Initial commit of project

predictors/predict_gender.py

@@ -0,0 +1,72 @@
+import os
+
+import cv2
+import numpy as np
+from keras.models import load_model
+from PIL import Image
+import config
+
+gender_classifier = load_model(config.GENDER_MODEL_PATH, compile=False)
+
+# https://github.com/keras-team/keras/issues/6462
+gender_classifier._make_predict_function()
+
+gender_target_size = gender_classifier.input_shape[1:3]
+gender_offsets = (10, 10)
+
+
+def detect_genders(faceBoundary, image):
+    result = ""
+    face = get_boundary_box(faceBoundary, np.shape(image))
+    x1, x2, y1, y2 = apply_offsets(face, gender_offsets)
+    rgb_face = image[y1:y2, x1:x2]
+
+    try:
+        rgb_face = cv2.resize(rgb_face, gender_target_size)
+    except Exception as e:
+        print('Error while resizing the image', e)
+        return result
+
+    rgb_face = pre_process_input(rgb_face)
+    rgb_face = np.expand_dims(rgb_face, 0)
+    gender_prediction = gender_classifier.predict(rgb_face)
+    result = {
+        'woman': float(gender_prediction[0][0]),
+        'man': float(gender_prediction[0][1])
+    }
+
+    return result
+
+
+def apply_offsets(face_coordinates, offsets):
+    x, y, width, height = face_coordinates
+    x_off, y_off = offsets
+    return max(x - x_off, 0), x + width + x_off, max(y - y_off, 0), y + height + y_off
+
+
+def pre_process_input(x, v2=False):
+    x = x.astype('float32')
+    x = x / 255.0
+    if v2:
+        x = x - 0.5
+        x = x * 2.0
+    return x
+
+
+def load_image(imageFilename):
+    try:
+        img = Image.open(imageFilename)
+        img = img.convert('RGB')
+        return np.array(img)
+    except Exception as e:
+        print("Not a valid image found on {0}: {1}".format(imageFilename, e))
+        return None
+
+
+def get_boundary_box(boundary, image_shape):
+    X = max(boundary[3], 0)
+    Y = max(boundary[0], 0)
+    width = min(abs(X - boundary[1]), image_shape[0])
+    height = min(abs(Y - boundary[2]), image_shape[1])
+    return [X, Y, width, height]
+