edited documentation

Author: patrick-5546

README.md

@@ -10,7 +10,7 @@ The process of organizing photos can be a huge pain. And when they aren't organi
 
 2. **Name Tags:** identifies people in an image and tags the image with their names
 
-3. **Rotation Correction:** detects crooked images and straightens them out
+3. **Rotation Correction:** detects crooked images and straightens them
 
 4. **Low-Light Enhancement:** detects dark images and brightens them
      - *Beta feature:* may output images with bright neon spots
@@ -21,31 +21,28 @@ The process of organizing photos can be a huge pain. And when they aren't organi
 
 ## Installation
 
-1. Ensure that [Python](https://www.python.org/downloads/)>=3.9 (with pip) is installed and [Git](https://git-scm.com/downloads) if you want to clone this repository
+1. Ensure that [Python](https://www.python.org/downloads/)>=3.9 (with pip) is installed
 
 2. Download the application
 
     - To get only the files required to run the application, download and unzip a [Release](https://github.com/patrick-5546/ML-Image-Processor/releases)
-    - To clone the repository (much larger, includes reports, datasets, notebooks, etc.):
+    - To also get the reports, datasets, notebooks, etc., clone the repository (much larger folder):
 
-        ```sh
-        git clone https://github.com/patrick-5546/ML-Image-Processor
-        ```
-
-3. Create a virtual environment to run the application in (optional)
+3. Create a virtual environment to run the application in (optional, but recommended)
     1. Navigate to the project directory
     2. See [Python Docs](https://docs.python.org/3/tutorial/venv.html#creating-virtual-environments)
          - For the first command, use `venv` as the directory name instead of `tutorial-env`
          - If using PowerShell, run the `Activate.ps1` script instead
-    - Ensures that the correct packages and versions necessary to run the application are used without adding to or replacing packages in the main python package director
+    - This ensures that the packages necessary to run the application are used without adding to or replacing packages in the main python package directory
 
-4. In the project directory, install its requirements
+4. In the project directory, install the required packages
 
     ```sh
     pip install -r requirements.txt
     ```
 
 ## How to Run
 
-1. Run `python main.py` to start application and open <http://127.0.0.1:8080/> on your browser
-2. Application usage instructions can be found on the homepage, or a visual walkthrough in the [final report](/reports/final/Final%20Report.pdf)
+1. Run `python main.py` to start application
+2. Access application at <http://127.0.0.1:8080/>
+3. Application usage instructions can be found on the homepage, and a visual walkthrough in the [final report](/reports/final/Final%20Report.pdf)

templates/main.html

@@ -49,7 +49,7 @@ <h2 style="color:rgba(255, 255, 255, 0.888)">Feature Toggles</h2>
           <li>Each person's headshots should go in a separate directory named with that person's name</li>
           <li>For accurate results, have at least 7 headshots per person to distinguish between 5 people, or 14
             headshots per person for 10 people</li>
-          <li>Example of compatible formatting can be found in <code>ml_backend/ml_face/face_sample</code></li>
+          <li>Example of a compatible directory structure can be found in <code>ml_backend/ml_face/face_sample</code></li>
         </ul>
       </ul>
       <input type="checkbox" name="rotation" value="3" checked>
@@ -69,15 +69,11 @@ <h2 style="color:rgba(255, 255, 255, 0.888)">Using this Application</h2>
         out of the following:
         <ol type="a">
           <li><b>Category Tags:</b> identifies common objects in an image and uses them to tag the image with one or more categories
-            <ul>
-              <li> Categories include: Portrait, Group Photo, Urban, Pet, Nature, Sports, Food</li>
-              <li>The objects that the models are trained to detect, and how they map to the categories,
-                are listed in <code>tagger/categories.py</code></li>
-            </ul>
+            <ul><li> Categories include: Portrait, Group Photo, Urban, Pet, Nature, Sports, Food</li></ul>
           </li>
           <li><b>Name Tags:</b> identifies people in an image and tags the image with their names</li>
-          <li><b>Rotation Correction:</b> detects crooked images and brightens them</li>
-          <li><b>Low-Light Enhancement:</b> detects dark images and outputs a better-lit version
+          <li><b>Rotation Correction:</b> detects crooked images and straightens them</li>
+          <li><b>Low-Light Enhancement:</b> detects dark images and brightens them
             <ul><li><i>Beta feature:</i> may output images with bright neon spots</li></ul>
           </li>
         </ol>
@@ -87,7 +83,7 @@ <h2 style="color:rgba(255, 255, 255, 0.888)">Using this Application</h2>
       <li>Click on the <u>Gallery</u> button to view all the uploaded photos
       <li>Click on an image to expand it and view and modify the tags it was given by the ML models</li>
       </li>
-      <li>Click on the <u>Download Photos</u> button to download all the uploaded images with their new tags</li>
+      <li>Click on the <u>Download Photos</u> button to download all the uploaded images with their new tags and enhancements</li>
     </ol>
     <br class="clearBoth" />