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add age & gender detection combination tutorial
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‎README.md‎

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@@ -62,6 +62,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
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-[SIFT Feature Extraction using OpenCV in Python](https://www.thepythoncode.com/article/sift-feature-extraction-using-opencv-in-python). ([code](machine-learning/sift))
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-[Age Prediction using OpenCV in Python](https://www.thepythoncode.com/article/predict-age-using-opencv). ([code](machine-learning/face-age-prediction))
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-[Gender Detection using OpenCV in Python](https://www.thepythoncode.com/article/gender-detection-using-opencv-in-python). ([code](machine-learning/face-gender-detection))
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-[Age and Gender Detection using OpenCV in Python](https://www.thepythoncode.com/article/gender-and-age-detection-using-opencv-python). ([code](machine-learning/age-and-gender-detection))
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-[Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
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-[How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition))
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-[Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).
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#[Age and Gender Detection using OpenCV in Python](https://www.thepythoncode.com/article/gender-and-age-detection-using-opencv-python)
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To run this:
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-`pip3 install -r requirements.txt`
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- Check[the tutorial](https://www.thepythoncode.com/article/gender-and-age-detection-using-opencv-python) for more information on how to set this up.
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- After you download the models weights and architectures, you can perform age & gender detection on any image:
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```
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$ python age_and_gender_detection.py images/kids.jpg
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```
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- You can also use your camera, via the `age_and_gender_detection_live.py` script.
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# Import Libraries
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importcv2
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importnumpyasnp
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# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
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FACE_PROTO="weights/deploy.prototxt.txt"
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# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel
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FACE_MODEL="weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"
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# The gender model architecture
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# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ
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GENDER_MODEL='weights/deploy_gender.prototxt'
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# The gender model pre-trained weights
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# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHP
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GENDER_PROTO='weights/gender_net.caffemodel'
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# Each Caffe Model impose the shape of the input image also image preprocessing is required like mean
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# substraction to eliminate the effect of illunination changes
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MODEL_MEAN_VALUES= (78.4263377603,87.7689143744,114.895847746)
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# Represent the gender classes
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GENDER_LIST= ['Male','Female']
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# The model architecture
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# download from: https://drive.google.com/open?id=1kiusFljZc9QfcIYdU2s7xrtWHTraHwmW
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AGE_MODEL='weights/deploy_age.prototxt'
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# The model pre-trained weights
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# download from: https://drive.google.com/open?id=1kWv0AjxGSN0g31OeJa02eBGM0R_jcjIl
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AGE_PROTO='weights/age_net.caffemodel'
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# Represent the 8 age classes of this CNN probability layer
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AGE_INTERVALS= ['(0, 2)','(4, 6)','(8, 12)','(15, 20)',
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'(25, 32)','(38, 43)','(48, 53)','(60, 100)']
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# Initialize frame size
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frame_width=1280
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frame_height=720
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# load face Caffe model
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face_net=cv2.dnn.readNetFromCaffe(FACE_PROTO,FACE_MODEL)
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# Load age prediction model
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age_net=cv2.dnn.readNetFromCaffe(AGE_MODEL,AGE_PROTO)
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# Load gender prediction model
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gender_net=cv2.dnn.readNetFromCaffe(GENDER_MODEL,GENDER_PROTO)
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defget_faces(frame,confidence_threshold=0.5):
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# convert the frame into a blob to be ready for NN input
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blob=cv2.dnn.blobFromImage(frame,1.0, (300,300), (104,177.0,123.0))
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# set the image as input to the NN
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face_net.setInput(blob)
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# perform inference and get predictions
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output=np.squeeze(face_net.forward())
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# initialize the result list
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faces= []
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# Loop over the faces detected
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foriinrange(output.shape[0]):
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confidence=output[i,2]
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ifconfidence>confidence_threshold:
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box=output[i,3:7]* \
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np.array([frame.shape[1],frame.shape[0],
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frame.shape[1],frame.shape[0]])
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# convert to integers
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start_x,start_y,end_x,end_y=box.astype(np.int)
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# widen the box a little
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start_x,start_y,end_x,end_y=start_x- \
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10,start_y-10,end_x+10,end_y+10
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start_x=0ifstart_x<0elsestart_x
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start_y=0ifstart_y<0elsestart_y
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end_x=0ifend_x<0elseend_x
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end_y=0ifend_y<0elseend_y
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# append to our list
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faces.append((start_x,start_y,end_x,end_y))
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returnfaces
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defdisplay_img(title,img):
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"""Displays an image on screen and maintains the output until the user presses a key"""
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# Display Image on screen
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cv2.imshow(title,img)
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# Mantain output until user presses a key
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cv2.waitKey(0)
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# Destroy windows when user presses a key
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cv2.destroyAllWindows()
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# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv
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defimage_resize(image,width=None,height=None,inter=cv2.INTER_AREA):
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# initialize the dimensions of the image to be resized and
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# grab the image size
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dim=None
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(h,w)=image.shape[:2]
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# if both the width and height are None, then return the
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# original image
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ifwidthisNoneandheightisNone:
