Initial commit of project

predictors/predict_bmi.py

@@ -0,0 +1,78 @@
+import cv2
+import dlib
+import numpy as np
+import tensorflow as tf
+from tensorflow.python.keras.models import Model
+from tensorflow.python.keras.applications import ResNet50
+from tensorflow.python.keras.layers import Dense
+
+import config
+
+
+def get_age_model():
+    age_model = ResNet50(
+        include_top=False,
+        weights='imagenet',
+        input_shape=(config.RESNET50_DEFAULT_IMG_WIDTH, config.RESNET50_DEFAULT_IMG_WIDTH, 3),
+        pooling='avg'
+    )
+
+    prediction = Dense(units=101,
+                       kernel_initializer='he_normal',
+                       use_bias=False,
+                       activation='softmax',
+                       name='pred_age')(age_model.output)
+
+    age_model = Model(inputs=age_model.input, outputs=prediction)
+    return age_model
+
+
+def get_model(ignore_age_weights=False):
+    base_model = get_age_model()
+    if not ignore_age_weights:
+        base_model.load_weights(config.AGE_TRAINED_WEIGHTS_FILE)
+        print('Loaded weights from age classifier')
+    last_hidden_layer = base_model.get_layer(index=-2)
+
+    base_model = Model(
+        inputs=base_model.input,
+        outputs=last_hidden_layer.output)
+    prediction = Dense(1, kernel_initializer='normal')(base_model.output)
+
+    model = Model(inputs=base_model.input, outputs=prediction)
+    return model
+
+def get_trained_model():
+    weights_file = config.MODEL_WEIGHTS_PATH
+    _model = get_model(ignore_age_weights=True)
+    _model.load_weights(weights_file)
+    return _model
+
+model = get_trained_model()
+#detector = dlib.get_frontal_face_detector()
+
+graph = tf.get_default_graph()
+
+def predict_bmi(detected_faces, img):
+    input_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img_h, img_w, _ = np.shape(input_img)
+
+    faces = np.empty((len(detected_faces), config.RESNET50_DEFAULT_IMG_WIDTH,
+                      config.RESNET50_DEFAULT_IMG_WIDTH, 3))
+
+
+    for i, d in enumerate(detected_faces):
+        x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, \
+                               d.bottom() + 1, d.width(), d.height()
+        xw1 = max(int(x1 - config.MARGIN * w), 0)
+        yw1 = max(int(y1 - config.MARGIN * h), 0)
+        xw2 = min(int(x2 + config.MARGIN * w), img_w - 1)
+        yw2 = min(int(y2 + config.MARGIN * h), img_h - 1)
+
+        faces[i, :, :, :] = cv2.resize(img[yw1:yw2 + 1, xw1:xw2 + 1, :], (
+            config.RESNET50_DEFAULT_IMG_WIDTH,
+            config.RESNET50_DEFAULT_IMG_WIDTH)) / 255.00
+
+        with graph.as_default():
+            predictions = model.predict(faces)
+            return predictions