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# A Gender and Age Detection program by Mahesh Sawant
import os
import pandas as pd
import cv2
import math
import argparse
dic = {"images": [], "gender": [], "age": []}
def highlightFace(net, frame, conf_threshold=0.7):
frameOpencvDnn = frame.copy()
frameHeight = frameOpencvDnn.shape[0]
frameWidth = frameOpencvDnn.shape[1]
blob = cv2.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)
net.setInput(blob)
detections = net.forward()
faceBoxes = []
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)
faceBoxes.append([x1, y1, x2, y2])
cv2.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight / 150)), 8)
return frameOpencvDnn, faceBoxes
def process_image(image):
# parser=argparse.ArgumentParser()
# parser.add_argument('--image')
#
# args=parser.parse_args()
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']
faceNet = cv2.dnn.readNet(faceModel, faceProto)
ageNet = cv2.dnn.readNet(ageModel, ageProto)
genderNet = cv2.dnn.readNet(genderModel, genderProto)
video = cv2.VideoCapture(image)
padding = 20
while cv2.waitKey(1) < 0:
try:
hasFrame, frame = video.read()
if not hasFrame:
cv2.waitKey()
break
resultImg, faceBoxes = highlightFace(faceNet, frame)
if not faceBoxes:
print("No face detected")
for faceBox in faceBoxes:
face = frame[max(0, faceBox[1] - padding):
min(faceBox[3] + padding, frame.shape[0] - 1), max(0, faceBox[0] - padding)
:min(faceBox[2] + padding,
frame.shape[1] - 1)]
blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
genderNet.setInput(blob)
genderPreds = genderNet.forward()
gender = genderList[genderPreds[0].argmax()]
print(f'Gender: {gender}')
ageNet.setInput(blob)
agePreds = ageNet.forward()
age = ageList[agePreds[0].argmax()]
print(f'Age: {age[1:-1]} years')
dic['images'].append(image)
dic['gender'].append(gender)
dic['age'].append(age[1:-1])
# cv2.putText(resultImg, f'{gender}, {age}', (faceBox[0], faceBox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,255), 2, cv2.LINE_AA)
# cv2.imshow("Detecting age and gender", resultImg)
except Exception as e:
continue
import boto3
s3 = boto3.resource(
service_name = 's3',
region_name = 'ap-south-1',
aws_access_key_id = 'AKIAYNE4X3VIWUPXM75R',
aws_secret_access_key ='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'
)
def download_s3_folder(bucket, folder, local_dir='./images'):
bucket = s3.Bucket(bucket)
for obj in bucket.objects.filter(Prefix=folder):
target = obj.key if local_dir is None \
else os.path.join(local_dir, os.path.relpath(obj.key, folder))
if not os.path.exists(os.path.dirname(target)):
os.makedirs(os.path.dirname(target))
if obj.key[-1] == '/':
continue
bucket.download_file(obj.key, target)
def predict_age_gender():
image = os.listdir('images')
for img in image:
img = './images/' + img
process_image(img)
print(dic)
df = pd.DataFrame.from_dict(dic, orient='index').transpose()
df.head()
df.to_excel("./output/result_s3.xls")
download_s3_folder('genderagedata','input_images')
predict_age_gender()
s3.Bucket('genderagedata').upload_file(Filename='./output/result_s3.xls', Key='output_images/result.xls')
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