Code for Gender Detection using OpenCV in Python Tutorial

View on Github

predict_gender.py

# 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) # Initialize frame size frame_width = 1280 frame_height = 720 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 # from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA): # initialize the dimensions of the image to be resized and # grab the image size dim = None (h, w) = image.shape[:2] # if both the width and height are None, then return the # original image if width is None and height is None: return image # check to see if the width is None if width is None: # calculate the ratio of the height and construct the # dimensions r = height / float(h) dim = (int(w * r), height) # otherwise, the height is None else: # calculate the ratio of the width and construct the # dimensions r = width / float(w) dim = (width, int(h * r)) # resize the image return cv2.resize(image, dim, interpolation = inter) def predict_gender(input_path: str): """Predict the gender of the faces showing in the image""" # 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() if frame.shape[1] > frame_width: frame = image_resize(frame, width=frame_width) # 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] # 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(frame, (start_x, start_y), (end_x, end_y), box_color, 2) # Label processed image cv2.putText(frame, label, (start_x, yPos), cv2.FONT_HERSHEY_SIMPLEX, optimal_font_scale, box_color, 2) # Display processed image display_img("Gender Estimator", frame) # uncomment if you want to save the image cv2.imwrite("output.jpg", frame) # Cleanup cv2.destroyAllWindows() if __name__ == '__main__': # Parsing command line arguments entered by user import sys predict_gender(sys.argv[1])

predict_gender_live.py

# 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) # Initialize frame size frame_width = 1280 frame_height = 720 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 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 # from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA): # initialize the dimensions of the image to be resized and # grab the image size dim = None (h, w) = image.shape[:2] # if both the width and height are None, then return the # original image if width is None and height is None: return image # check to see if the width is None if width is None: # calculate the ratio of the height and construct the # dimensions r = height / float(h) dim = (int(w * r), height) # otherwise, the height is None else: # calculate the ratio of the width and construct the # dimensions r = width / float(w) dim = (width, int(h * r)) # resize the image return cv2.resize(image, dim, interpolation = inter) def predict_gender(): """Predict the gender of the faces showing in the image""" # create a new cam object cap = cv2.VideoCapture(0) while True: _, img = cap.read() # 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() if frame.shape[1] > frame_width: frame = image_resize(frame, width=frame_width) # 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] # 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(frame, (start_x, start_y), (end_x, end_y), box_color, 2) # Label processed image cv2.putText(frame, label, (start_x, yPos), cv2.FONT_HERSHEY_SIMPLEX, optimal_font_scale, box_color, 2) # Display processed image # frame = cv2.resize(frame, (frame_height, frame_width)) cv2.imshow("Gender Estimator", frame) if cv2.waitKey(1) == ord("q"): break # uncomment if you want to save the image # cv2.imwrite("output.jpg", frame) # Cleanup cv2.destroyAllWindows() if __name__ == '__main__': predict_gender()