I have made a motion detection with cv2 which get video as input and as output it returns a csv file contain the time of the motions that happend during the video. the problem is, I have used datetime.now() for the time that the motion happening but the speed of the video is much more faster. I don't have problem with the speed of the video, I don't know how to say the object moved at this time of the video.
It would be great if you could help me :)
here is the code:
def press_it():
STime=0
FTime=0
moji = True
first_frame = None
status_list = [None,None]
times = []
startTime=datetime.now()
print(startTime)
#Dataframe to store the time values during which object detection and movement appears | "C:/Users/mojta/Desktop/videos/pred.mp4"
df = pd.DataFrame(columns=['Start','End','Duration'])
cam = cv2.VideoCapture(file)
frames = cam.get(cv2.CAP_PROP_FRAME_COUNT)
fps = cam.get(cv2.CAP_PROP_FPS)
seconds = round(frames / fps)
length = int(cam.get(cv2.CAP_PROP_FRAME_COUNT))
print(length)
y=int(values["-IN4-"])
x=int(values["-IN3-"])
h= int(values["-IN5-"])
w= int(values["-IN6-"])
#Iterate through frames and display the window
while cam.isOpened():
check, frame = cam.read()
length-=1
if moji==True:
STime=datetime.now()
moji=False
frame = frame[y:y+h, x:x+w]
#Status at beginning of the recording is zero as the object is not visisble
status = 0
#Converting each frame into gray scale image
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
#Convert grayscale image to GaussianBlur
gray = cv2.GaussianBlur(gray, (21,21), 0)
#This is used to store the first image/frame of the video
if first_frame is None or length%500==0:
first_frame = gray
continue
#Calculates the difference between the first frame and another frames
delta_frame = cv2.absdiff(first_frame,gray)
#Giving a threshold value, such that it will convert the difference value with less than 30 to black
#If it is greater than 30, then it will convert those pixels to white
_,thresh_delta = cv2.threshold(delta_frame, 30, 255, cv2.THRESH_BINARY)
thresh_delta = cv2.dilate(thresh_delta, None, iterations=3)
#Defining the contour area i.e., borders
cnts,_ = cv2.findContours(thresh_delta.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#Removes noises and shadows, i.e., it will keep only that part white, which has area greater than 10000 pixels
Acuraccy = acuraccySlider
for cont in cnts:
if cv2.contourArea(cont) < Acuraccy:
continue
#Change in status when the object is being detected
status = 1
#creates a rectangular box around the object in the frame
(x1, y1, w1, h1) = cv2.boundingRect(cont)
cv2.rectangle(frame, (x1,y1), (x1+w1,y1+h1), (0,0,255), 3)
#List of status for every frame
status_list.append(status)
status_list = status_list[-2:]
#Record datetime in a list when change occurs
if status_list[-1]==1 and status_list[-2]==0:
times.append(datetime.now()-startTime)
if status_list[-1]==0 and status_list[-2]==1:
times.append(datetime.now()-startTime)
#Opening all types of frames/images
cv2.imshow("Grey Scale",gray)
cv2.imshow("Delta", delta_frame)
cv2.imshow("Threshold", thresh_delta)
cv2.imshow("Colored frame",frame)
last_frame_num = cam.get(cv2.CAP_PROP_FRAME_COUNT)
#Generate a new frame after every 1 millisecond
key = cv2.waitKey(1)
#If entered 'q' on keyboard, breaks out of loop, and window gets destroyed
print(length)
if key == ord('q') or length<=10:
if status==1:
times.append(datetime.now()-startTime)
FTime=datetime.now()
break
#Store time values in a Dataframe
DURATION=FTime-STime
FINAL = DURATION/seconds
for i in range(0,len(times),2):
if len(times)%2==1 and i==len(times)-1:
break
df = df.append({'Start':times[i],'End':times[i+1],'Duration':(times[i+1]-times[i])}, ignore_index=True)
#Write the dataframe to a CSV file
df.to_csv("Times.csv")
cam.release()
#Closes all the windows
cv2.destroyAllWindows
window.Close()
