def DetectViolence(PATH_FILE_NAME_OF_SOURCE_VIDEO,
PATH_FILE_NAME_TO_SAVE_RESULT):
violenceDetector = ViolenceDetector()
videoReader = cv2.VideoCapture(PATH_FILE_NAME_OF_SOURCE_VIDEO)
shouldSaveResult = (PATH_FILE_NAME_TO_SAVE_RESULT != None)
if shouldSaveResult:
videoSavor = VideoSavor(PATH_FILE_NAME_TO_SAVE_RESULT + "_Result",
videoReader)
listOfForwardTime = []
isCurrentFrameValid, currentImage = videoReader.read()
while isCurrentFrameValid:
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(currentImage)
startDetectTime = time.time()
isFighting = violenceDetector.Detect(netInput)
endDetectTime = time.time()
listOfForwardTime.append(endDetectTime - startDetectTime)
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2 * deploySettings.BORDER_SIZE
currentImage = cv2.resize(currentImage, (targetSize, targetSize))
if isFighting:
print("isFighting")
resultImage = cv2.copyMakeBorder(
currentImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.FIGHT_BORDER_COLOR)
else:
print("is't Fighting")
resultImage = cv2.copyMakeBorder(
currentImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.NO_FIGHT_BORDER_COLOR)
cv2.imshow("Violence Detection", resultImage)
if shouldSaveResult:
videoSavor.AppendFrame(resultImage)
userResponse = cv2.waitKey(1)
if userResponse == ord('q'):
videoReader.release()
cv2.destroyAllWindows()
break
else:
isCurrentFrameValid, currentImage = videoReader.read()
# PrintUnsmoothedResults(violenceDetector.unsmoothedResults)
averagedForwardTime = np.mean(listOfForwardTime)
print("Averaged Forward Time: ", averagedForwardTime)
def DetectViolence():
violenceDetector = ViolenceDetector()
videoReader = cv2.VideoCapture(
"rtsp://*****:*****@118.70.125.33:8554/CH001.sdp")
count = 0
while (True):
listOfForwardTime = []
isCurrentFrameValid, currentImage = videoReader.read()
# print(isCurrentFrameValid)
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(currentImage)
startDetectTime = time.time()
isFighting = violenceDetector.Detect(netInput)
print(isFighting)
endDetectTime = time.time()
listOfForwardTime.append(endDetectTime - startDetectTime)
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2 * deploySettings.BORDER_SIZE
currentImage = cv2.resize(currentImage, (targetSize, targetSize))
if isFighting:
count += 1
if count == 5:
print("saving...")
cv2.imwrite('image.jpg', currentImage) # luu image
#send mail
sendEmail.sendMail(
'*****@*****.**', '12345Aa@',
'*****@*****.**', 'Fighting', 'Have Fight',
'C:/Users/anlan/OneDrive/Desktop/Project_Violence/ViolenceDetection-master_main/image.jpg'
)
resultImage = cv2.copyMakeBorder(
currentImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.FIGHT_BORDER_COLOR)
# copyMakeBorder them borfer cho video
else:
if count > 5:
count = 0
resultImage = cv2.copyMakeBorder(
currentImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.NO_FIGHT_BORDER_COLOR)
cv2.imshow("Violence Detection", resultImage)
print("count", count)
userResponse = cv2.waitKey(1)
if userResponse == ord('q'):
videoReader.release()
cv2.destroyAllWindows()
break
else:
