Adding checklist of dependencies, and slimmed down code to download style transfer model.

README.md

@@ -0,0 +1,10 @@
+## Dependencies
+- Tensorflow serving apis
+- Python 3.6+
+- Docker
+- Nvidia-docker
+- tensorflow/serving:latest-gpu image
+- Go
+- gocv
+- tensorflow and tensorflow/serving repositories (for golang protobufs)
+- golang protobuf library

client.go

@@ -0,0 +1,19 @@
+package main
+
+import (
+	"flag"
+	"fmt"
+	"gocv.io/x/gocv"
+	pb "tensorflow_serving/apis"
+)
+
+func main() {
+	request := &pb.PredictRequest{}
+	webcam, _ := gocv.VideoCaptureDevice(0)
+	window := gocv.NewWindow("hello")
+	for {
+		webcam.Read(&img)
+		window.IMShow(img)
+		window.WaitKey(1)
+	}
+}

client.py

@@ -44,18 +44,3 @@ if __name__ == "__main__":
             break
     video.release()
     cv2.destroyAllWindows()
-
-    # styled_plot = plt.imshow(styled_image)
-    # plt.axis('off')
-    # plt.show()
-    #
-    # IMAGES_PATH = '/home/albert/Downloads/sam'
-    # for path in os.listdir(IMAGES_PATH):
-    #     start = time.time()
-    #     file = tf.io.read_file(os.path.join(IMAGES_PATH, path))
-    #     image = tf.io.decode_image(file)
-    #     output = style_transfer(stub, image)
-    #     styled_plot.set_data(output)
-    #     plt.draw()
-    #     end = time.time()
-    #     print(f'Time taken to predict: {end - start}s')

download_model.py

@@ -0,0 +1,7 @@
+import os
+import tensorflow as tf
+import tensorflow_hub as hub
+os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'
+
+hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
+tf.saved_model.save(hub_model, 'style/1')

main.py

@@ -1,68 +0,0 @@
-import os
-import tensorflow as tf
-
-os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'
-import matplotlib.pyplot as plt
-import numpy as np
-import time
-import functools
-import PIL
-
-
-def tensor_to_image(tensor):
-    tensor *= 255
-    tensor = np.array(tensor, dtype=np.uint8)
-    if np.ndim(tensor) > 3:
-        assert tensor.shape[0] == 1
-        tensor = tensor[0]
-    return PIL.Image.fromarray(tensor)
-
-
-content_path = tf.keras.utils.get_file('YellowLabradorLooking_new.jpg',
-                                       'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg')
-style_path = tf.keras.utils.get_file('kandinsky5.jpg',
-                                     'https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg')
-
-
-def load_img(path_to_img):
-    max_dim = 512
-    img = tf.io.read_file(path_to_img)
-    img = tf.image.decode_image(img, channels=3)
-    img = tf.image.convert_image_dtype(img, tf.float32)
-
-    shape = tf.cast(tf.shape(img)[:-1], tf.float32)
-    long_dim = max(shape)
-    scale = max_dim / long_dim
-
-    new_shape = tf.cast(shape * scale, tf.int32)
-
-    img = tf.image.resize(img, new_shape)
-    img = img[tf.newaxis, ...]
-    return img
-
-
-def imshow(image, title=None):
-    if np.ndim(image) > 3:
-        image = tf.squeeze(image, axis=0)
-
-    plt.imshow(image)
-    if title:
-        plt.title(title)
-
-
-content_image = load_img(content_path)
-style_image = load_img(style_path)
-
-plt.subplot(1, 2, 1)
-imshow(content_image, 'Content Image')
-plt.subplot(1, 2, 2)
-imshow(style_image, 'Style Image')
-
-import tensorflow_hub as hub
-
-hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
-stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
-tensor_to_image(stylized_image)
-print(type(hub_model))
-
-tf.saved_model.save(hub_model, 'style')