Add a demo script to make predictions on a single sample.

data.py

@@ -206,6 +206,33 @@ class DataSet():
         else:
             return None
 
+    def get_frames_by_filename(self, filename, data_type):
+        """Given a filename for one of our samples, return the data
+        the model needs to make predictions."""
+        # First, find the sample row.
+        sample = None
+        for row in self.data:
+            if row[2] == filename:
+                sample = row
+                break
+        if sample is None:
+            raise ValueError("Couldn't find sample: %s" % filename)
+
+        if data_type == "images":
+            # Get and resample frames.
+            frames = self.get_frames_for_sample(sample)
+            frames = self.rescale_list(frames, self.seq_length)
+            # Build the image sequence
+            sequence = self.build_image_sequence(frames)
+        else:
+            # Get the sequence from disk.
+            sequence = self.get_extracted_sequence(data_type, sample)
+
+            if sequence is None:
+                raise ValueError("Can't find sequence. Did you generate them?")
+
+        return sequence
+
     @staticmethod
     def get_frames_for_sample(sample):
         """Given a sample row from the data file, get all the corresponding frame
@@ -236,12 +263,11 @@ class DataSet():
         # Cut off the last one if needed.
         return output[:size]
 
-    @staticmethod
-    def print_class_from_prediction(predictions, nb_to_return=5):
+    def print_class_from_prediction(self, predictions, nb_to_return=5):
         """Given a prediction, print the top classes."""
         # Get the prediction for each label.
         label_predictions = {}
-        for i, label in enumerate(data.classes):
+        for i, label in enumerate(self.classes):
             label_predictions[label] = predictions[i]
 
         # Now sort them.

demo.py

+"""
+Given a video path and a saved model (checkpoint), produce classification
+predictions.
+
+Note that if using a model that requires features to be extracted, those
+features must be extracted first.
+
+Note also that this is a rushed demo script to help a few people who have
+requested it and so is quite "rough". :)
+"""
+from keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping, CSVLogger
+from keras.models import load_model
+from models import ResearchModels
+from data import DataSet
+import time
+import os.path
+import numpy as np
+
+def predict(data_type, seq_length, saved_model, image_shape, video_name, class_limit):
+    model = load_model(saved_model)
+
+    # Get the data and process it.
+    if image_shape is None:
+        data = DataSet(seq_length=seq_length, class_limit=class_limit)
+    else:
+        data = DataSet(seq_length=seq_length, image_shape=image_shape,
+            class_limit=class_limit)
+    
+    # Extract the sample from the data.
+    sample = data.get_frames_by_filename(video_name, data_type)
+
+    # Predict!
+    prediction = model.predict(np.expand_dims(sample, axis=0))
+    print(prediction)
+    print(data.print_class_from_prediction(np.squeeze(prediction, axis=0)))
+
+def main():
+    # model can be one of lstm, lrcn, mlp, conv_3d, c3d.
+    model = 'lstm'
+    # Must be a weights file.
+    saved_model = 'data/checkpoints/lstm-features.026-0.239.hdf5'
+    # Sequence length must match the lengh used during training.
+    seq_length = 40
+    # Limit must match that used during training.
+    class_limit = 4
+
+    # Demo file. Must already be extracted & features generated (if model requires)
+    # Do not include the extension.
+    # Assumes it's in data/[train|test]/
+    # It also must be part of the train/test data.
+    # TODO Make this way more useful. It should take in the path to
+    # an actual video file, extract frames, generate sequences, etc.
+    #video_name = 'v_Archery_g04_c02'
+    video_name = 'v_ApplyLipstick_g01_c01'
+
+    # Chose images or features and image shape based on network.
+    if model in ['conv_3d', 'c3d', 'lrcn']:
+        data_type = 'images'
+        image_shape = (80, 80, 3)
+    elif model in ['lstm', 'mlp']:
+        data_type = 'features'
+        image_shape = None
+    else:
+        raise ValueError("Invalid model. See train.py for options.")
+
+    predict(data_type, seq_length, saved_model, image_shape, video_name, class_limit)
+
+if __name__ == '__main__':
+    main()