add age detection using opencv tutorial

Author: x4nth055

Diff for: README.md

@@ -60,6 +60,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [Image Transformations using OpenCV in Python](https://www.thepythoncode.com/article/image-transformations-using-opencv-in-python). ([code](machine-learning/image-transformation))
         - [How to Apply HOG Feature Extraction in Python](https://www.thepythoncode.com/article/hog-feature-extraction-in-python). ([code](machine-learning/hog-feature-extraction))
         - [SIFT Feature Extraction using OpenCV in Python](https://www.thepythoncode.com/article/sift-feature-extraction-using-opencv-in-python). ([code](machine-learning/sift))
+        - [Age Prediction using OpenCV in Python](https://www.thepythoncode.com/article/predict-age-using-opencv). ([code](machine-learning/face-age-prediction))
     - [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))
     - [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))
     - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).

Diff for: machine-learning/face-age-prediction/3-people.jpg

@@ -0,0 +1,8 @@
+# [Age Prediction using OpenCV in Python](https://www.thepythoncode.com/article/predict-age-using-opencv)
+Before running the code, do the following:
+- `pip3 install -r requirements.txt`
+- Download [face detection] and [age detection] models and put them in the `weights` folder.
+- Run the program:
+    ```
+    python predict_age.py 3-people.jpg
+    ```

Diff for: machine-learning/face-age-prediction/README.md

@@ -0,0 +1,121 @@
+# Import Libraries
+import cv2
+import os
+import filetype
+import numpy as np
+
+# The model architecture
+# download from: https://drive.google.com/open?id=1kiusFljZc9QfcIYdU2s7xrtWHTraHwmW
+AGE_MODEL = 'weights/deploy_age.prototxt'
+# The model pre-trained weights
+# download from: https://drive.google.com/open?id=1kWv0AjxGSN0g31OeJa02eBGM0R_jcjIl
+AGE_PROTO = 'weights/age_net.caffemodel'
+# Each Caffe Model impose the shape of the input image also image preprocessing is required like mean
+# substraction to eliminate the effect of illunination changes
+MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
+# Represent the 8 age classes of this CNN probability layer
+AGE_INTERVALS = ['(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)',
+                 '(25, 32)', '(38, 43)', '(48, 53)', '(60, 100)']
+# download from: https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
+FACE_PROTO = "weights/deploy.prototxt.txt"
+# download from: https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel
+FACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"
+
+# Initialize frame size
+frame_width = 1280
+frame_height = 720
+
+# load face Caffe model
+face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL)
+# Load age prediction model
+age_net = cv2.dnn.readNetFromCaffe(AGE_MODEL, AGE_PROTO)
+
+
+def get_faces(frame, confidence_threshold=0.5):
+    """Returns the box coordinates of all detected faces"""
+    # convert the frame into a blob to be ready for NN input
+    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104, 177.0, 123.0))
+    # set the image as input to the NN
+    face_net.setInput(blob)
+    # perform inference and get predictions
+    output = np.squeeze(face_net.forward())
+    # initialize the result list
+    faces = []
+    # Loop over the faces detected
+    for i in range(output.shape[0]):
+        confidence = output[i, 2]
+        if confidence > confidence_threshold:
+            box = output[i, 3:7] * np.array([frame_width, frame_height, frame_width, frame_height])
+            # convert to integers
+            start_x, start_y, end_x, end_y = box.astype(np.int)
+            # widen the box a little
+            start_x, start_y, end_x, end_y = start_x - \
+                10, start_y - 10, end_x + 10, end_y + 10
+            start_x = 0 if start_x < 0 else start_x
+            start_y = 0 if start_y < 0 else start_y
+            end_x = 0 if end_x < 0 else end_x
+            end_y = 0 if end_y < 0 else end_y
+            # append to our list
+            faces.append((start_x, start_y, end_x, end_y))
+    return faces
+
+
+def display_img(title, img):
+    """Displays an image on screen and maintains the output until the user presses a key"""
+    # Display Image on screen
+    cv2.imshow(title, img)
+    # Mantain output until user presses a key
+    cv2.waitKey(0)
+    # Destroy windows when user presses a key
+    cv2.destroyAllWindows()
+
+
+def predict_age(input_path: str):
+    """Predict the age of the faces showing in the image"""
+    # Read Input Image
+    img = cv2.imread(input_path)
+    # resize the image
+    img = cv2.resize(img, (frame_width, frame_height))
+    # Take a copy of the initial image and resize it
+    frame = img.copy()
+    faces = get_faces(frame)
+    for i, (start_x, start_y, end_x, end_y) in enumerate(faces):
+            face_img = frame[start_y: end_y, start_x: end_x]
+            # image --> Input image to preprocess before passing it through our dnn for classification.
+            blob = cv2.dnn.blobFromImage(
+                image=face_img, scalefactor=1.0, size=(227, 227), 
+                mean=MODEL_MEAN_VALUES, swapRB=False
+            )
+            # Predict Age
+            age_net.setInput(blob)
+            age_preds = age_net.forward()
+            print("="*30, f"Face {i+1} Prediction Probabilities", "="*30)
+            for i in range(age_preds[0].shape[0]):
+                print(f"{AGE_INTERVALS[i]}: {age_preds[0, i]*100:.2f}%")
+            i = age_preds[0].argmax()
+            age = AGE_INTERVALS[i]
+            age_confidence_score = age_preds[0][i]
+            # Draw the box
+            label = f"Age:{age} - {age_confidence_score*100:.2f}%"
+            print(label)
+            # get the position where to put the text
+            yPos = start_y - 15
+            while yPos < 15:
+                yPos += 15
+            # write the text into the frame
+            cv2.putText(frame, label, (start_x, yPos),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), thickness=2)
+            # draw the rectangle around the face
+            cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2)
+    # Display processed image
+    display_img('Age Estimator', frame)
+    # save the image if you want
+    # cv2.imwrite("predicted_age.jpg", frame)
+
+
+
+if __name__ == '__main__':
+    # Parsing command line arguments entered by user
+    import sys
+    image_path = sys.argv[1]
+    predict_age(image_path)

Diff for: machine-learning/face-age-prediction/predict_age.py

@@ -0,0 +1,2 @@
+opencv-python
+numpy