isCurrentFrameValid, currentImage = videoReader.read()
averagedForwardTime = np.mean(listOfForwardTime)
def Detector(frames, methods=['GET', 'POST']):
dataJson = json.loads(str(frames).replace('\'','\"'))
for item in dataJson['data']:
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(readb64(item["img"]))
isFighting = violenceDetector.Detect(netInput)
#siddet tespit edildi
if isFighting:
response={'isDone':'false','message':'Baslangic:'+str(item['time'])}
socketio.emit('Detector', response, callback=MessageReceived)
else:
response={'isDone':'false','message':'Bitis:'+str(item['time'])}
socketio.emit('Detector', response, callback=MessageReceived)
response={'isDone':'true','message':'tespit bitti'}
socketio.emit('Detector', response, callback=MessageReceived)
def DetectViolence(PATH_FILE_NAME_OF_SOURCE_VIDEO, PATH_FILE_NAME_TO_SAVE_RESULT):
violenceDetector = ViolenceDetector()
videoReader = cv2.VideoCapture(PATH_FILE_NAME_OF_SOURCE_VIDEO)
print(videoReader)
shouldSaveResult = (PATH_FILE_NAME_TO_SAVE_RESULT != None)
if shouldSaveResult:
videoSavor = VideoSavor(PATH_FILE_NAME_TO_SAVE_RESULT + "_Result", videoReader)
listOfForwardTime = []
isCurrentFrameValid, currentImage = videoReader.read()
count = 0
while isCurrentFrameValid:
print(isCurrentFrameValid)
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(currentImage)
startDetectTime = time.time()
isFighting = violenceDetector.Detect(netInput)
print(isFighting)
endDetectTime = time.time()
listOfForwardTime.append(endDetectTime - startDetectTime)
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2*deploySettings.BORDER_SIZE
currentImage = cv2.resize(currentImage, (targetSize, targetSize))
if isFighting:
count += 1
if count == 5:
print("saving...")
cv2.imwrite('image.jpg',currentImage)
resultImage = cv2.copyMakeBorder(currentImage,deploySettings.BORDER_SIZE,deploySettings.BORDER_SIZE,deploySettings.BORDER_SIZE,deploySettings.BORDER_SIZE,cv2.BORDER_CONSTANT,value=deploySettings.FIGHT_BORDER_COLOR)
else:
resultImage = cv2.copyMakeBorder(currentImage,deploySettings.BORDER_SIZE,deploySettings.BORDER_SIZE,deploySettings.BORDER_SIZE,deploySettings.BORDER_SIZE,cv2.BORDER_CONSTANT,value=deploySettings.NO_FIGHT_BORDER_COLOR)
cv2.imshow("Violence Detection", resultImage)
print("count",count)
if shouldSaveResult:
print("shouldSaveResult",shouldSaveResult)
videoSavor.AppendFrame(resultImage)
userResponse = cv2.waitKey(1)
if userResponse == ord('q'):
videoReader.release()
cv2.destroyAllWindows()
break
else:
isCurrentFrameValid, currentImage = videoReader.read()
# cv2.imshow('image',image)
# PrintUnsmoothedResults(violenceDetector.unsmoothedResults)
averagedForwardTime = np.mean(listOfForwardTime)
if count > 5:
print("truee")
sendEmail.sendMail('*****@*****.**','12345Aa@','*****@*****.**','Fighting','Have Fight','C:/Users/anlan/OneDrive/Desktop/Project_Violence/ViolenceDetection-master_main/image.jpg')
def SaveCurrentBatchData(self):
batchOfImages = self._batchData.batchOfImages
for eachBatch in range(batchOfImages.shape[0]):
for eachFrame in range(batchOfImages.shape[1]):
