Update README.md

README.md

@@ -6,24 +6,26 @@ Simplifying the integration of weed detection into your platform.
 
 ## Overview
 The OpenWeedLocator (OWL) is an open-source weed detection device for low-cost, 
-DIY site-specific weed management. Here, we're making the software that supports 
-the OWL more accesible with an easy-to-use API, so you can integrate weed detection 
-into your own devices and services more easily by simply calling 
-`GreenonBrown.find()`.
+DIY site-specific weed management. The full project can be found [here](https://github.com/geezacoleman/OpenWeedLocator). 
+Here, we're making the software that supports the OWL more accesible with an easy-to-use API, 
+so you can integrate weed detection into your own devices and services more easily 
+by simply calling `GreenonBrown.find()`.
 
-Currently, it supports green-on-brown detection on all platforms, and green-on-green
-on desktops through the Ultralytics package.
+Currently, owl-tools supports green-on-brown detection on all platforms, and green-on-green
+on desktops through the Ultralytics package. Train your only YOLO algorithms and simply integrate 
+them by providing a path to the trained `model.pt`.
 
 In the future, we will support plant counting, GPS integration and green-on-green 
-on edge devices (Jetson Nano, Raspberry Pi)
+on edge devices (Jetson Nano, Raspberry Pi). If you're nterested in specific features,
+feel free to raise an issue or pull request.
 
 ## Installation
 To install the latest version of `openweedlocator-tools` simply run:
 ### Desktop (GreenOnGreen + GreenOnBrown)
 ```
 pip install openweedlocator-tools[desktop]
 ```
-### Raspberry Pi (
+### Raspberry Pi (GreenOnBrown only)
 ```
 pip install openweedlocator-tools[rpi]
 ```
@@ -40,17 +42,23 @@ webcam(algorithm='gog', model_path='models/yolov8n.pt) # press escape to exit, a
 images_and_video(media_path='path/to/your/media_files
 ```
 
+## Streamlit App
+Try it for yourself now with the online, Streamlit app!
+[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://owl-tools.streamlit.app/)
+
 ## Example
-This example shows the basic functionality of the detection classes, GreenOnBrown and GreenOnGreen to operate on a video.
+These examples shows the basic functionality of the detection classes, GreenOnBrown and GreenOnGreen to operate on a video.
+
 ```Python
 from owl.detection import GreenOnBrown
 import cv2
 
-video_path = 'path_to_video.mp4'
+VIDEO_PATH = r'media/test_video_1.avi'
+ALGORITHM = 'exhsv'
 
 weed_detector = GreenOnBrown()
 
-video_feed = cv2.VideoCapture(video_path)
+video_feed = cv2.VideoCapture(VIDEO_PATH)
 
 while True:
     ret, frame = video_feed.read()
@@ -59,7 +67,7 @@ while True:
         break
 
     # the weed_detector.find() method returns a tuple with four items
-    results = weed_detector.find(image,
+    results = weed_detector.find(frame.copy(),
                                  exgMin=30,
                                  exgMax=250,
                                  hueMin=30,
@@ -70,48 +78,52 @@ while True:
                                  saturationMax=255,
                                  minArea=1,
                                  show_display=False,
-                                 algorithm='exg',
+                                 algorithm=ALGORITHM,
                                  invert_hue=False)
-    
+
     contours, bounding_boxes, weed_centres, display_image = results
     cv2.imshow('OWL-tput', display_image)
-        
+
     if cv2.waitKey(1) & 0xFF == ord('q'):
         break
-
 ```
-Instead of instantiating your own `GreenOnBrown()` instance, you can make the most of the 
-`get_weed_detector()` and `setup_and_run_detector()`. The `setup_and_run_detector()` function 
-makes it easy to use the inbuilt `config.CONFIG_DAY_SENSITIVITY_1`, to avoid providing all
+
+Instead of instantiating your own `GreenOnBrown()` instance, you can make the most of 
+`owl.utils.io.get_weed_detector()` and `owl.utils.io.setup_and_run_detector()` functions. The `setup_and_run_detector()` function
+makes it easy to use the inbuilt `owl.utils.config.CONFIG_DAY_SENSITIVITY_1`, to avoid providing all
 variables yourself. For example:
 
 ```Python
-weed_detector = get_weed_detector(algorithm=algorithm, model_path=model_path, platform=platform)
+from owl.utils.io import get_weed_detector, load_config, setup_and_run_detector
+import cv2
+
+CONFIG_NAME = "CONFIG_DAY_SENSITIVITY_1"
+ALGORITHM = 'exhsv'
+MODEL_PATH = None
+VIDEO_PATH = r'media/test_video_1.avi'
+PLATFORM = 'desktop'
+
+weed_detector = get_weed_detector(algorithm=ALGORITHM, model_path=MODEL_PATH, platform='desktop')
 
-algorithm = 'exhsv'
-CONFIG_NAME="CONFIG_DAY_SENSITIVITY_1"
 config = load_config(CONFIG_NAME)
-config.update({"algorithm": f"{algorithm}"})
+config.update({"algorithm": f"{ALGORITHM}"})
 
-video_feed = cv2.VideoCapture(video_path)
+video_feed = cv2.VideoCapture(VIDEO_PATH)
 
 while True:
     ret, frame = video_feed.read()
 
-    _, _, _, image = setup_and_run_detector(weed_detector=weed_detector,
+    if not ret:
+        break
+
+    _, _, _, display_image = setup_and_run_detector(weed_detector=weed_detector,
                                             frame=frame.copy(),
                                             config=config)
-    cv2.imshow('Video Feed', image)
 
     cv2.imshow('OWL-tput', display_image)
-        
+
     if cv2.waitKey(1) & 0xFF == ord('q'):
         break
 
 cv2.destroyAllWindows()
 ```
-
-
-## Streamlit App
-Try it for yourself now with the online, Streamlit app!
-[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://owl-tools.streamlit.app/)