Video-Summary/Application/LayerManager.py

import time
from multiprocessing.pool import ThreadPool

import cv2
import numpy as np

from Application.Classifiers.Classifier import Classifier
from Application.Config import Config
from Application.Exporter import Exporter
from Application.Layer import Layer
from Application.VideoReader import VideoReader


class LayerManager:
    def __init__(self, config, layers):
        self.data = {}
        self.layers = layers
        self.tolerance = config["tolerance"]
        self.ttolerance = config["ttolerance"]
        self.minLayerLength = config["minLayerLength"]
        self.maxLayerLength = config["maxLayerLength"]
        self.resizeWidth = config["resizeWidth"]
        self.footagePath = config["inputPath"]
        self.config = config
        # self.classifier = Classifier()
        self.tags = []
        print("LayerManager constructed")

    def cleanLayers(self):
        print("'Cleaning' Layers")
        print("Before deleting short layers ", len(self.layers))
        self.freeMin()
        print("Before deleting long layers ", len(self.layers))
        self.freeMax()
        self.sortLayers()
        print("Before deleting sparse layers ", len(self.layers))
        self.deleteSparse()
        print("after deleting sparse layers ", len(self.layers))
        #self.calcTimeOffset()

    def deleteSparse(self):
        toDelete = []
        for i, l in enumerate(self.layers):
            empty = l.bounds.count([])
            if empty / len(l) > 0.5:
                toDelete.append(i)

        for i, id in enumerate(toDelete):
            del self.layers[id - i]

    def freeMin(self):
        self.data.clear()
        layers = []
        for l in self.layers:
            if len(l) > self.minLayerLength:
                layers.append(l)
        self.layers = layers

    def freeMax(self):
        layers = []
        for l in self.layers:
            if len(l) < self.maxLayerLength:
                layers.append(l)
        self.layers = layers

    def tagLayers(self):
        """Use classifieres the tag all Layers, by reading the contour content from the original video, then applying the classifier"""
        print("Tagging Layers")
        exporter = Exporter(self.config)
        start = time.time()
        for i, layer in enumerate(self.layers):
            print(f"{round(i/len(self.layers)*100,2)} {round((time.time() - start), 2)}")
            start = time.time()
            if len(layer.bounds[0]) == 0:
                continue
            listOfFrames = exporter.makeListOfFrames([layer])

            videoReader = VideoReader(self.config, listOfFrames)
            videoReader.fillBuffer()

            while not videoReader.videoEnded():
                frameCount, frame = videoReader.pop()
                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                data = []
                for (x, y, w, h) in layer.bounds[frameCount - layer.startFrame]:
                    if x is None:
                        break
                    factor = videoReader.w / self.resizeWidth
                    x = int(x * factor)
                    y = int(y * factor)
                    w = int(w * factor)
                    h = int(h * factor)
                    data.append(np.copy(frame[y : y + h, x : x + w]))
                layer.data.append(data)
            tags = self.classifier.tagLayer(layer.data)
            print(tags)
            self.tags.append(tags)

            videoReader.thread.join()

    def sortLayers(self):
        self.layers.sort(key=lambda c: c.startFrame)

    def calcTimeOffset(self):
        lenL = len(self.layers)
        for i in range(1, len(self.layers)):
            layer = self.layers[i]
            print(f"\r {i}/{lenL}", end="\r")
            overlap = True
            tries = 1
            while overlap:
                overlap = False
                for l in self.layers[:i:-1]:
                    if layer.timeOverlaps(l) and layer.spaceOverlaps(l):
                        overlap = True
                        break
                if overlap:
                    self.layers[i].exportOffset += 20 * tries
                    tries += 1

                # if self.layers[i].exportOffset >= 300000:
                #    break