# Note: for the Group Dimension, we ONLY need one image, therefore, pick the last group-index image.
cvFormatImage = ImageUtils.ConvertImageFrom_NetInput_to_CV(
batchOfImages[eachBatch, eachFrame, -1])
pathToSaveImage = os.path.join(
trainSettings.PATH_TO_SAVE_MODEL,
"save_epoch_" + str(self._dataManager.epoch))
if not os.path.exists(pathToSaveImage):
os.makedirs(pathToSaveImage)
fileName = str(self._dataManager.step) + "_" + str(
eachBatch) + "_" + str(eachFrame) + ".jpg"
cv2.imwrite(os.path.join(pathToSaveImage, fileName),
cvFormatImage)
def Detector(frames, methods=['GET', 'POST']):
dataFrames = list()
dataJson = json.loads(str(frames).replace('\'', '\"'))
for item in dataJson['data']:
# print(item[item.index("base64,")+7:])
imgdata = readb64(item)
dataFrames.append(imgdata)
# Capture frame-by-frame
ret, currentImage = imgdata
# do what you want with frame
# and then save to file
count += 1
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(currentImage)
startDetectTime = time.time()
isFighting = violenceDetector.Detect(netInput)
endDetectTime = time.time()
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2 * deploySettings.BORDER_SIZE
currentImage = cv2.resize(currentImage, (targetSize, targetSize))
if isFighting: #şiddet tespit edildi
if len(baslangicsn) == len(bitissn):
baslangicsn.append(count / 25)
response = {
'isDone': 'false',
'message': 'Baslangic:' + str(baslangicsn.append(count / 25))
}
socketio.emit('Detector', response, callback=messageReceived)
else:
if len(baslangicsn) != len(bitissn):
bitissn.append(count / 25)
response = {
'isDone': 'false',
'message': 'Bitis:' + str(bitissn.append(count / 25))
}
socketio.emit('Detector', response, callback=messageReceived)
print("Eleman sayisi:" + str(len(dataFrames)))
bitissn.append(count / 25)
response = {'isDone': 'true', 'message': 'tespit bitti'}
socketio.emit('Detector', response, callback=messageReceived)
def SocketDetectorWebcam(frames, methods=['GET', 'POST']):
dataJson = json.loads(str(frames).replace('\'','\"'))
isStarted=0
for item in dataJson['data']:
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(readb64(item["img"]))
isFighting = violenceDetector.Detect(netInput)
#siddet tespit edildi
if isFighting:
#response={'isFight':'true'}
#socketio.emit('SocketDetectorState', response, callback=MessageReceived)
isStarted=1
response={'isStarted':''+isStarted,'isDone':'false','message':''+str(item['time'])}
socketio.emit('SocketDetectorComplete', response, callback=MessageReceived)
else:
#response={'isFight':'false'}
#socketio.emit('SocketDetectorState', response, callback=MessageReceived)
response={'isStarted':''+isStarted,'isDone':'true','message':''+str(item['time'])}
socketio.emit('SocketDetectorComplete', response, callback=MessageReceived)
isStarted=0
response={'isComplete':'true','message':'tespit bitti'}
socketio.emit('SocketDetectorComplete', response, callback=MessageReceived)
def upload():
target = os.path.join(APP_ROOT, 'videos/')
print(target)
if not os.path.isdir(target):
os.mkdir(target)
print(request.files.getlist("file"))
for upload in request.files.getlist("file"):
print(upload)
print("{} is the file name".format(upload.filename))
filename = upload.filename
# This is to verify files are supported
ext = os.path.splitext(filename)[1]
if (ext == ".mp4") or (ext == ".mov"):
print("File supported moving on...")
else:
render_template("index_upload.html",
message="Files uploaded are not supported...")
destination = "/".join([target, filename])
print("Accept incoming file:", filename)
print("Save it to:", destination)
upload.save(destination)
vidcap = cv2.VideoCapture(destination)
success, image = vidcap.read()
count = 0
message = ""
while success:
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(image)
isFighting = violenceDetector.Detect(netInput)
#siddet tespit edildi
if isFighting:
message += "Siddet Basladi."
else:
message += "Siddet Bitti."
count += 1
success, image = vidcap.read()
# return send_from_directory("images", filename, as_attachment=True)
return render_template("index_upload.html", message=message)
def LoadVideoImages(self, dataAugmentFunction_=None):
'''
This function will Block the current thread utill the images are loaded.
'''
try:
rgbImages = skvideo.io.vread(self.name)
numberOfLoadedImages = rgbImages.shape[0]
if self.totalFrames != numberOfLoadedImages:
print("Warning! self.totalFrames (=" + str(self.totalFrames) +
") != loadedImages(=" + str(numberOfLoadedImages) + ")!")
print(
"\t This may due to the inconsistence of OpenCV & Sk-Video..."
)
self.totalFrames = numberOfLoadedImages
self._calculateLabels()
if dataAugmentFunction_ != None:
rgbImages = dataAugmentFunction_(rgbImages)
self._images = np.zeros([
numberOfLoadedImages, dataSettings.IMAGE_SIZE,
dataSettings.IMAGE_SIZE, dataSettings.IMAGE_CHANNELS
])
for i in range(numberOfLoadedImages):
self._images[
i] = ImageUtils.ConvertImageFrom_RGB255_to_NetInput(
rgbImages[i])
self.hasImages = True
except Exception as error:
print("---------------------------------------------")
print("Video: " + self.name)
print(error)
print("ignore the video because of the above error...")
print("---------------------------------------------")
self.hasImages = False
def DetectViolence(PATH_FILE_NAME_OF_SOURCE_VIDEO,
PATH_FILE_NAME_TO_SAVE_RESULT):
violenceDetector = ViolenceDetector()
videoReader = cv2.VideoCapture(PATH_FILE_NAME_OF_SOURCE_VIDEO)
shouldSaveResult = (PATH_FILE_NAME_TO_SAVE_RESULT != None)
if shouldSaveResult:
videoSavor = VideoSavor(PATH_FILE_NAME_TO_SAVE_RESULT + "_Result",
videoReader)
listOfForwardTime = []
isCurrentFrameValid, currentImage = videoReader.read()
data = DataSet()
model = load_model(
'/home/furkan/five-video-classification-methods-master/inception.023-3.04.hdf5'
)
# Predict.
image_arr = np.expand_dims(currentImage, axis=0)
predictions = model.predict(image_arr)
label_predictions = {}
for i, label in enumerate(data.classes):
label_predictions[label] = predictions[0][i]
sorted_lps = sorted(label_predictions.items(),
key=operator.itemgetter(1),
reverse=True)
listeString = list()
listeValue = list()
for i, class_prediction in enumerate(sorted_lps):
# Just get the top five.
if i > 4:
break
#print("%s: %.2f" % (class_prediction[0], class_prediction[1]))
listeString.append(class_prediction[0])
listeValue.append(class_prediction[1])
maxValue = max(listeValue)
maxValueIndex = listeValue.index(maxValue)
#print(maxValueIndex,"--",maxValue)
#print(listeString[maxValueIndex])
i += 1
X = 0
while isCurrentFrameValid:
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(currentImage)
startDetectTime = time.time()
isFighting = violenceDetector.Detect(netInput)
endDetectTime = time.time()
listOfForwardTime.append(endDetectTime - startDetectTime)
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2 * deploySettings.BORDER_SIZE
currentImage = cv2.resize(currentImage, (targetSize, targetSize))
if isFighting: #şiddet tespit edildi
if X == 50:
listeString.clear()
listeValue.clear()
image_arr = np.expand_dims(currentImage, axis=0)
predictions = model.predict(image_arr)
label_predictions = {}
for i, label in enumerate(data.classes):
label_predictions[label] = predictions[0][i]
sorted_lps = sorted(label_predictions.items(),
key=operator.itemgetter(1),
reverse=True)
for i, class_prediction in enumerate(sorted_lps):
