add gender detection tutorial using opencv

Author: x4nth055

machine-learning/face-gender-detection/README.md

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+# [Gender Detection using OpenCV in Python](https://www.thepythoncode.com/article/gender-detection-using-opencv-in-python)
+Before running the code, do the following:
+- `pip3 install -r requirements.txt`
+- Download [face detection](https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel) and [gender detection](hhttps://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ) models and put them in the `weights` folder. Check [the tutorial](https://www.thepythoncode.com/article/gender-detection-using-opencv-in-python) for more on how to set it up.
+- Run the program:
+    ```
+    python predict_gender.py "images\\Donald Trump.jpg"
+    ```

machine-learning/face-gender-detection/images/Donald Trump.jpg

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+import cv2
+import os
+
+
+factor = 5
+
+
+if not os.path.isdir("new-images"):
+    os.mkdir("new-images")
+
+for file in os.listdir("images"):
+    file = os.path.join("images", file)
+    basename = os.path.basename(file)
+    img = cv2.imread(file)
+    old_size = img.shape
+    new_size = (img.shape[1] // factor, img.shape[0] // factor)
+    print("Old size:", old_size)
+    print("New size:", new_size)
+    img = cv2.resize(img, new_size)
+    new_filename = os.path.join("new-images", basename)
+    cv2.imwrite(new_filename, img)
+    print("Wrote", new_filename)

machine-learning/face-gender-detection/images/jose-ibarra-ifM0755GnS0-unsplash.jpg

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+# Import Libraries
+import cv2
+import numpy as np
+
+
+# The gender model architecture
+# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ
+GENDER_MODEL = 'weights/deploy_gender.prototxt'
+# The gender model pre-trained weights
+# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHP
+GENDER_PROTO = 'weights/gender_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 gender classes
+GENDER_LIST = ['Male', 'Female']
+# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
+FACE_PROTO = "weights/deploy.prototxt.txt"
+# 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"
+
+# load face Caffe model
+face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL)
+# Load gender prediction model
+gender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO)
+
+
+def get_faces(frame, confidence_threshold=0.5):
+    # 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.shape[1], frame.shape[0],
+                         frame.shape[1], frame.shape[0]])
+            # 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 get_optimal_font_scale(text, width):
+    """Determine the optimal font scale based on the hosting frame width"""
+    for scale in reversed(range(0, 60, 1)):
+        textSize = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=scale/10, thickness=1)
+        new_width = textSize[0][0]
+        if (new_width <= width):
+            return scale/10
+    return 1
+
+
+def predict_gender(input_path: str):
+    """Predict the gender of the faces showing in the image"""
+    # Initialize frame size
+    # frame_width = 1280
+    # frame_height = 720
+    # Read Input Image
+    img = cv2.imread(input_path)
+    # resize the image, uncomment if you want to resize the image
+    # img = cv2.resize(img, (frame_width, frame_height))
+    # Take a copy of the initial image and resize it
+    frame = img.copy()
+    # predict the faces
+    faces = get_faces(frame)
+    # Loop over the faces detected
+    # for idx, face in enumerate(faces):
+    for i, (start_x, start_y, end_x, end_y) in enumerate(faces):
+        face_img = frame[start_y: end_y, start_x: end_x]
+        face_img = cv2.rectangle(
+            frame, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)
+        # image --> Input image to preprocess before passing it through our dnn for classification.
+        # scale factor = After performing mean substraction we can optionally scale the image by some factor. (if 1 -> no scaling)
+        # size = The spatial size that the CNN expects. Options are = (224*224, 227*227 or 299*299)
+        # mean = mean substraction values to be substracted from every channel of the image.
+        # swapRB=OpenCV assumes images in BGR whereas the mean is supplied in RGB. To resolve this we set swapRB to True.
+        blob = cv2.dnn.blobFromImage(image=face_img, scalefactor=1.0, size=(
+            227, 227), mean=MODEL_MEAN_VALUES, swapRB=False, crop=False)
+        # Predict Gender
+        gender_net.setInput(blob)
+        gender_preds = gender_net.forward()
+        i = gender_preds[0].argmax()
+        gender = GENDER_LIST[i]
+        gender_confidence_score = gender_preds[0][i]
+        # Draw the box
+        label = "{}-{:.2f}%".format(gender, gender_confidence_score*100)
+        print(label)
+        yPos = start_y - 15
+        while yPos < 15:
+            yPos += 15
+        # get the font scale for this image size
+        optimal_font_scale = get_optimal_font_scale(label,((end_x-start_x)+25))
+        box_color = (255, 0, 0) if gender == "Male" else (147, 20, 255)
+        cv2.rectangle(face_img, (start_x, start_y), (end_x, end_y), box_color, 2)
+        # Label processed image
+        cv2.putText(face_img, label, (start_x, yPos),
+                    cv2.FONT_HERSHEY_SIMPLEX, optimal_font_scale, box_color, 2)
+
+        # Display processed image
+    display_img("Gender Estimator", face_img)
+    # uncomment if you want to save the image
+    cv2.imwrite("output.jpg", face_img)
+    # Cleanup
+    cv2.destroyAllWindows()
+
+
+
+if __name__ == '__main__':
+    # Parsing command line arguments entered by user
+    import sys
+    predict_gender(sys.argv[1])

machine-learning/face-gender-detection/images/pexels-karolina-grabowska-8526635.jpg

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+opencv-python
+numpy