OpenCV3.3+ ,算法出自论文《SSD: Single Shot MultiBox Detector》。OpenCV提供了caffe和tensorflow两个版本。
#!/usr/bin/env python
# -*- coding:utf-8 -*-
from __future__ import division
import cv2
import time
import sys
def detectFaceOpenCVDnn(net, frame):
frameOpencvDnn = frame.copy()
frameHeight = frameOpencvDnn.shape[0]
frameWidth = frameOpencvDnn.shape[1]
'''检测'''
blob = cv2.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], False, False)
net.setInput(blob)
detections = net.forward()
bboxes = []
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > conf_threshold:
x1 = int(detections[0, 0, i, 3] * frameWidth)
y1 = int(detections[0, 0, i, 4] * frameHeight)
x2 = int(detections[0, 0, i, 5] * frameWidth)
y2 = int(detections[0, 0, i, 6] * frameHeight)
bboxes.append([x1, y1, x2, y2])
cv2.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight / 150)), 8)
return frameOpencvDnn, bboxes
if __name__ == "__main__":
# OpenCV DNN supports 2 networks.
# 1. FP16 version of the original caffe implementation ( 5.4 MB )
# 2. 8 bit Quantized version using Tensorflow ( 2.7 MB )
'''模型加载'''
DNN = "TF"
if DNN == "CAFFE":
modelFile = "./model/res10_300x300_ssd_iter_140000_fp16.caffemodel"
configFile = "./model/deploy.prototxt"
net = cv2.dnn.readNetFromCaffe(configFile, modelFile)
else:
modelFile = "./model/opencv_face_detector_uint8.pb"
configFile = "./model/opencv_face_detector.pbtxt"
net = cv2.dnn.readNetFromTensorflow(modelFile, configFile)
conf_threshold = 0.7
source = 0
if len(sys.argv) > 1:
source = sys.argv[1]
cap = cv2.VideoCapture(source)
hasFrame, frame = cap.read()
vid_writer = cv2.VideoWriter('output-dnn-{}.avi'.format(str(source).split(".")[0]),
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 15, (frame.shape[1], frame.shape[0]))
frame_count = 0
tt_opencvDnn = 0
while (1):
hasFrame, frame = cap.read()
if not hasFrame:
break
frame_count += 1
t = time.time()
outOpencvDnn, bboxes = detectFaceOpenCVDnn(net, frame)
tt_opencvDnn += time.time() - t
fpsOpencvDnn = frame_count / tt_opencvDnn
label = "OpenCV DNN ; FPS : {:.2f}".format(fpsOpencvDnn)
cv2.putText(outOpencvDnn, label, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.4, (0, 0, 255), 3, cv2.LINE_AA)
cv2.imshow("Face Detection Comparison", outOpencvDnn)
vid_writer.write(outOpencvDnn)
if frame_count == 1:
tt_opencvDnn = 0
k = cv2.waitKey(10)
if k == 27:
break
cv2.destroyAllWindows()
vid_writer.release()