# Just get the top five.
if i > 4:
break
#print("%s: %.2f" % (class_prediction[0], class_prediction[1]))
listeString.append(class_prediction[0])
listeValue.append(class_prediction[1])
maxValue = 0
maxValue = max(listeValue)
maxValueIndex = listeValue.index(maxValue)
print(listeString[maxValueIndex], "--", maxValue)
print(listeString[maxValueIndex])
i += 1
x = 0
else:
X += 1
resultImage = cv2.copyMakeBorder(
currentImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.FIGHT_BORDER_COLOR)
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (10, 300)
fontScale = 1
fontColor = (255, 255, 255)
lineType = 2
cv2.putText(resultImage, listeString[maxValueIndex],
bottomLeftCornerOfText, font, fontScale, fontColor,
lineType)
print(listeString[maxValueIndex], "--", maxValue)
else:
resultImage = cv2.copyMakeBorder(
currentImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.NO_FIGHT_BORDER_COLOR)
cv2.imshow("Violence Detection", resultImage)
if shouldSaveResult:
videoSavor.AppendFrame(resultImage)
userResponse = cv2.waitKey(1)
if userResponse == ord('q'):
videoReader.release()
cv2.destroyAllWindows()
break
else:
isCurrentFrameValid, currentImage = videoReader.read()
PrintUnsmoothedResults(violenceDetector.unsmoothedResults)
averagedForwardTime = np.mean(listOfForwardTime)
print("Averaged Forward Time: ", averagedForwardTime)
def DetectViolence(PATH_FILE_NAME_OF_SOURCE_VIDEO, saveresult):
violenceDetector = ViolenceDetector()
videoReader = cv2.VideoCapture(PATH_FILE_NAME_OF_SOURCE_VIDEO)
if saveresult == True:
videoSavor = VideoSavor(PATH_FILE_NAME_OF_SOURCE_VIDEO + "_Result",
videoReader)
listOfForwardTime = []
isCurrentFrameValid, currentImage = videoReader.read()
while isCurrentFrameValid:
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(currentImage)
startDetectTime = time.time()
isFighting = violenceDetector.Detect(netInput)
endDetectTime = time.time()
listOfForwardTime.append(endDetectTime - startDetectTime)
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2 * deploySettings.BORDER_SIZE
currentImage = cv2.resize(currentImage, (targetSize, targetSize))
if isFighting:
resultImage = cv2.copyMakeBorder(
currentImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.FIGHT_BORDER_COLOR)
# Using cv2.putText() method
resultImage = cv2.putText(resultImage, 'Violence :(',
deploySettings.org, deploySettings.font,
deploySettings.fontScale,
deploySettings.color2,
deploySettings.thickness, cv2.LINE_AA)
else:
resultImage = cv2.copyMakeBorder(
currentImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.NO_FIGHT_BORDER_COLOR)
resultImage = cv2.putText(resultImage, 'NonViolence :)',
deploySettings.org, deploySettings.font,
deploySettings.fontScale,
deploySettings.color1,
deploySettings.thickness, cv2.LINE_AA)
cv2.imshow("Violence Detection", resultImage)
if saveresult == True:
videoSavor.AppendFrame(resultImage)
userResponse = cv2.waitKey(1)
if userResponse == ord('q'):
videoReader.release()
cv2.destroyAllWindows()
break
else:
isCurrentFrameValid, currentImage = videoReader.read()
PrintUnsmoothedResults(violenceDetector.unsmoothedResults)
averagedForwardTime = np.mean(listOfForwardTime)
print("Averaged Forward Time: ", averagedForwardTime)
def DetectViolence(PATH_FILE_NAME_OF_SOURCE_VIDEO):
videoReader = cv2.VideoCapture(PATH_FILE_NAME_OF_SOURCE_VIDEO)
listOfForwardTime = []
# isCurrentFrameValid : 아직 프레임이 남아있다.
# currentImage : 현재 이미지
isCurrentFrameValid, currentImage = videoReader.read()
cnt = 0
tol_cnt = 0
fighting = False
while isCurrentFrameValid:
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(currentImage)
startDetectTime = time.time()
# detecting
isFighting = violenceDetector.Detect(netInput)
endDetectTime = time.time()
listOfForwardTime.append(endDetectTime - startDetectTime)
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2 * deploySettings.BORDER_SIZE
resizedImage = cv2.resize(currentImage, (targetSize, targetSize))
if isFighting:
image_list[cnt] = currentImage
cnt += 1
resultImage = cv2.copyMakeBorder(
resizedImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.FIGHT_BORDER_COLOR)
else:
tol_cnt += 1
if tol_cnt > deploySettings.TOLERANCE:
tol_cnt = 0
cnt = 0
resultImage = cv2.copyMakeBorder(
resizedImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.NO_FIGHT_BORDER_COLOR)
cv2.imshow("Violence Detection", resultImage)
userResponse = cv2.waitKey(1)
if userResponse == ord('q'):
videoReader.release()
cv2.destroyAllWindows()
break
else:
isCurrentFrameValid, currentImage = videoReader.read()
if cnt >= deploySettings.THRESHOLD:
fighting = True
break
# boolean 값
PrintUnsmoothedResults(violenceDetector.unsmoothedResults)
averagedForwardTime = np.mean(listOfForwardTime)
print("Averaged Forward Time: ", averagedForwardTime)
