124 lines
4.2 KiB
Python
124 lines
4.2 KiB
Python
from Application.Layer import Layer
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from Application.Config import Config
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from Application.VideoReader import VideoReader
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from Application.Exporter import Exporter
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from multiprocessing.pool import ThreadPool
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from Application.Classifiers.Classifier import Classifier
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import cv2
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import numpy as np
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import time
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class LayerManager:
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def __init__(self, config, layers):
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self.data = {}
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self.layers = layers
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self.tolerance = config["tolerance"]
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self.ttolerance = config["ttolerance"]
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self.minLayerLength = config["minLayerLength"]
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self.maxLayerLength = config["maxLayerLength"]
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self.resizeWidth = config["resizeWidth"]
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self.footagePath = config["inputPath"]
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self.config = config
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#self.classifier = Classifier()
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self.tags = []
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print("LayerManager constructed")
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def transformLayers(self):
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print("'Cleaning' Layers")
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print("Before deleting short layers ", len(self.layers))
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self.freeMin()
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print("Before deleting long layers ", len(self.layers))
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self.freeMax()
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self.sortLayers()
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self.calcStats()
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print("Before deleting sparse layers ", len(self.layers))
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self.deleteSparse()
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print("after deleting sparse layers ", len(self.layers))
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self.calcTimeOffset()
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def deleteSparse(self):
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toDelete = []
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for i, l in enumerate(self.layers):
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empty = l.bounds.count([])
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if empty / len(l) > 0.5:
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toDelete.append(i)
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for i, id in enumerate(toDelete):
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del self.layers[id - i]
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def calcStats(self):
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for layer in self.layers:
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layer.calcStats()
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def freeMin(self):
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self.data.clear()
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layers = []
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for l in self.layers:
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if len(l) > self.minLayerLength:
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layers.append(l)
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self.layers = layers
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def freeMax(self):
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layers = []
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for l in self.layers:
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if len(l) < self.maxLayerLength:
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layers.append(l)
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self.layers = layers
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def tagLayers(self):
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'''Use classifieres the tag all Layers, by reading the contour content from the original video, then applying the classifier'''
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print("Tagging Layers")
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exporter = Exporter(self.config)
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start = time.time()
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for i, layer in enumerate(self.layers):
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print(f"{round(i/len(self.layers)*100,2)} {round((time.time() - start), 2)}")
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start = time.time()
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if len(layer.bounds[0]) == 0:
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continue
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listOfFrames = exporter.makeListOfFrames([layer])
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videoReader = VideoReader(self.config, listOfFrames)
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videoReader.fillBuffer()
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while not videoReader.videoEnded():
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frameCount, frame = videoReader.pop()
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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data = []
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for (x, y, w, h) in layer.bounds[frameCount - layer.startFrame]:
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if x is None:
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break
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factor = videoReader.w / self.resizeWidth
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x = int(x * factor)
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y = int(y * factor)
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w = int(w * factor)
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h = int(h * factor)
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data.append(np.copy(frame[y:y+h, x:x+w]))
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layer.data.append(data)
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tags = self.classifier.tagLayer(layer.data)
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print(tags)
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self.tags.append(tags)
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videoReader.thread.join()
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def sortLayers(self):
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self.layers.sort(key = lambda c:c.startFrame)
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def calcTimeOffset(self):
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for i, layer in enumerate(self.layers):
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overlap = True
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while overlap:
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overlap = False
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for l in self.layers[i:]:
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if layer.timeOverlaps(l):
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if layer.spaceOverlaps(l):
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overlap = True
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if overlap:
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self.addDelay(i, 10)
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if self.layers[i].exportOffset >= 180:
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break
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def addDelay(self, index, frames):
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for layer in self.layers[index:]:
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layer.exportOffset += frames
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