DeepQuestAI · memudualimatou · Mar 17, 2022 · Mar 15, 2022 · Mar 15, 2022 · Mar 17, 2022
diff --git a/README.md b/README.md
@@ -1,2 +1,95 @@
 # DeepStack_FireNET
-A custom DeepStack model for detecting fire indoor and outdoor
+
+
+This repository provides a custom DeepStack model that has been trained and can be used for creating a new `object detection API` for detecting **fire** present indoor and outdoor using [FireNET Dataset](https://github.com/OlafenwaMoses/FireNET). Also included in this repository is that dataset with the **YOLO annotations**.
+
+[>> Watch Video Demo](https://www.youtube.com/watch?v=ts3yxfNrDnY)
+
+- **Download DeepStack Model and Dataset**
+- **Create API and Detect Objects**
+- **Discover more Custom Models**
+- **Train your own Model**
+
+![](images/fire_net.png)
+
+# Download DeepStack Model and Dataset
+
+You can download the pre-trained **DeepStack_FireNET** model and the annotated dataset via the links below.
+
+- [YOLOv5x DeepStack Model](https://github.com/DeepQuestAI/DeepStack_FireNET/releases/tag/v1)
+
+- [FireNET with YOLO annotation](https://github.com/DeepQuestAI/DeepStack_FireNET/releases/download/v1/firenet_yolo.zip)
+
+
+# Create API and Detect Fire
+
+The Trained Model can detect **fire** in images and videos. 
+
+To start detecting, follow the steps below
+
+- **Install DeepStack:** Install DeepStack AI Server with instructions on DeepStack's documentation via [https://docs.deepstack.cc](https://docs.deepstack.cc/index.html#installation)
+- **Download Custom Model:** Download the trained custom model `firenetv1.pt` from [this GitHub release](https://github.com/DeepQuestAI/DeepStack_FireNET/releases/tag/v1). Create a folder on your machine and move the downloaded model to this folder.
+
+    E.g A path on Windows Machine `C\Users\MyUser\Documents\DeepStack-Models`, which will make your model file path `C\Users\MyUser\Documents\DeepStack-Models\firenetv1.pt`
+
+- **Run DeepStack:** To run DeepStack AI Server with the custom FireNET model, run the command that applies to your machine as detailed on DeepStack's documentation [linked here](https://docs.deepstack.cc/custom-models/deployment/index.html#starting-deepstack).
+
+    E.g
+
+    For a Windows version, you run the command below
+    ```bash
+    deepstack --MODELSTORE-DETECTION "C\Users\MyUser\Documents\DeepStack-Models" --PORT 80
+    ```
+
+    For a Linux machine
+    ```bash
+    sudo docker run -v /home/MyUser/Documents/DeepStack-Models -p 80:5000 deepquestai/deepstack
+    ```
+    Once DeepStack runs, you will see a log like the one below in your `Terminal/Console`
+
+    ![](images/custom_model.png)
+
+    That means DeepStack is running your custom `firenet.pt` model and now ready to start detecting fire images via the API endpoint `http://localhost:80/v1/vision/custom/firenet` or `http://your_machine_ip:80/v1/vision/custom/firenet`
+
+- **Detect fire in image:** You can detect objects in an image by sending a `POST` request to the url mentioned above with the paramater `image` set to an `image` using any proggramming language or with a tool like POSTMAN. For the purpose of this repository, we have provided a sample Python code below.
+
+    - A sample image can be found in `images/test.jpg` of this repository.
+
+    ![](images/test.png)
+
+    - Install Python and install the **DeepStack Python SDK** via the command below
+        ```bash
+        pip install deepstack_sdk
+        ```
+    - Run the Python file `detect.py` in this repository.
+
+        ```bash
+        python detect.py
+        ```
+    - After the code runs, you will find a new image in `images/test_detected.jpg` with the detection visualized, with the following results printed in the Terminal/Console.
+
+        ```
+       Name: fire, Confidence: 0.92534935, x_min: 607, y_min: 348, x_max: 797, y_max: 530
+        ```
+
+    ![](images/test_detected.jpg)
+
+- Fire detection sample images
+
+    ![](images/test1_detected.jpg)
+
+    ![](images/test2_detected.jpg)
+
+# Discover more Custom Models
+
+For more custom DeepStack models that has been trained and ready to use, visit the Custom Models sample page on DeepStack's documentation [https://docs.deepstack.cc/custom-models-samples/](https://docs.deepstack.cc/custom-models-samples/) .
+
+
+
+# Train your own Model
+
+If you will like to train a custom model yourself, follow the instructions below.
+
+- **Prepare and Annotate:** Collect images on and annotate object(s) you plan to detect as [ detailed here ](https://docs.deepstack.cc/custom-models/datasetprep/index.html)
+- **Train your Model:** Train the model as [detailed here](https://docs.deepstack.cc/custom-models/training/index.html)
+
diff --git a/detect.py b/detect.py
@@ -0,0 +1,11 @@
+from deepstack_sdk import ServerConfig, Detection
+import os
+
+config = ServerConfig("http://localhost:80")
+detection = Detection(config, name="firenetv1")
+
+response = detection.detectObject(image=os.path.join("images", "test.png"), output=os.path.join("images", "test_detected.jpg"))
+
+for obj in response:
+    print("Name: {}, Confidence: {}, x_min: {}, y_min: {}, x_max: {}, y_max: {}".format(obj.label, obj.confidence, obj.x_min, obj.y_min, obj.x_max,
+                                                                                        obj.y_max))
diff --git a/images/custom_model.PNG b/images/custom_model.PNG
diff --git a/images/fire_net.png b/images/fire_net.png
diff --git a/images/test.png b/images/test.png
diff --git a/images/test1.jpg b/images/test1.jpg
diff --git a/images/test1_detected.jpg b/images/test1_detected.jpg
diff --git a/images/test2.jpg b/images/test2.jpg
diff --git a/images/test2_detected.jpg b/images/test2_detected.jpg
diff --git a/images/test3.jpg b/images/test3.jpg
diff --git a/images/test3_detected.jpg b/images/test3_detected.jpg
diff --git a/images/test4.jpg b/images/test4.jpg
diff --git a/images/test4_detected.jpg b/images/test4_detected.jpg
diff --git a/images/test_detected.jpg b/images/test_detected.jpg