# 폭력 감지가 되면
# fighting = true : 경보!!!!!
return fighting
def DetectViolence(PATH_FILE_NAME_OF_SOURCE_VIDEO,
PATH_FILE_NAME_TO_SAVE_RESULT):
violenceDetector = ViolenceDetector()
videoReader = cv2.VideoCapture(PATH_FILE_NAME_OF_SOURCE_VIDEO)
#videoReader.set(cv2.CAP_PROP_FPS, 80)
#video_fps = videoReader.get(cv2.CAP_PROP_FPS)
videoReader.get(cv2.CAP_PROP_FPS)
shouldSaveResult = (PATH_FILE_NAME_TO_SAVE_RESULT != None)
if shouldSaveResult:
videoSavor = VideoSavor(PATH_FILE_NAME_TO_SAVE_RESULT + "_Result",
videoReader)
listOfForwardTime = []
isCurrentFrameValid, currentImage = videoReader.read()
while isCurrentFrameValid:
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(currentImage)
startDetectTime = time.time()
isFighting = violenceDetector.Detect(netInput)
endDetectTime = time.time()
listOfForwardTime.append(endDetectTime - startDetectTime)
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2 * deploySettings.BORDER_SIZE
currentImage = cv2.resize(currentImage, (targetSize, targetSize))
if isFighting:
resultImage = cv2.copyMakeBorder(
currentImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.FIGHT_BORDER_COLOR)
cv2.putText(resultImage, "Violence detected", (20, 40),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 225), 2)
else:
resultImage = cv2.copyMakeBorder(
currentImage,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.NO_FIGHT_BORDER_COLOR)
cv2.putText(resultImage, "No violence", (20, 40),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
# display a piece of text to the frame (so we can benchmark
# fairly against the fast method)
# cv2.putText(resultImage, "Slow Method", (10, 30),
# cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
cv2.imshow("Violence Detection", resultImage)
if shouldSaveResult:
videoSavor.AppendFrame(resultImage)
userResponse = cv2.waitKey(1)
#userResponse = cv2.waitkey(int(2000/video_fps))
fps.update() #
if userResponse == ord('q'):
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed())) #
print("[INFO] approx. FPS: {:.2f}".format(fps.fps())) #
videoReader.release()
cv2.destroyAllWindows()
break
else:
isCurrentFrameValid, currentImage = videoReader.read()
PrintUnsmoothedResults(violenceDetector.unsmoothedResults)
averagedForwardTime = np.mean(listOfForwardTime)
print("Averaged Forward Time: ", averagedForwardTime)
def Check_TrainDataManager():
pauseLoadData = False
print("Start reading videos...")
dataManager = TrainDataManager(PATH_TO_DATA)
print("Read videos finished.")