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returnimage
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# check to see if the width is None
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ifwidthisNone:
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# calculate the ratio of the height and construct the
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# dimensions
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r=height/float(h)
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dim= (int(w*r),height)
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# otherwise, the height is None
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else:
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# calculate the ratio of the width and construct the
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# dimensions
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r=width/float(w)
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dim= (width,int(h*r))
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# resize the image
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returncv2.resize(image,dim,interpolation=inter)
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defget_gender_predictions(face_img):
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blob=cv2.dnn.blobFromImage(
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image=face_img,scalefactor=1.0,size=(227,227),
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mean=MODEL_MEAN_VALUES,swapRB=False,crop=False
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)
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gender_net.setInput(blob)
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returngender_net.forward()
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defget_age_predictions(face_img):
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blob=cv2.dnn.blobFromImage(
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image=face_img,scalefactor=1.0,size=(227,227),
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mean=MODEL_MEAN_VALUES,swapRB=False
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)
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age_net.setInput(blob)
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returnage_net.forward()
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defpredict_age_and_gender(input_path:str):
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"""Predict the gender of the faces showing in the image"""
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# Initialize frame size
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# frame_width = 1280
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# frame_height = 720
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# Read Input Image
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img=cv2.imread(input_path)
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# resize the image, uncomment if you want to resize the image
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# img = cv2.resize(img, (frame_width, frame_height))
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# Take a copy of the initial image and resize it
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frame=img.copy()
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ifframe.shape[1]>frame_width:
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frame=image_resize(frame,width=frame_width)
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# predict the faces
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faces=get_faces(frame)
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# Loop over the faces detected
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# for idx, face in enumerate(faces):
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fori, (start_x,start_y,end_x,end_y)inenumerate(faces):
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face_img=frame[start_y:end_y,start_x:end_x]
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age_preds=get_age_predictions(face_img)
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gender_preds=get_gender_predictions(face_img)
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i=gender_preds[0].argmax()
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gender=GENDER_LIST[i]
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gender_confidence_score=gender_preds[0][i]
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i=age_preds[0].argmax()
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age=AGE_INTERVALS[i]
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age_confidence_score=age_preds[0][i]
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# Draw the box
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label=f"{gender}-{gender_confidence_score*100:.1f}%,{age}-{age_confidence_score*100:.1f}%"
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# label = "{}-{:.2f}%".format(gender, gender_confidence_score*100)
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print(label)
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yPos=start_y-15
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whileyPos<15:
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yPos+=15
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box_color= (255,0,0)ifgender=="Male"else (147,20,255)
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cv2.rectangle(frame, (start_x,start_y), (end_x,end_y),box_color,2)
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# Label processed image
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font_scale=0.54
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cv2.putText(frame,label, (start_x,yPos),
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cv2.FONT_HERSHEY_SIMPLEX,font_scale,box_color,2)
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# Display processed image
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display_img("Gender Estimator",frame)
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# uncomment if you want to save the image
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cv2.imwrite("output.jpg",frame)
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# Cleanup
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cv2.destroyAllWindows()
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if__name__=="__main__":
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importsys
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input_path=sys.argv[1]
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predict_age_and_gender(input_path)
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# Import Libraries
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importcv2
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importnumpyasnp
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# The gender model architecture
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# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ
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GENDER_MODEL='weights/deploy_gender.prototxt'
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# The gender model pre-trained weights
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# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHP
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GENDER_PROTO='weights/gender_net.caffemodel'
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# Each Caffe Model impose the shape of the input image also image preprocessing is required like mean
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# substraction to eliminate the effect of illunination changes
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MODEL_MEAN_VALUES= (78.4263377603,87.7689143744,114.895847746)
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# Represent the gender classes
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GENDER_LIST= ['Male','Female']
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# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
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FACE_PROTO="weights/deploy.prototxt.txt"
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# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel
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FACE_MODEL="weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"
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# The model architecture
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# download from: https://drive.google.com/open?id=1kiusFljZc9QfcIYdU2s7xrtWHTraHwmW
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AGE_MODEL='weights/deploy_age.prototxt'
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# The model pre-trained weights
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# download from: https://drive.google.com/open?id=1kWv0AjxGSN0g31OeJa02eBGM0R_jcjIl
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AGE_PROTO='weights/age_net.caffemodel'
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# Represent the 8 age classes of this CNN probability layer
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AGE_INTERVALS= ['(0, 2)','(4, 6)','(8, 12)','(15, 20)',
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'(25, 32)','(38, 43)','(48, 53)','(60, 100)']
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# Initialize frame size
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frame_width=1280
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frame_height=720
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# load face Caffe model
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face_net=cv2.dnn.readNetFromCaffe(FACE_PROTO,FACE_MODEL)
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# Load age prediction model
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age_net=cv2.dnn.readNetFromCaffe(AGE_MODEL,AGE_PROTO)
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# Load gender prediction model
