違反を検出するためのビデオ処理の自動化は、多くの業界でコンピュータビジョンの要求される領域の1つです。
今日は、自動システムでの操作時に、フレーム内にクライアントが存在しないことをビデオで検出しようとします。
従業員の職場の監視カメラからの記録と操作のログを自由に利用できます。
, . 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. , , .
. .
. , , . «» , .
ディープラーニングトラッカーは、最初と2番目の問題を解決できます。たとえば、トラッカー GOTURN
。このトラッカーはライブラリopencv
に実装されています が、その操作のために追加のファイルをダウンロードする必要があります。人気のトラッカーRe3
や最近導入されたトラッカーを 使用することもでき AcurusTrack
ます。3番目の問題は、ニューラルネットワークを交換するか、着席している人に再トレーニングすることで解決できます。