while True:
'''
The following Info should be extracted before calling 'AssignBatchData()'
'''
listOfBatchInfo = []
listOfBatchInfo.append('dataManager.epoch=' + str(dataManager.epoch))
listOfBatchInfo.append('dataManager.step=' + str(dataManager.step))
batchData = BatchData()
startGetBatchTime = time.time()
dataManager.AssignBatchData(batchData)
finishGetBatchTime = time.time()
print("GetBatchTime = ", finishGetBatchTime - startGetBatchTime)
info = dataManager.GetQueueInfo()
print("\t" + info + "\n")
batchData.batchOfImages = batchData.batchOfImages.reshape([
batchData.batchSize * batchData.unrolledSize *
batchData.groupedSize, dataSettings.IMAGE_SIZE,
dataSettings.IMAGE_SIZE, dataSettings.IMAGE_CHANNELS
])
batchData.batchOfLabels = batchData.batchOfLabels.reshape([
batchData.batchSize * batchData.unrolledSize,
dataSettings.NUMBER_OF_CATEGORIES
])
i = 0
while i < batchData.batchOfImages.shape[0]:
currentImage = batchData.batchOfImages[i]
currentImage = ImageUtils.ConvertImageFrom_NetInput_to_CV(
currentImage)
currentImage = cv2.resize(currentImage, (500, 500))
currentLabel = batchData.batchOfLabels[int(
i / dataSettings.GROUPED_SIZE)]
listOfInfoToDisplay = []
listOfInfoToDisplay += listOfBatchInfo
listOfInfoToDisplay.append('i = ' + str(i))
listOfInfoToDisplay.append('batchImages.shape = ' +
str(batchData.batchOfImages.shape))
listOfInfoToDisplay.append('label = ' + str(currentLabel))
resultImage = DrawInfo(currentImage, listOfInfoToDisplay)
cv2.imshow("Result", resultImage)
userResponse = cv2.waitKey(0)
if userResponse == ord('n'):
i += 1
elif userResponse == ord('l'):
i = batchData.batchOfImages.shape[0] - 1
elif userResponse == ord('p'):
pauseLoadData = not pauseLoadData
if pauseLoadData:
dataManager.Pause()
else:
dataManager.Continue()
elif userResponse == ord('q'):
dataManager.Stop()
raise StopIteration()
def DetectViolence(PATH_FILE_NAME_OF_SOURCE_VIDEO,
PATH_FILE_NAME_TO_SAVE_RESULT):
violenceDetector = ViolenceDetector()
videoReader = cv2.VideoCapture(PATH_FILE_NAME_OF_SOURCE_VIDEO)
shouldSaveResult = (PATH_FILE_NAME_TO_SAVE_RESULT != None)
if shouldSaveResult:
videoSavor = VideoSavor(PATH_FILE_NAME_TO_SAVE_RESULT + "_Result",
videoReader)
listOfForwardTime = []
isCurrentFrameValid, currentImage = videoReader.read()
count = 0
baslangicsn = list()
bitissn = list()
while isCurrentFrameValid:
count += 1
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(currentImage)
startDetectTime = time.time()
isFighting = violenceDetector.Detect(netInput)
endDetectTime = time.time()
listOfForwardTime.append(endDetectTime - startDetectTime)
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2 * deploySettings.BORDER_SIZE
currentImage = cv2.resize(currentImage, (targetSize, targetSize))
if isFighting: #şiddet tespit edildi
p = 0
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (10, 50)
fontScale = 1
fontColor = (255, 255, 255)
lineType = 2
if len(baslangicsn) == len(bitissn):
baslangicsn.append(count / 25)
cv2.putText(currentImage, "Siddet tespit edildi",
bottomLeftCornerOfText, font, fontScale, fontColor,
lineType)
bottomLeftCornerOfText = (10, 450)
else:
if len(baslangicsn) != len(bitissn):
bitissn.append(count / 25)
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (10, 450)
fontScale = 1
fontColor = (255, 255, 255)
lineType = 2
cv2.putText(currentImage, "Siddet tespit edilmedi",
bottomLeftCornerOfText, font, fontScale, fontColor,
lineType)
cv2.imshow("Violence Detection", currentImage)
if shouldSaveResult:
videoSavor.AppendFrame(currentImage)
userResponse = cv2.waitKey(1)
if userResponse == ord('q'):
videoReader.release()
cv2.destroyAllWindows()
break
else:
isCurrentFrameValid, currentImage = videoReader.read()
PrintUnsmoothedResults(violenceDetector.unsmoothedResults)
