import random
import glob
import os.path
from data import DataSet
from processor import process_image
from keras.models import load_model
import base64
from PIL import Image
import StringIO
app = Flask(__name__)
app.config['SECRET_KEY'] = 'vnkdjnfjknfl1232#'
socketio = SocketIO(app)
violenceDetector = ViolenceDetector()
count = 0
baslangicsn = list()
bitissn = list()
@app.route('/')
def sessions():
return render_template('index_webcam.html')
def messageReceived(methods=['GET', 'POST']):
print('message was received!!!')
import settings.DeploySettings as deploySettings
import settings.DataSettings as dataSettings
import src.data.ImageUtils as ImageUtils
import numpy as np
import operator
import random
import glob
import os.path
from data import DataSet
from processor import process_image
from keras.models import load_model
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()
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
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_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 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, 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)
num_frames = 0
startTime = time.time()
if shouldSaveResult:
videoSavor = VideoSavor(
PATH_FILE_NAME_TO_SAVE_RESULT + "_Result", videoReader)
listOfForwardTime = []
isCurrentFrameValid, currentImage = videoReader.read()
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)
while isCurrentFrameValid:
nowTime = time.time()
if ((nowTime - startTime)) > num_frames:
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)
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)
startTime = time.time()
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,
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)
