diffFrame = currentFrame - prevFrame;
prevFrame = currentFrame;
diffFrameAbs = np.absolute(diffFrame)
frameVect = currentFrame.reshape(1,newSize[0]*newSize[1]);
frameVectFloat = frameVect.astype(float);
frRecShort = np.delete(frRec, 0, 0);
frRec = np.vstack((frRecShort,frameVectFloat));
sumFrRec = np.sum(frRec,0);
posDic, maxMovement, cfrVectRec, frameVectFloatRec = getting_frame_record(frRec, 0, winST,fb);
im3CRaw = create_3C_image (cfrVectRec)
if np.count_nonzero(im3CRaw[:,:,0])>6400:
im3CRaw[:,:,0] = np.zeros((80,80))
if np.count_nonzero(im3CRaw[:,:,1])>800:
im3CRaw[:,:,1] = np.zeros((80,80))
rgbArray = np.zeros((80,80,3), 'uint8')
rgbArray[..., 0] = im3CRaw[:,:,0]
rgbArray[..., 1] = im3CRaw[:,:,1]
rgbArray[..., 2] = im3CRaw[:,:,2]
im3C = Image.fromarray(rgbArray)
X_rs = np.zeros((1,80,80,3))
prevFrame = currentFrame
diffFrameAbs = np.absolute(diffFrame)
frameVect = currentFrame.reshape(1, newSize[0] * newSize[1])
frameVectFloat = frameVect.astype(float)
frRecShort = np.delete(frRec, 0, 0)
frRec = np.vstack((frRecShort, frameVectFloat))
# sumFrRec = np.sum(frRec,0);
posDic, maxMovement, cfrVectRec, frameVectFloatRec = getting_frame_record(
frRec, 0, winST, fb, newSize, roi, CVNsize)
im3CRaw = create_3C_image(cfrVectRec, CVNsize)
if np.count_nonzero(im3CRaw[:, :, 0]) > (roi * roi):
im3CRaw[:, :, 0] = np.zeros((roi, roi))
if np.count_nonzero(im3CRaw[:, :, 1]) > (roi * 10):
im3CRaw[:, :, 1] = np.zeros((roi, roi))
rgbArray = np.zeros((roi, roi, 3), 'uint8')
rgbArray[..., 0] = im3CRaw[:, :, 0]
rgbArray[..., 1] = im3CRaw[:, :, 1]
rgbArray[..., 2] = im3CRaw[:, :, 2]
im3C = Image.fromarray(rgbArray)
# X_rs = np.zeros((1,roi,roi,3))
def video_clips_to_3C_image_fun(dirPathInput, dirPathOutput, fbList, clipStart,
clipEnd, clipsNumber, bufferSize, windowST,
modelName, OSplatform, roi, CVNsize):
if modelName != 'none':
model = keras.models.load_model(modelName)
clips = np.arange(clipFirst, clipsNumber, 1)
fileList = os.listdir(dirPathInput)
clIndex = 0
for cl in clips:
fileName = fileList[cl]
ext = fileName[-3:]
if (ext == 'avi' or ext == 'mov' or ext == 'mp4') == True:
if OSplatform == 'Linux':
fileDirPathInputName = dirPathInput + '/' + fileName
if OSplatform == 'Windows':
fileDirPathInputName = dirPathInput + '\\' + fileName
if OSplatform == 'Darwin':
fileDirPathInputName = dirPathInput + '/' + fileName
r = np.arange(0, clipEnd, bufferSize)
for bf in r:
startFrame = bf
endFrame = startFrame + bufferSize
frRec = read_frames(startFrame, endFrame, fileDirPathInputName,
newSize) #resizes frame to newSize
if clIndex == clips[0] and startFrame == clipStart:
frRecRemain = np.zeros(
(windowST, (int(newSize[0] * newSize[1]))))
#160000=400*400
frRec = np.concatenate((frRecRemain, frRec), axis=0)
if clIndex > clips[0] or startFrame > clipStart:
frRec = np.concatenate((frRecRemain, frRec), axis=0)
for fb in fbList:
recIm3C = np.zeros((bufferSize, CVNsize, CVNsize, 3))
for w in range(0, frRec.shape[0] - windowST):
startWin = w