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gender_net=cv2.dnn.readNetFromCaffe(GENDER_MODEL,GENDER_PROTO)
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defget_faces(frame,confidence_threshold=0.5):
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# convert the frame into a blob to be ready for NN input
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blob=cv2.dnn.blobFromImage(frame,1.0, (300,300), (104,177.0,123.0))
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# set the image as input to the NN
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face_net.setInput(blob)
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# perform inference and get predictions
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output=np.squeeze(face_net.forward())
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# initialize the result list
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faces= []
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# Loop over the faces detected
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foriinrange(output.shape[0]):
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confidence=output[i,2]
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ifconfidence>confidence_threshold:
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box=output[i,3:7]* \
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np.array([frame.shape[1],frame.shape[0],
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frame.shape[1],frame.shape[0]])
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# convert to integers
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start_x,start_y,end_x,end_y=box.astype(np.int)
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# widen the box a little
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start_x,start_y,end_x,end_y=start_x- \
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10,start_y-10,end_x+10,end_y+10
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start_x=0ifstart_x<0elsestart_x
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start_y=0ifstart_y<0elsestart_y
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end_x=0ifend_x<0elseend_x
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end_y=0ifend_y<0elseend_y
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# append to our list
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faces.append((start_x,start_y,end_x,end_y))
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returnfaces
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# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv
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defimage_resize(image,width=None,height=None,inter=cv2.INTER_AREA):
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# initialize the dimensions of the image to be resized and
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# grab the image size
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dim=None
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(h,w)=image.shape[:2]
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# if both the width and height are None, then return the
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# original image
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ifwidthisNoneandheightisNone:
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returnimage
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# check to see if the width is None
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ifwidthisNone:
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# calculate the ratio of the height and construct the
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# dimensions
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r=height/float(h)
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dim= (int(w*r),height)
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# otherwise, the height is None
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else:
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# calculate the ratio of the width and construct the
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# dimensions
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r=width/float(w)
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dim= (width,int(h*r))
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# resize the image
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returncv2.resize(image,dim,interpolation=inter)
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defget_gender_predictions(face_img):
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blob=cv2.dnn.blobFromImage(
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image=face_img,scalefactor=1.0,size=(227,227),
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mean=MODEL_MEAN_VALUES,swapRB=False,crop=False
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)
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gender_net.setInput(blob)
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returngender_net.forward()
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defget_age_predictions(face_img):
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blob=cv2.dnn.blobFromImage(
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image=face_img,scalefactor=1.0,size=(227,227),
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mean=MODEL_MEAN_VALUES,swapRB=False
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)
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age_net.setInput(blob)
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returnage_net.forward()
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defpredict_age_and_gender():
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"""Predict the gender of the faces showing in the image"""
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# create a new cam object
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cap=cv2.VideoCapture(0)
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whileTrue:
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_,img=cap.read()
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# Take a copy of the initial image and resize it
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frame=img.copy()
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# resize if higher than frame_width
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ifframe.shape[1]>frame_width:
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frame=image_resize(frame,width=frame_width)
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# predict the faces
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faces=get_faces(frame)
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# Loop over the faces detected
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# for idx, face in enumerate(faces):
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fori, (start_x,start_y,end_x,end_y)inenumerate(faces):
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face_img=frame[start_y:end_y,start_x:end_x]
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# predict age
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age_preds=get_age_predictions(face_img)
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# predict gender
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gender_preds=get_gender_predictions(face_img)
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i=gender_preds[0].argmax()
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gender=GENDER_LIST[i]
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gender_confidence_score=gender_preds[0][i]
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i=age_preds[0].argmax()
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age=AGE_INTERVALS[i]
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age_confidence_score=age_preds[0][i]
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# Draw the box
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label=f"{gender}-{gender_confidence_score*100:.1f}%,{age}-{age_confidence_score*100:.1f}%"
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# label = "{}-{:.2f}%".format(gender, gender_confidence_score*100)
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print(label)
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yPos=start_y-15
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whileyPos<15:
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yPos+=15
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box_color= (255,0,0)ifgender=="Male"else (147,20,255)
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cv2.rectangle(frame, (start_x,start_y), (end_x,end_y),box_color,2)
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# Label processed image
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cv2.putText(frame,label, (start_x,yPos),
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cv2.FONT_HERSHEY_SIMPLEX,0.54,box_color,2)
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# Display processed image
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cv2.imshow("Gender Estimator",frame)
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ifcv2.waitKey(1)==ord("q"):
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break
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# uncomment if you want to save the image
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# cv2.imwrite("output.jpg", frame)
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# Cleanup
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cv2.destroyAllWindows()
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if__name__=="__main__":
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predict_age_and_gender()
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opencv-python
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numpy

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