Otomatisasi pemrosesan video untuk mendeteksi pelanggaran adalah salah satu bidang yang dituntut dari computer vision di banyak industri.
Hari ini kami akan mencoba mendeteksi di video tidak adanya klien dalam bingkai pada saat operasi dalam sistem otomatis.
Kami memiliki catatan pembuangan dari kamera pengintai di tempat kerja karyawan dan log operasi.
, . MobileNet CSRT Tracker opencv. Tesseract-OCR.
MobileNet. 20 . : . Github.
cv2 pytesseract.
!pip install opencv-python !pip install pytesseract
pytesseract Tesseract-OCR c .
Tesseract-OCR:
import os
video_path = ... #
tesseract_path = ... # Tesseract
os.environ["PATH"] += os.pathsep + tesseract_path
import pytesseract
import cv2
import imutils
import pandas as pd
import datetime as dt
, . , / :
df = pd.DataFrame(columns = ['', ' '])
work_place = () #,
date = None #
tracked = False #
, . , :
prototxt = 'MobileNetSSD_deploy.prototxt' #
weights = 'MobileNetSSD_deploy.caffemodel' #
20 , :
classNames = {0: 'background',
1: 'aeroplane',
2: 'bicycle',
3: 'bird',
4: 'boat',
5: 'bottle',
6: 'bus',
7: 'car',
8: 'cat',
9: 'chair',
10: 'cow',
11: 'diningtable',
12: 'dog',
13: 'horse',
14: 'motorbike',
15: 'person',
16: 'pottedplant',
17: 'sheep',
18: 'sofa',
19: 'train',
20: 'tvmonitor'}
, .
thr = 0.1 #
:
net = cv2.dnn.readNetFromCaffe(prototxt, weights) #
cv2.VideoCapture, :
cap = cv2.VideoCapture(video_path)
, .read(), . , . . :
%%time
cap = cv2.VideoCapture(video_path)
total_frame = 0
while True:
success, frame = cap.read()
if success:
total_frame += 1
else:
break
video_length = ... #
fps = round(total_frame / video_length)
fps
, . 100- 2 .
, , , . , , .
while cap.isOpened():
ret, frame = cap.read()
if ret:
frame = imutils.resize(frame, width=1200) # ,
# ,
if len(work_place) == 0:
cv2.putText(frame, 'Set the client\'s location', (0, 90), cv2.FONT_HERSHEY_SIMPLEX,
2, (0,255,0), 2)
work_place = cv2.selectROI('frame', frame, fromCenter=False, showCrosshair=True)
x, y, w, h = [int(coord) for coord in work_place]
#
if not date:
try:
cv2.putText(frame, 'Set the date, (0, 160), cv2.FONT_HERSHEY_SIMPLEX,
2, (0,255,0), 2)
date = cv2.selectROI('frame', frame, fromCenter=False, showCrosshair=True)
date_x, date_y, date_w, date_h = [int(coord) for coord in date]
date_ = frame[date_y : date_y+date_h, date_x : date_x+date_w]
date_ = cv2.cvtColor(date_, cv2.COLOR_BGR2GRAY) #
#date_ = cv2.threshold(date_, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
date_ = cv2.threshold(date_, 180, 255, 0)[1] #
date = pytesseract.image_to_string(date_)
date = dt.datetime.strptime(date, '%Y-%m-%d %H:%M:%S')
except:
print(' , -- ::')
date_ = input()
date = dt.datetime.strptime(date_, '%Y-%m-%d %H:%M:%S')
if cap.get(1) % fps == 0:
date += dt.timedelta(seconds = 1)
if not tracked or (cap.get(1) % (fps * 30) == 0):
#
frame_resized = cv2.resize(frame, (300, 300)) # 300 300
blob = cv2.dnn.blobFromImage(frame_resized, 0.007843,
(300,300), (127.5, 127.5, 127.5), False)
#
net.setInput(blob)
detections = net.forward()
#[0, 0, object, [0, class_id, confidence, xLeftBottom, yLeftBottom, xRightTop, yRightTop]]
#
cols = frame_resized.shape[1]
rows = frame_resized.shape[0]
#
for obj in detections[0,0, :, :]:
confidence = obj[2]
if confidence > thr:
class_id = int(obj[1])
if class_id == 15:
xLeftBottom = int(obj[3] * cols)
yLeftBottom = int(obj[4] * rows)
xRightTop = int(obj[5] * cols)
yRightTop = int(obj[6] * rows)
#
heightFactor = frame.shape[0] / 300.0
widthFactor = frame.shape[1] / 300.0
#
xLeftBottom = int(widthFactor * xLeftBottom)
yLeftBottom = int(heightFactor * yLeftBottom)
xRightTop = int(widthFactor * xRightTop)
yRightTop = int(heightFactor * yRightTop)
#
xCenter = xLeftBottom + (xRightTop - xLeftBottom)/2
yCenter = yLeftBottom + (yRightTop - yLeftBottom)/2
#
if xCenter < x + w and yCenter < y + h and xCenter > x and yCenter > y:
tracker = cv2.TrackerCSRT_create()
tracker.init(frame, (xLeftBottom, yLeftBottom, xRightTop-xLeftBottom, yRightTop-yLeftBottom))
tracked = True
cv2.rectangle(frame, (xLeftBottom,yLeftBottom), (xRightTop,yRightTop), (0,255,0), 3, 1)
break
else:
tracked = False
else:
_, bbox = tracker.update(frame)
X, Y, W, H = [int(coord) for coord in bbox]
xCenter = X + W/2
yCenter = Y + H/2
if xCenter < x + w and yCenter < y + h and xCenter > x and yCenter > y:
tracked = True
cv2.rectangle(frame, (X,Y), (X + W, Y + H), (255,255,0), 3, 1)
else:
tracked = False
cv2.imshow('frame', frame)
df.loc[cap.get(1), :] = [date, tracked]
print(cap.get(1), date, tracked) # , /
if cv2.waitKey(1) == 27: #ESC
break
else:
break
cap.release()
cv2.destroyAllWindows()
.read() : , , – . , , , .
. , «-- ::». , : , . date
.
Tesseract-
, . , , .
.get() 1 , , fps
, date
. tesseract, , .
: cv2.resize() cv2.dnn.blobFromImage(). , . , detections
. , 20 .
, 15. , , tracked
True . tracked
date
df
.
, . , tracked
True False, .
. , , . .
, / . , , . , .
df_ = df.groupby('', as_index=False).agg(max)
df_.to_excel('output.xlsx', index=False)
, :
. , . opencv. , , .
. .
. , , . «» , .
Pelacak pembelajaran mendalam dapat memecahkan masalah pertama dan kedua. Misalnya, pelacak GOTURN
. Pelacak ini diterapkan di perpustakaan opencv
, tetapi untuk operasinya Anda perlu mengunduh file tambahan. Anda juga dapat menggunakan pelacak populer Re3
atau pelacak yang baru-baru ini diperkenalkan AcurusTrack
. Masalah ketiga dapat diselesaikan dengan mengganti jaringan saraf dan / atau melatihnya kembali pada orang yang duduk.