Merge master

.github/pull_request_template.md

@@ -10,4 +10,5 @@ Pull Request Checklist
 Maintainer's responsibilities:
 - [ ] `_version.py` has been updated.
 - [ ] `CHANGELOG.md` has been updated.
-- [ ] Updated app container has been pushed, if relevant.
+- [ ] Updated app container has been pushed, if relevant, with current version number.
+- [ ] App container version number has been updated in README.

CHANGELOG.md

@@ -3,10 +3,17 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
-# [1.2.2] - 2019-8-22
+# [1.2.3] - 2019-8-22
 ### Changed
  - Tests and instructions now refer to "localhost" instead of "0.0.0.0".
 
+# [1.2.2] - 2019-8-19
+### Fixed
+ - App can actually accept image files with capitalized extensions (e.g. ".JPG"").
+### Added
+ - Explicit version numbers on dockerhub containers.
+
+
 # [1.2.1] - 2019-8-3
 ### Fixed
  - App can accept image files with capitalized extensions (e.g. ".JPG").

README.md

@@ -16,9 +16,24 @@ This project uses deep learning computer vision to label images taken by motion-
 If you just want to get labels for your images, you can use the following steps to run a service that passes images through a trained model.
 
 1. Make sure [Docker](https://www.docker.com/get-started) is installed and running.
-2. Run `docker pull gsganden/autofocus_serve` to download the app image. (Note that it takes up about 4GB of disk space.)
-3. Run `docker run -p 8000:8000 gsganden/autofocus_serve` to start the app.
-4. Make POST requests against the app to get predictions. For instance, with the base of this repo as the working directory you can send the image `fawn.jpeg` to the app with `curl -F "file=@./gallery/fawn.jpeg" -X POST http://localhost:8000/predict`. Or you can create a zip file of the `gallery` directory and send it to the app with `curl -F "file=@gallery.zip" -X POST http://localhost:8000/predict_zip`. See `autofocus/predict/example_post.py` or `autofocus/predict/example_post.R` for example scripts that make requests using Python and R, respectively.
+2. Run `docker pull gsganden/autofocus_serve:1.2.2` to download the app image. (Note that it takes up about 4GB of disk space.)
+3. Run `docker run -p 8000:8000 gsganden/autofocus_serve:1.2.2` to start the app.
+4. Make POST requests against the app to get predictions.
+
+For instance, with the base of this repo as the working directory you can send the image `fawn.JPG` to the app with this command:
+
+```bash
+curl -F "file=@./gallery/fawn.JPG" -X POST http://localhost:8000/predict
+```
+
+Or send the zipped `gallery` directory to the app with this command:
+
+```bash
+curl -F "file=@gallery.zip" -X POST http://localhost:8000/predict_zip
+```
+
+See `autofocus/predict/example_post.py` and `autofocus/predict/example_post.R` for example scripts that make requests using Python and R, respectively.
+>>>>>>> master
 
 For a single image, the app will respond with a JSON object that indicates the model's probability that the image contains an animal in each of the categories that it has been trained on. For instance, it might give the following response for an image containing raccoons:
 
@@ -48,6 +63,8 @@ For a single image, the app will respond with a JSON object that indicates the m
 }
 ```
 
+The model generates each of these probabilities separately to allow for the possibility e.g. that an image contains both a human and a dog, so they will not sum to 1 in general.
+
 The `/predict_zip` endpoint returns a JSON object mapping file paths to model probabilities formatted as above.
 
 During development, it is convenient to run the app in debug mode with the local directory mounted to the Docker container so that changes you make locally are reflected in the service immediately:
@@ -116,6 +133,6 @@ To test the app, run `pip install -r requirements-dev.txt` and then `pytest`. Th
 
 ![buck](./gallery/buck.jpeg)
 
-![fawn](./gallery/fawn.jpeg)
+![fawn](./gallery/fawn.JPG)
 
 ![racoons](gallery/raccoons.jpeg)

autofocus/predict/app/app.py

@@ -5,8 +5,8 @@
 from flask import Flask, jsonify, make_response, request
 from werkzeug import secure_filename
 
-from model import predict_multiple, predict_single
-from utils import allowed_file, filter_image_files, list_zip_files
+from .model import predict_multiple, predict_single
+from .utils import allowed_file, filter_image_files, list_zip_files
 
 # We are going to upload the files to the server as part of the request, so set tmp folder here.
 UPLOAD_FOLDER = "/tmp/"

tests/test_app.py

@@ -6,11 +6,11 @@
 
 
 def test_sample_predict_request():
-    filepath = Path(__file__).resolve().parents[1] / "gallery" / "fawn.jpeg"
+    filepath = Path(__file__).resolve().parents[1] / "gallery" / "raccoons.jpeg"
     response = requests.post(
         f"{BASE_URL}/predict", files={"file": open(filepath, "rb")}
     )
-    assert response.json()["deer"] > 0.9
+    assert response.json()["raccoon"] > 0.9
 
 
 def test_sample_predict_request_JPG():
@@ -26,4 +26,4 @@ def test_sample_predict_zip_request():
     response = requests.post(
         f"{BASE_URL}/predict_zip", files={"file": open(filepath, "rb")}
     )
-    assert len(response.json()) == 5
+    assert len(response.json()) == 4