PS|Add gender and age modularised

app.py

@@ -1,12 +1,14 @@
 import gradio as gr
 from utils.face_texture import GetFaceTexture
 from utils.face_symmetry import GetFaceSymmetry
+from utils.face_demographics import GetFaceDemographics
 
 
 def combined_fn(input_image):
     texture_results = GetFaceTexture().main(input_image)
     symmetry_results = GetFaceSymmetry().main(input_image)
-    return (*texture_results, *symmetry_results)
+    demographics_results = GetFaceDemographics().main(input_image)
+    return (*texture_results, *symmetry_results, demographics_results)
 
 
 iface = gr.Interface(
@@ -15,11 +17,11 @@ iface = gr.Interface(
     outputs=[
         gr.outputs.Image(type="pil"),  # From GetFaceTexture
         gr.outputs.Image(type="pil"),  # From GetFaceTexture
-        "text",                        # From GetFaceTexture
+        "text",  # From GetFaceTexture
         gr.outputs.Image(type="pil"),  # From GetFaceSymmetry
-        "text"                         # From GetFaceSymmetry
-    ]
+        "text",  # From GetFaceSymmetry
+        "text",  # From GetFaceDemographics
+    ],
 )
 
 iface.launch()
-

parameters.yml

@@ -1,3 +1,9 @@
 face_detection:
-  prototxt: "models/face_detection/deploy.prototxt.txt"
+  config: "models/face_detection/deploy.prototxt.txt"
   model: "models/face_detection/res10_300x300_ssd_iter_140000.caffemodel"
+face_age:
+  config: "models/face_age/age_deploy.prototxt"
+  model: "models/face_age/age_net.caffemodel"
+face_gender:
+  config: "models/face_gender/gender_deploy.prototxt"
+  model: "models/face_gender/gender_net.caffemodel"

utils/face_demographics.py

@@ -0,0 +1,63 @@
+import cv2
+import yaml
+import numpy as np
+import os
+from typing import Tuple
+from utils.cv_utils import get_image
+
+
+with open("parameters.yml", "r") as stream:
+    try:
+        parameters = yaml.safe_load(stream)
+    except yaml.YAMLError as exc:
+        print(exc)
+
+
+class GetFaceDemographics:
+    def __init__(self):
+        pass
+    
+    @staticmethod
+    def get_age(blob) -> Tuple:
+        age_net = cv2.dnn.readNet(parameters["face_age"]["config"], parameters["face_age"]["model"])
+        age_list = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
+        age_net.setInput(blob)
+        age_preds = age_net.forward()
+        i = age_preds[0].argmax()
+        age = age_list[i]
+        age_confidence_score = age_preds[0][i]
+        return age, age_confidence_score
+    
+    @staticmethod
+    def get_gender(blob) -> Tuple:
+        gender_net = cv2.dnn.readNet(parameters["face_gender"]["config"], parameters["face_gender"]["model"])
+        gender_list = ['Male', 'Female']
+        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]
+        return gender, gender_confidence_score
+
+    def main(self, image_input) -> dict:
+        image = get_image(image_input)      
+        model_mean = (78.4263377603, 87.7689143744, 114.895847746) # taken from the model page on Caffe
+        blob = cv2.dnn.blobFromImage(image, 1.0, (227, 227), model_mean, swapRB=False)
+        age, age_confidence_score = self.get_age(blob)
+        gender, gender_confidence_score = self.get_gender(blob)
+        d = {
+            "age_range": age,
+            "age_confidence": age_confidence_score,
+            "gender": gender,
+            "gender_confidence": gender_confidence_score 
+        }
+        return d
+
+
+if __name__ == "__main__":
+    path_to_images = "data/"
+    image_files = os.listdir(path_to_images)
+    for image in image_files:
+        print(image)
+        results = GetFaceDemographics().main(path_to_images + image)
+        print(results)

utils/face_symmetry.py

@@ -22,13 +22,13 @@ class GetFaceSymmetry:
     def get_faces(self, image: np.array) -> np.array:
         self.h, self.w = image.shape[:2]
         blob = cv2.dnn.blobFromImage(image=image, scalefactor=1.0, size=(300, 300))
-        net = cv2.dnn.readNetFromCaffe(
-            parameters["face_detection"]["prototxt"],
+        face_detector_net = cv2.dnn.readNetFromCaffe(
+            parameters["face_detection"]["config"],
             parameters["face_detection"]["model"],
         )
-        net.setInput(blob)
-        detections = net.forward()
-        return detections
+        face_detector_net.setInput(blob)
+        face_detections = face_detector_net.forward()
+        return face_detections
 
     @staticmethod
     def postprocess_face(face: np.array) -> np.array:
@@ -105,15 +105,15 @@ class GetFaceSymmetry:
         }
         return d
 
-    def main(self, image_input):
+    def main(self, image_input) -> Tuple:
         image = get_image(image_input)
-        detections = self.get_faces(image)
+        face_detections = self.get_faces(image)
         lowest_mse = float("inf")
         best_face_data, best_left_half, best_right_half = None, None, None
-        for i in range(0, detections.shape[2]):
-            confidence = detections[0, 0, i, 2]
+        for i in range(0, face_detections.shape[2]):
+            confidence = face_detections[0, 0, i, 2]
             if confidence > 0.99:
-                box = detections[0, 0, i, 3:7] * np.array(
+                box = face_detections[0, 0, i, 3:7] * np.array(
                     [self.w, self.h, self.w, self.h]
                 )
                 (startX, startY, endX, endY) = box.astype("int")
@@ -137,6 +137,6 @@ class GetFaceSymmetry:
 
 
 if __name__ == "__main__":
-    image_path = "data/images_symmetry/gigi_hadid.webp"
+    image_path = "data/gigi_hadid.webp"
     results = GetFaceSymmetry().main(image_path)
     print(results)

utils/face_texture.py

@@ -61,5 +61,5 @@ class GetFaceTexture:
 
 
 if __name__ == "__main__":
-    image_path = "data/images_symmetry/gigi_hadid.webp"
+    image_path = "data/gigi_hadid.webp"
     print(GetFaceTexture().main(image_path))