def __init__(self,vidfile,labelfile,modelFile,labelListFile,exportPath, perClassFrames=40,frameRate=20,context_size=5, banerWidth = 80,scale = 1): # input properties self.vidreader = VideoReader(vidfile); self.labelreader = open(labelfile,'r'); self.N = self.vidreader.frames; self.width,self.height = self.vidreader.width,self.vidreader.height; self.context_size=context_size; self.perClassFrames = perClassFrames; self.labels = load_labels(labelListFile); self.n_outs = len(self.labels); self.flag_colors = []; for index in range(self.n_outs): self.flag_colors.extend([tuple(np.array(cm.jet(index/float(self.n_outs))[:3][::-1])*255)]) with open(modelFile,'r') as fp: model = fp.readline(); self.predictor = dnn_instance(model.strip()); self.input_shape = [self.height,self.width,3] self.batch_size = self.predictor.batch_size #write properites self.banerWidth = banerWidth self.vidWriter = VideoWriter(exportPath,self.banerWidth+self.width,self.height,fps=frameRate); self.colors = np.random.randint(256, size=(len(self.labels), 3)) self.scale = scale; #status self.frameIdx = 0; self.tasks = deque(); self.isFinished = False; self.vidWriter.build();
def load_predictors(modelFile): predictors = []; with open(modelFile,'r') as fp: for model in fp: predictors.extend([dnn_instance(model.strip())]); n_outs = predictors[0].model_config['n_outs']; predictorStartIdx = [0]*n_outs; for idx in range(n_outs-1): predictorStartIdx[idx+1] = predictorStartIdx[idx] + predictors[idx+1].model_config['n_outs']; return predictors,predictorStartIdx;
def __init__(self,
vidfile,
labelfile,
modelFile,
labelListFile,
exportPath,
perClassFrames=40,
frameRate=20,
context_size=5,
banerWidth=80,
scale=1):
# input properties
self.vidreader = VideoReader(vidfile)
self.labelreader = open(labelfile, 'r')
self.N = self.vidreader.frames
self.width, self.height = self.vidreader.width, self.vidreader.height
self.context_size = context_size
self.perClassFrames = perClassFrames
self.labels = load_labels(labelListFile)
self.n_outs = len(self.labels)
self.flag_colors = []
for index in range(self.n_outs):
self.flag_colors.extend([
tuple(
np.array(cm.jet(index / float(self.n_outs))[:3][::-1]) *
255)
])
with open(modelFile, 'r') as fp:
model = fp.readline()
self.predictor = dnn_instance(model.strip())
self.input_shape = [self.height, self.width, 3]
self.batch_size = self.predictor.batch_size
#write properites
self.banerWidth = banerWidth
self.vidWriter = VideoWriter(exportPath,
self.banerWidth + self.width,
self.height,
fps=frameRate)
self.colors = np.random.randint(256, size=(len(self.labels), 3))
self.scale = scale
#status
self.frameIdx = 0
self.tasks = deque()
self.isFinished = False
self.vidWriter.build()