __init__(self, startFrame, data, config):
        '''returns a Layer object
        
        Layers are collections of contours with a StartFrame, 
        which is the number of the frame the first contour of
        this layer was extraced from

        A Contour is a CV2 Contour, which is a y*x*3 rgb numpy array,
        but we only care about the corners of the contours. 
        So we save the bounds (x,y,w,h) in bounds[] and the actual content in data[] 
        '''
        self.startFrame = startFrame
        self.lastFrame = startFrame
        self.config = config
        self.data = []
        self.bounds = []
        self.bounds.append([data])
        #print("Layer constructed")

    def add(self, frameNumber, bound):
        '''Adds a bound'''
        if self.startFrame + len(self.bounds) - 1 > frameNumber:
            if len(self.bounds[frameNumber - self.startFrame]) >= 1:
                self.bounds[frameNumber - self.startFrame].append(bound)
        else:
            self.lastFrame = frameNumber
            self.bounds.append([bound])

    def getLength(self):
        return len(self)

    def __len__(self):
        self.length = len(self.bounds)
        return self.length

    def clusterDelete(self):
        '''Uses a cluster analysis to remove contours which are not the result of movement'''
        org = self.bounds
        if len(org) == 1:
            return
        mapped = []
        mapping = []
        clusterCount = 1
        noiseSensitivity = self.config["noiseSensitivity"] 
        noiseThreashold = self.config["noiseThreashold"]

        # calculates the middle of each contour in the 2d bounds[] and saves it in 1d list
        # and saves the 2d indexes in a mapping array
        for i, bounds in enumerate(org):
            for j, bound in enumerate(bounds):
                x = (bound[0] + bound[2]/2) / self.config["w"]
                y = (bound[1] + bound[3]/2) / self.config["w"]

                mapped.append(list((x,y)))
                mapping.append([i,j])

        mapped = np.array(mapped).astype(np.float16)
        labels = []
        centers = []
        kmeans = None

        # the loop isn't nessecary (?) if the number of clusters is known, since it isn't the loop tries to optimize
        while True:
            kmeans = KMeans(init="random", n_clusters=clusterCount, n_init=10, max_iter=300, random_state=42)
            kmeans.fit(mapped)
            labels = list(kmeans.labels_)

            if kmeans.n_features_in_ < clusterCount:
                break

            maxm = 0
            for x in set(labels):
                y = labels.count(x)
                if y > maxm:
                    maxm = y

            if maxm > len(mapped)*(noiseSensitivity) and clusterCount+1<=len(kmeans.cluster_centers_):
                clusterCount += 1
            else: 
                centers = kmeans.cluster_centers_
                break

        # transformes the labels array
        # new array:
        # the index is the cluster id, the array is the id of the contour 
        # [
        # [1,2,3]
        # [3,4,5]
        # [6,7,8,9]   
        # ]
        classed = [[]]
        for i, x in enumerate(list(labels)):
            while len(classed) <= x:
                classed.append([])
            classed[x].append(i)

        # calculates the euclidean distance (without the sqrt) of each point in a cluster to the cluster center
        dists = []
        for num, cen in enumerate(centers):
            dist = 0
            for i in classed[num]:
                dist += (mapped[i][0]-cen[0])**2 + (mapped[i][1]-cen[1])**2
            dist/=len(classed[num])
            dists.append(dist*1000)

        # copy all contours of the clusters with more movement than the threshold
        newContours = [[]]
        for i, dis in enumerate(dists):
            # copy contours which are spread out, delete rest by not copying them 
            
            for j in classed[i]:
                x, y = mapping[j]
                while x >= len(newContours):
                    newContours.append([])
                while y > len(newContours[x]):
                    newContours[x].append((None, None, None, None))
                if dis > noiseThreashold:
                    newContours[x].append(org[x][y])      
        
        self.bounds = newContours
        #print(f"{clusterCount} clusters identified {dists}")

        #fig, ax = plt.subplots()
        #x=mapped[:,0]
        #y=mapped[:,1]
        
        #ax.scatter(x, y, labels, s=10, cmap="rainbow")
        #ax.grid(True)
        #plt.show()

        #print("done")