averagedForwardTime = np.mean(listOfForwardTime)
bitissn.append(count / 25)
print(len(baslangicsn), "-------", len(bitissn))
for index in range(len(baslangicsn)):
try:
print("tespit edilen sureler", baslangicsn.pop(index), "------",
bitissn.pop(index))
except IndexError:
print("----son----")
print("Averaged Forward Time: ", averagedForwardTime)
def DetectViolence(PATH_FILE_NAME_TO_SAVE_RESULT):
# font for text used on video frames
font = cv2.FONT_HERSHEY_SIMPLEX
violenceDetector = ViolenceDetector()
capture = cv2.VideoCapture(0)
shouldSaveResult = (PATH_FILE_NAME_TO_SAVE_RESULT != None)
if shouldSaveResult:
videoSavor = VideoSavor(PATH_FILE_NAME_TO_SAVE_RESULT + "_Result",
capture)
listOfForwardTime = []
# Get some properties of VideoCapture (frame width, frame height and frames per second (fps)):
frame_width = capture.get(cv2.CAP_PROP_FRAME_WIDTH)
frame_height = capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
fps = capture.get(cv2.CAP_PROP_FPS)
# Print these values:
print("CV_CAP_PROP_FRAME_WIDTH: '{}'".format(frame_width))
print("CV_CAP_PROP_FRAME_HEIGHT : '{}'".format(frame_height))
print("CAP_PROP_FPS : '{}'".format(fps))
# Check if camera opened successfully
if capture.isOpened() is False:
print("Error opening the camera")
flag = False
# Read until video is completed
while capture.isOpened():
# Capture frame-by-frame from the camera
ret, frame = capture.read()
if ret is True:
# Display the captured frame:
# cv2.imshow('Input frame from the camera', frame)
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(frame)
startDetectTime = time.time()
isFighting = violenceDetector.Detect(netInput)
endDetectTime = time.time()
listOfForwardTime.append(endDetectTime - startDetectTime)
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2 * deploySettings.BORDER_SIZE
currentImage = cv2.resize(frame, (targetSize, targetSize))
if isFighting:
resultImage = cv2.copyMakeBorder(
frame,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.FIGHT_BORDER_COLOR)
else:
resultImage = cv2.copyMakeBorder(
frame,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
deploySettings.BORDER_SIZE,
cv2.BORDER_CONSTANT,
value=deploySettings.NO_FIGHT_BORDER_COLOR)
# frameText = "Violence Detected!" if isFighting else "No Violence Detected."
# textColor = deploySettings.FIGHT_BORDER_COLOR if isFighting else deploySettings.NO_FIGHT_BORDER_COLOR
# cv2.putText(frame, frameText, (50, 50), font, 4, (0, 255, 0), 2, cv2.LINE_AA)
cv2.imshow("Violence Detection", resultImage)
if shouldSaveResult:
videoSavor.AppendFrame(resultImage)
userResponse = cv2.waitKey(1)
if userResponse == ord('q'):
capture.release()
cv2.destroyAllWindows()
flag = True
break
else:
isCurrentFrameValid, currentImage = capture.read()
print("Details about current frame:")
PrintUnsmoothedResults(violenceDetector.unsmoothedResults)
averagedForwardTime = np.mean(listOfForwardTime)
# print("Averaged Forward Time: ", averagedForwardTime)
if flag:
break
cap = cv2.VideoCapture(0)
violenceDetector = ViolenceDetector()
count=0
baslangicsn=list()
bitissn=list()
while(True):
# Capture frame-by-frame
ret, currentImage = cap.read()
# do what you want with frame
# and then save to file
cv2.imwrite('/home/murat/Desktop/image.png', currentImage)
count +=1
netInput = ImageUtils.ConvertImageFrom_CV_to_NetInput(currentImage)
startDetectTime = time.time()
isFighting = violenceDetector.Detect(netInput)
endDetectTime = time.time()
targetSize = deploySettings.DISPLAY_IMAGE_SIZE - 2*deploySettings.BORDER_SIZE
currentImage = cv2.resize(currentImage, (targetSize, targetSize))
if isFighting:#şiddet tespit edildi
p=0
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (10,50)
fontScale = 1
fontColor = (255,255,255)
lineType = 2