OpenCV3.3で公式にサポートされたDNN(深層ニューラルネットワーク)モジュールの
Python版mobilenetサンプルを動作させてみました。
学習済みモデルは以下URLにあるはずだが、リンク切れていたため、
https://github.com/chuanqi305/MobileNet-SSD/blob/master/MobileNetSSD_train.caffemodel
ひとまず、以下のURLから取得。
https://drive.google.com/file/d/0BwY-lpO6tzxHRHNCdlRKczIzaEU/view?usp=sharing
動画は以下。
CPUのみでもそこそこ動く。
www.youtube.com
ソースコードは以下。
基本的には、「opencv\sources\samples\dnn\mobilenet_ssd_python.py」と同じ。
自分の環境構築が悪いのか、そのままで動作しなかったところを一部修正。
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import argparse
try:
#import cv2 as cv
import cv2 #[修正] cv2呼び出ししているため、as cvを削除
from cv2 import dnn #[修正] dnnモジュールをインポート
except ImportError:
raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
inWidth = 300
inHeight = 300
WHRatio = inWidth / float(inHeight)
inScaleFactor = 0.007843
meanVal = 127.5
classNames = ('background',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--video", help="path to video file. If empty, camera's stream will be used")
parser.add_argument("--prototxt", default="MobileNetSSD_300x300.prototxt",
help="path to caffe prototxt")
parser.add_argument("-c", "--caffemodel", help="path to caffemodel file, download it here: "
"https://github.com/chuanqi305/MobileNet-SSD/blob/master/MobileNetSSD_train.caffemodel")
parser.add_argument("--thr", default=0.2, help="confidence threshold to filter out weak detections")
args = parser.parse_args()
net = dnn.readNetFromCaffe(args.prototxt, args.caffemodel)
#if len(args.video): #[修正] --video未指定時にlen()で長さ取得できないはずなので修正
if args.video != None:
cap = cv2.VideoCapture(args.video)
else:
cap = cv2.VideoCapture(0)
while True:
# Capture frame-by-frame
ret, frame = cap.read()
blob = dnn.blobFromImage(frame, inScaleFactor, (inWidth, inHeight), meanVal)
net.setInput(blob)
detections = net.forward()
cols = frame.shape[1]
rows = frame.shape[0]
if cols / float(rows) > WHRatio:
cropSize = (int(rows * WHRatio), rows)
else:
cropSize = (cols, int(cols / WHRatio))
y1 = (rows - cropSize[1]) / 2
y2 = y1 + cropSize[1]
x1 = (cols - cropSize[0]) / 2
x2 = x1 + cropSize[0]
frame = frame[y1:y2, x1:x2]
cols = frame.shape[1]
rows = frame.shape[0]
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > args.thr:
class_id = int(detections[0, 0, i, 1])
xLeftBottom = int(detections[0, 0, i, 3] * cols)
yLeftBottom = int(detections[0, 0, i, 4] * rows)
xRightTop = int(detections[0, 0, i, 5] * cols)
yRightTop = int(detections[0, 0, i, 6] * rows)
cv2.rectangle(frame, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop),
(0, 255, 0))
label = classNames[class_id] + ": " + str(confidence)
labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
cv2.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]),
(xLeftBottom + labelSize[0], yLeftBottom + baseLine),
(255, 255, 255), cv2.FILLED)
cv2.putText(frame, label, (xLeftBottom, yLeftBottom),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
cv2.imshow("detections", frame)
if cv2.waitKey(1) >= 0:
break
