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waguiiwaguii
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machine-learning/face-gender-detection/images/pexels-karolina-grabowska-8526635.jpg renamed to machine-learning/face-gender-detection/images/2_mulheres.jpg

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

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‎machine-learning/face-gender-detection/predict_gender.py‎

Lines changed: 41 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -116,6 +116,7 @@ def predict_gender(input_path: str):
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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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print(faces)
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# Loop over the faces detected
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# for idx, face in enumerate(faces):
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for i, (start_x, start_y, end_x, end_y) in enumerate(faces):
@@ -154,9 +155,48 @@ def predict_gender(input_path: str):
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# Cleanup
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cv2.destroyAllWindows()
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def predict_gender_simple(input_path: str):
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"""Predict the gender of the faces showing in the image"""
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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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if frame.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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print(faces)
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# Loop over the faces detected
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# for idx, face in enumerate(faces):
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for i, (start_x, start_y, end_x, end_y) in enumerate(faces):
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face_img = frame[start_y: end_y, start_x: end_x]
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# image --> Input image to preprocess before passing it through our dnn for classification.
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# scale factor = After performing mean substraction we can optionally scale the image by some factor. (if 1 -> no scaling)
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# size = The spatial size that the CNN expects. Options are = (224*224, 227*227 or 299*299)
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# mean = mean substraction values to be substracted from every channel of the image.
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# swapRB=OpenCV assumes images in BGR whereas the mean is supplied in RGB. To resolve this we set swapRB to True.
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blob = cv2.dnn.blobFromImage(image=face_img, scalefactor=1.0, size=(
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227, 227), mean=MODEL_MEAN_VALUES, swapRB=False, crop=False)
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# Predict Gender
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gender_net.setInput(blob)
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gender_preds = gender_net.forward()
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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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print(f'gender => {gender}')
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print(f'gender_confidence_score => {gender_confidence_score}')
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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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# Parsing command line arguments entered by user
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import sys
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predict_gender(sys.argv[1])
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predict_gender_simple(sys.argv[1])

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