endWin = startWin + windowST
posDic, maxMovement, cfrVectRec, frameVectFloatRec = getting_frame_record(
frRec, startWin, endWin, fb, newSize, roi, CVNsize)
im3C = create_3C_image(cfrVectRec, CVNsize)
if modelName != 'none':
predictBehavior = 1 # predict behavior from ST-image and store the label
else:
predictBehavior = 0
if predictBehavior == 1:
X_rs = np.zeros((1, CVNsize, CVNsize, 3))
X_rs[0, :, :, :] = im3C
X = X_rs / 256 # normalize
predictionsProb = model.predict(X)
predictionLabel = np.zeros(
(1, np.shape(predictionsProb)[0]))
predictionLabel[0, :] = np.argmax(predictionsProb,
axis=1)
#cv2.imshow('im3C',im3C)
recIm3C[w, :, :, :] = im3C
xPos = posDic["xPos"]
yPos = posDic["yPos"]
if w == 0:
global xPosRec #AER
xPosRec = xPos
global yPosRec #AER
yPosRec = yPos
global maxMovementRec #AER
maxMovementRec = maxMovement
if modelName != 'none':
global behPredictionRec #AER
behPredictionRec = predictionLabel
else:
behPredictionRec = 0
if w > 0:
xPosRec = np.vstack((xPosRec, xPos))
yPosRec = np.vstack((yPosRec, yPos))
maxMovementRec = np.vstack(
(maxMovementRec, maxMovement))
if modelName != 'none':
behPredictionRec = np.vstack(
(behPredictionRec, predictionLabel))
else:
behPredictionRec = 0
xPosRec = 0 #AER
yPosRec = 0 #AER
dictPosRec = {
"xPosRec": xPosRec,
"yPosRec": yPosRec
}
maxMovementRec = 0 #AER
behPredictionRec = 0 #AER
dictST = {
"recIm3C": recIm3C,
"dictPosRec": dictPosRec,
"maxMovementRec": maxMovementRec,
"behPredictionRec": behPredictionRec
}
nameSMRec = 'dict3C_' + fileName[0:-4] + '_Arena1_' + str(
'%06.0f' % bf)
if OSplatform == 'Linux':
#newPath = dirPathOutput + '/' + fileName[0:-4] + '_fb' + str(fb) #!!!!!!works with non-numbered clips (one clip per video recording)
newPath = dirPathOutput + '/' + fileName[
0:
-4] + '_Arena1' #!!!!!!works with numbered clips (multiple clips of the same video; clip names end with index number)
if OSplatform == 'Windows':
#newPath = dirPathOutput + '\\' + fileName[0:-4] + '_fb' + str(fb) #!!!!!!works with non-numbered clips (one clip per video recording)
newPath = dirPathOutput + '\\' + fileName[
0:
-4] + '_Arena1' #!!!!!!works with numbered clips (multiple clips of the same video; clip names end with index number)
if OSplatform == 'Darwin':
#newPath = dirPathOutput + '/' + fileName[0:-4] + '_fb' + str(fb) #!!!!!!works with non-numbered clips (one clip per video recording)
newPath = dirPathOutput + '/' + fileName[
0:
-4] + '_Arena1' #!!!!!!works with numbered clips (multiple clips of the same video; clip names end with index number)
if not os.path.exists(newPath):
os.mkdir(newPath)
if OSplatform == 'Linux':
fileDirPathOutputName = newPath + '/' + nameSMRec
if OSplatform == 'Windows':
fileDirPathOutputName = newPath + '\\' + nameSMRec
if OSplatform == 'Darwin':
fileDirPathOutputName = newPath + '/' + nameSMRec
with open(fileDirPathOutputName, "wb") as f:
pickle.dump(dictST, f)
frRecSh = frRec.shape
frRecRemain = frRec[bufferSize:frRecSh[0], :]
clIndex = clIndex + 1
print(clIndex)