Upload images and bboxes to s3. Refactor. (#5)

* Upload images and bboxes to s3. Refactor.

* Remove comment.

.gitignore

@@ -150,3 +150,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+# config with secrets
+config.toml

README.md

@@ -4,14 +4,8 @@
 steps:
 * use git lfs for the model +
 * write better code +
-* convert model to onnx or some other format?
-* deploy bare working app, without nice things
+* convert model to onnx or some other format?. not needed
+* deploy bare working app, without nice things +
+* save images to amazon+
 * make better design
 * think about descriptions on the site
-
-On using git lfs:
-```shell
-git lfs install
-git lfs track "*.psd"
-git add .gitattributes
-```

config.toml

@@ -1,8 +0,0 @@
-# Everything in this section will be available as an environment variable
-db_username = "Jane"
-db_password = "12345qwerty"
-
-AWS_ACCESS_KEY_ID = 'AKIAI4JDKBYRCHGT77VQ'
-AWS_SECRET_ACCESS_KEY = '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'
-S3_BUCKET = 'digitdrawdetect'
-S3_BUCKET_NAME = 'digitdrawdetect'

src/utils.py

@@ -1,10 +1,16 @@
-from typing import List, Dict
+import datetime
+import json
+import uuid
+from typing import List
 
+import boto3
 import matplotlib
 import matplotlib.patches as patches
 import matplotlib.pyplot as plt
 import numpy.typing as npt
-import tomli as tomllib
+import streamlit as st
+
+client = boto3.client('s3')
 
 
 def plot_img_with_rects(
@@ -48,14 +54,35 @@ def plot_img_with_rects(
     return fig
 
 
-def get_config() -> Dict:
+def save_object_to_s3(filename, s3_filename):
+    client.upload_file(filename, 'digitdrawdetect', s3_filename)
+
+
+@st.cache(show_spinner=False)
+def save_image(image: npt.ArrayLike, pred: List[List]) -> str:
     """
-    Get dict from config.
+    Save the image and upload the image with bboxes to s3.
+
+    Args:
+        image: np.array with image
+        pred: bboxes
 
     Returns:
-        config
+        image name
+
     """
-    with open('config.toml', 'rb') as f:
-        config = tomllib.load(f)
+    # create a figure and save it
+    fig, ax = plt.subplots(1, figsize=(4, 4))
+    ax.imshow(image)
+    file_name = str(datetime.datetime.today().date()) + str(uuid.uuid1())
+    fig.savefig(f'{file_name}.png')
+
+    # dump bboxes in a local file
+    with open(f'{file_name}.json', 'w') as f:
+        json.dump({f'{file_name}.png': pred}, f)
+
+    # upload the image and the bboxes to s3.
+    save_object_to_s3(f'{file_name}.png', f'images/{file_name}.png')
+    save_object_to_s3(f'{file_name}.json', f'labels/{file_name}.json')
 
-    return config
+    return file_name

st_app.py

@@ -6,7 +6,7 @@ from PIL import Image
 from streamlit_drawable_canvas import st_canvas
 
 from src.ml_utils import predict, get_model, transforms
-from src.utils import plot_img_with_rects, get_config
+from src.utils import plot_img_with_rects, save_image
 
 logging.info('Starting')
 
@@ -26,7 +26,6 @@ with col1:
         key='canvas',
     )
 with col2:
-    data = get_config()
     logging.info('canvas ready')
     if canvas_result.image_data is not None:
         # convert a drawn image into numpy array with RGB from a canvas image with RGBA
@@ -37,9 +36,11 @@ with col2:
         logging.info('model ready')
         pred = predict(model, image)
         logging.info('prediction done')
+
+        file_name = save_image(image.permute(1, 2, 0).numpy(), pred)
         threshold = st.slider('Bbox probability slider', min_value=0.0, max_value=1.0, value=0.5)
 
         fig = plot_img_with_rects(image.permute(1, 2, 0).numpy(), pred, threshold, coef=192)
-        fig.savefig('figure_name1.png')
-        image = Image.open('figure_name1.png')
+        fig.savefig(f'{file_name}_temp.png')
+        image = Image.open(f'{file_name}_temp.png')
         st.image(image)