Kali ini kita belajar mendeteksi umur menggunakan OpenCV Python. Sebelumnya untuk teman-teman yang belum belajar cara memblurkan wajah bisa lihat di link berikut : Cara Memblurkan Wajah Menggunakan OpenCV Python.
Adapun langkah-langkahnya adalah sebagai berikut :
- Buat folder dan beri nama “age-detection”.
- Siapkan gambar yang akan dideteksi umurnya.
- Kemudian download file model age_detector dan face_detector (Download).
- Ekstrak model tersebut di folder “age-detection”.
- Buka IDLE Python 3.7.
- Klik file->new file.
- Ketikan kode berikut ini:
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# USAGE # python detect_age.py --image images/adrian.png --face face_detector --age age_detector # import the necessary packages import numpy as np import cv2 import os # construct the argument parse and parse the arguments # define the list of age buckets our age detector will predict AGE_BUCKETS = ["(0-2)", "(4-6)", "(8-12)", "(15-20)", "(25-32)", "(38-43)", "(48-53)", "(60-100)"] # memuat model pendeteksi wajah prototxtPath = ("face_detector/deploy.prototxt") weightsPath = ( "face_detector/res10_300x300_ssd_iter_140000.caffemodel") faceNet = cv2.dnn.readNet(prototxtPath, weightsPath) prototxtPath = ("age_detector/age_deploy.prototxt") weightsPath = ("age_detector/age_net.caffemodel") ageNet = cv2.dnn.readNet(prototxtPath, weightsPath) # load the input image and construct an input blob for the image image = cv2.imread("foto.jpg") (h, w) = image.shape[:2] blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0)) # pass the blob through the network and obtain the face detections print("[INFO] computing face detections...") faceNet.setInput(blob) detections = faceNet.forward() # loop over the detections for i in range(0, detections.shape[2]): # extract the confidence (i.e., probability) associated with the # prediction confidence = detections[0, 0, i, 2] # filter out weak detections by ensuring the confidence is # greater than the minimum confidence if confidence > 0.5: # compute the (x, y)-coordinates of the bounding box for the # object box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # extract the ROI of the face and then construct a blob from # *only* the face ROI face = image[startY:endY, startX:endX] faceBlob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), (78.4263377603, 87.7689143744, 114.895847746), swapRB=False) # make predictions on the age and find the age bucket with # the largest corresponding probability ageNet.setInput(faceBlob) preds = ageNet.forward() i = preds[0].argmax() age = AGE_BUCKETS[i] ageConfidence = preds[0][i] # display the predicted age to our terminal text = "{}: {:.2f}%".format(age, ageConfidence * 100) print("[INFO] {}".format(text)) # draw the bounding box of the face along with the associated # predicted age y = startY - 10 if startY - 10 > 10 else startY + 10 cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2) cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2) # display the output image cv2.imshow("Image", image) cv2.waitKey(0) |
- Simpan difolder “age-detection”.
- Habis itu jalankan.
Hasilnya akan terlihat seperti pada gambar dan video diatas. Jika terdapat error teman-teman bisa berkomentar dibawah.