Die Automatisierung der Videoverarbeitung zur Erkennung von Verstößen ist in vielen Branchen einer der gefragtesten Bereiche der Bildverarbeitung.
Heute werden wir versuchen, auf dem Video die Abwesenheit des Clients im Frame zum Zeitpunkt des Vorgangs im automatisierten System zu erkennen.
Wir verfügen über Aufzeichnungen von Überwachungskameras am Arbeitsplatz des Mitarbeiters und ein Betriebsprotokoll.
, . 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. , , .
. .
. , , . «» , .
Deep Learning Tracker können das erste und zweite Problem lösen. Zum Beispiel ein Tracker GOTURN
. Dieser Tracker ist in der Bibliothek implementiert opencv
, für den Betrieb müssen Sie jedoch zusätzliche Dateien herunterladen. Sie können auch den beliebten Tracker Re3
oder den kürzlich eingeführten Tracker verwenden AcurusTrack
. Das dritte Problem kann gelöst werden, indem das neuronale Netzwerk ersetzt und / oder an sitzenden Personen umgeschult wird.