holiday similarity update

1 files changed, 65 insertions, 0 deletions

diff --git a/cv/holiday_similarity/generate_image_features.py b/cv/holiday_similarity/generate_image_features.py
new file mode 100644
index 0000000..6c26995
--- /dev/null
+++ b/cv/holiday_similarity/generate_image_features.py
@@ -0,0 +1,65 @@
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+from keras.applications import imagenet_utils
+from keras.applications import resnet50
+from keras.models import Model
+
+IMAGE_DIR = '/Users/chunhuizhang/workspaces/00_datasets/images/INRIA Holidays dataset /jpg'
+DATA_DIR = './data'
+
+
+def image_batch_generator(image_names, batch_size):
+    num_batches = len(image_names) // batch_size
+    for i in range(num_batches):
+        batch = image_names[i * batch_size: (i + 1) * batch_size]
+        yield batch
+    batch = image_names[(i + 1) * batch_size:]
+    yield batch
+
+
+def vectorize_images(image_dir, image_size, preprocessor,
+                     model, vector_file, batch_size=32):
+    image_names = os.listdir(image_dir)
+    num_vecs = 0
+    fvec = open(vector_file, "w")
+    for image_batch in image_batch_generator(image_names, batch_size):
+        batched_images = []
+        for image_name in image_batch:
+            image = plt.imread(os.path.join(image_dir, image_name))
+            # image = imresize(image, (image_size, image_size))
+            image = np.asarray(Image.fromarray(image).resize((image_size, image_size)))
+            batched_images.append(image)
+        X = preprocessor(np.array(batched_images, dtype="float32"))
+        vectors = model.predict(X)
+        for i in range(vectors.shape[0]):
+            if num_vecs % 100 == 0:
+                print("{:d}/{:d} vectors generated".format(num_vecs, vectors.shape[0]))
+            image_vector = ",".join(["{:.5e}".format(v) for v in vectors[i].tolist()])
+            # print(image_batch[i], image_vector)
+            # print(type(image_batch[i]), type(image_vector))
+            fvec.write("{:s}\t{:s}\n".format(image_batch[i], image_vector))
+            num_vecs += 1
+    print("{:d} vectors generated".format(num_vecs))
+    fvec.close()
+
+
+def generate_features(model, image_size, vector_file):
+    model = Model(input=model.input,
+                  output=model.get_layer("avg_pool").output)
+    preprocessor = imagenet_utils.preprocess_input
+
+    vectorize_images(IMAGE_DIR, image_size, preprocessor, model, vector_file)
+
+
+if __name__ == '__main__':
+    IMAGE_SIZE = 224
+    # vgg16_model = vgg16.VGG16(weights="imagenet", include_top=True)
+    # VECTOR_FILE = os.path.join(DATA_DIR, "vgg19-vectors.tsv")
+    # generate_features(vgg16_model, IMAGE_SIZE, VECTOR_FILE)
+
+    VECTOR_FILE = os.path.join(DATA_DIR, "resnet-vectors.tsv")
+    resnet_model = resnet50.ResNet50(weights="imagenet", include_top=True)
+    generate_features(resnet_model, IMAGE_SIZE, VECTOR_FILE)