learnopencv 之 AgeGender @ Jupyter

本系列运行在 Jupyter 上 主要参考github 记录是督促自己学习

github  https://github.com/spmallick/learnopencv.git

 

该文与原始的差别主要在 

原始    args = parser.parse_args()
改为    args = parser.parse_args(args=['--input', './sample1.jpg'])

# Import required modules
import cv2 as cv
import math
import time
import argparse

import sys
sys.path.append("..")
import pltcvshow

def getFaceBox(net, frame, conf_threshold=0.7):
    frameOpencvDnn = frame.copy()
    frameHeight = frameOpencvDnn.shape[0]
    frameWidth = frameOpencvDnn.shape[1]
    blob = cv.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, 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])
            cv.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight/150)), 8)
    return frameOpencvDnn, bboxes


parser = argparse.ArgumentParser(description='Use this script to run age and gender recognition using OpenCV.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
# args = parser.parse_args()
args = parser.parse_args(args=['--input', './sample1.jpg'])

faceProto = "opencv_face_detector.pbtxt"
faceModel = "opencv_face_detector_uint8.pb"

ageProto = "age_deploy.prototxt"
ageModel = "age_net.caffemodel"

genderProto = "gender_deploy.prototxt"
genderModel = "gender_net.caffemodel"

MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
genderList = ['Male', 'Female']

# Load network
ageNet = cv.dnn.readNet(ageModel, ageProto)
genderNet = cv.dnn.readNet(genderModel, genderProto)
faceNet = cv.dnn.readNet(faceModel, faceProto)

# Open a video file or an image file or a camera stream
cap = cv.VideoCapture(args.input if args.input else 0)
padding = 20
while cv.waitKey(1) < 0:
    # Read frame
    t = time.time()
    hasFrame, frame = cap.read()
    if not hasFrame:
        cv.waitKey()
        break

    frameFace, bboxes = getFaceBox(faceNet, frame)
    if not bboxes:
        print("No face Detected, Checking next frame")
        continue

    for bbox in bboxes:
        # print(bbox)
        face = frame[max(0,bbox[1]-padding):min(bbox[3]+padding,frame.shape[0]-1),max(0,bbox[0]-padding):min(bbox[2]+padding, frame.shape[1]-1)]

        blob = cv.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
        genderNet.setInput(blob)
        genderPreds = genderNet.forward()
        gender = genderList[genderPreds[0].argmax()]
        # print("Gender Output : {}".format(genderPreds))
        print("Gender : {}, conf = {:.3f}".format(gender, genderPreds[0].max()))

        ageNet.setInput(blob)
        agePreds = ageNet.forward()
        age = ageList[agePreds[0].argmax()]
        print("Age Output : {}".format(agePreds))
        print("Age : {}, conf = {:.3f}".format(age, agePreds[0].max()))

        label = "{},{}".format(gender, age)
        cv.putText(frameFace, label, (bbox[0], bbox[1]-10), cv.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv.LINE_AA)
        # cv.imshow("Age Gender Demo", frameFace)
        cv.imwrite("age-gender-out-{}".format(args.input),frameFace)
        pltcvshow.cv_show(frameFace, "Age Gender Demo")
    print("time : {:.3f}".format(time.time() - t))

运行结果

Gender : Male, conf = 1.000
Age Output : [[3.7542086e-05 7.3026726e-04 9.7675973e-01 7.0780763e-05 2.1828709e-02
  1.8040613e-04 3.4971791e-04 4.2986831e-05]]
Age : (8-12), conf = 0.977
Gender : Male, conf = 1.000
Age Output : [[2.5715157e-01 6.5672058e-01 8.5812099e-02 5.9076989e-05 7.5025520e-05
  1.0845805e-04 3.2667591e-05 4.0536001e-05]]
Age : (4-6), conf = 0.657
Gender : Female, conf = 0.989
Age Output : [[5.1279640e-06 5.3716817e-06 4.8542512e-03 6.6057127e-03 9.8655695e-01
  1.9603276e-03 8.6553655e-06 3.6936779e-06]]
Age : (25-32), conf = 0.987
time : 0.721

图片如下: