From c2ae1cc9a727bed3c76a76f37de45530195f805b Mon Sep 17 00:00:00 2001
From: Antonio Martinez <jose.antonio.martinez.torres@intel.com>
Date: Tue, 17 Sep 2024 13:59:08 -0700
Subject: [PATCH 1/3] Adding loss prevention documentation

Signed-off-by: Antonio Martinez <jose.antonio.martinez.torres@intel.com>
---
 .../use-cases/loss-prevention/advanced.md     |  35 ++++
 .../loss-prevention/getting_started.md        | 158 ++++++++++++++++++
 .../loss-prevention/loss-prevention.md        |  13 ++
 mkdocs.yml                                    |   4 +
 4 files changed, 210 insertions(+)
 create mode 100644 docs_src/use-cases/loss-prevention/advanced.md
 create mode 100644 docs_src/use-cases/loss-prevention/getting_started.md
 create mode 100644 docs_src/use-cases/loss-prevention/loss-prevention.md

diff --git a/docs_src/use-cases/loss-prevention/advanced.md b/docs_src/use-cases/loss-prevention/advanced.md
new file mode 100644
index 0000000..8fcafbc
--- /dev/null
+++ b/docs_src/use-cases/loss-prevention/advanced.md
@@ -0,0 +1,35 @@
+# Advanced Settings
+
+To further customize a loss prevention pipeline, let's add more variables to the execution:
+
+!!! Example
+
+    ```bash
+    make PIPELINE_SCRIPT=yolov8s_roi.sh RESULTS_DIR="../render_results"  run-render-mode
+    ```
+
+The above command will execute a DLStreamer pipeline using YOLOv8s model for object detection on a region of interest (ROI) with object tracking mechanism.
+
+## Modify ROI coordinates
+
+To modify the ROI coordinates, locate the file `roi.json` under src/pipelines/roi.json. Since the "objects" attribute is an array, it is possible to add multiple ROIs.
+
+```json
+[
+    {
+        "objects": [
+            {
+                "detection": {
+                    "label": "ROI1"
+                },
+                "x": 0,
+                "y": 0,
+                "w": 620,
+                "h": 1080
+            }           
+        ]
+    }
+]
+```
+
+
diff --git a/docs_src/use-cases/loss-prevention/getting_started.md b/docs_src/use-cases/loss-prevention/getting_started.md
new file mode 100644
index 0000000..5464e70
--- /dev/null
+++ b/docs_src/use-cases/loss-prevention/getting_started.md
@@ -0,0 +1,158 @@
+# Getting Started
+
+## Step by step instructions:
+
+1. Download the models using download_models/downloadModels.sh
+
+    ```bash
+    make download-models
+    ```
+
+2. Update github submodules
+
+    ```bash
+    make update-submodules
+    ```
+
+3. Download sample videos used by the performance tools
+
+    ```bash
+    make download-sample-videos
+    ```
+
+4. Build the demo Docker image
+
+    ```bash
+    make build
+    ```
+
+5. Start Loss prevention using the Docker Compose file. The Docker Compose also includes an RTSP camera simulator that will infinitely loop through the sample videos downloaded in step 3.
+
+    ```bash
+    make run-render-mode
+    ```
+
+6. Verify Docker containers
+
+    ```bash
+    docker ps --format 'table{{.Names}}\t{{.Status}}\t{{.Image}}'
+    ```
+    Result:
+    ```bash
+    NAMES                 STATUS          IMAGE
+    camera-simulator0    Up 17 seconds   jrottenberg/ffmpeg:4.1-alpine
+    src-OvmsClientGst-1  Up 17 seconds   dlstreamer:dev
+    camera-simulator     Up 17 seconds   aler9/rtsp-simple-server
+    ```
+
+7. Verify Results
+
+    After starting Automated Self Checkout you will begin to see result files being written into the results/ directory. Here are example outputs from the 3 log files.
+
+    gst-launch_<time>_gst.log
+    ```
+    /GstPipeline:pipeline0/GstGvaWatermark:gvawatermark0/GstCapsFilter:capsfilter1: caps = video/x-raw(memory:VASurface), format=(string)RGBA
+    /GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0/GstXImageSink:ximagesink0: sync = true
+    Got context from element 'vaapipostproc1': gst.vaapi.Display=context, gst.vaapi.Display=(GstVaapiDisplay)"\(GstVaapiDisplayGLX\)\ vaapidisplayglx0", gst.vaapi.Display.GObject=(GstObject)"\(GstVaapiDisplayGLX\)\ vaapidisplayglx0";
+    Progress: (open) Opening Stream
+    Pipeline is PREROLLED ...
+    Prerolled, waiting for progress to finish...
+    Progress: (connect) Connecting to rtsp://localhost:8554/camera_0
+    Progress: (open) Retrieving server options
+    Progress: (open) Retrieving media info
+    Progress: (request) SETUP stream 0
+    ```
+
+    pipeline<time>_gst.log
+    ```
+    14.58
+    14.58
+    15.47
+    15.47
+    15.10
+    15.10
+    14.60
+    14.60
+    14.88
+    14.88
+    ```
+
+    r<time>_gst.jsonl
+```json
+
+{
+    "objects": [
+        {
+            "detection": {
+                "bounding_box": {
+                    "x_max": 0.7873924346958825,
+                    "x_min": 0.6701603093745723,
+                    "y_max": 0.7915918938548927,
+                    "y_min": 0.14599881349270305
+                },
+                "confidence": 0.8677337765693665,
+                "label": "bottle",
+                "label_id": 39
+            },
+            "h": 697,
+            "region_id": 610,
+            "roi_type": "bottle",
+            "w": 225,
+            "x": 1287,
+            "y": 158
+        },
+        {
+            "detection": {
+                "bounding_box": {
+                    "x_max": 0.3221945836811382,
+                    "x_min": 0.19950163649114616,
+                    "y_max": 0.7924592239981934,
+                    "y_min": 0.1336837231479251
+                },
+                "confidence": 0.8625879287719727,
+                "label": "bottle",
+                "label_id": 39
+            },
+            "h": 711,
+            "region_id": 611,
+            "roi_type": "bottle",
+            "w": 236,
+            "x": 383,
+            "y": 144
+        },
+        {
+            "detection": {
+                "bounding_box": {
+                    "x_max": 0.5730873789069046,
+                    "x_min": 0.42000878963365595,
+                    "y_max": 0.9749763191740435,
+                    "y_min": 0.12431765065780453
+                },
+                "confidence": 0.854443371295929,
+                "label": "bottle",
+                "label_id": 39
+            },
+            "h": 919,
+            "region_id": 612,
+            "roi_type": "bottle",
+            "w": 294,
+            "x": 806,
+            "y": 134
+        }
+    ],
+    "resolution": {
+        "height": 1080,
+        "width": 1920
+    },
+    "timestamp": 755106652
+}
+
+```
+
+8. Stop the demo using docker compose down
+```bash
+make down
+```
+
+
+## [Proceed to Advanced Settings](advanced.md)    
diff --git a/docs_src/use-cases/loss-prevention/loss-prevention.md b/docs_src/use-cases/loss-prevention/loss-prevention.md
new file mode 100644
index 0000000..9a6fa5c
--- /dev/null
+++ b/docs_src/use-cases/loss-prevention/loss-prevention.md
@@ -0,0 +1,13 @@
+# Intel® Loss Prevention Reference Package
+
+## Overview
+
+As computer vision technology becomes more mainstream in industrial and retail settings, using it for loss prevention is becoming increasingly complex. These vision workloads are substantial and require multiple stages of processing. For example, a typical loss prevention pipeline might capture video data, define regions of interest, implement tracking to monitor which products customers interact with, analyze the data using models like YOLOv5 and EfficientNet, and then post-process it to generate metadata that highlights which products are being purchased or potentially stolen. This is just one example of how such models and workflows can be utilized.
+
+Implementing loss prevention solutions in retail isn't straightforward. Retailers, independent software vendors (ISVs), and system integrators (SIs) need a solid understanding of both hardware and software, as well as the costs involved in setting up and scaling these systems. Given the data-intensive nature of vision workloads, systems must be carefully designed, built, and deployed with numerous considerations in mind. Effectively combating shrinkage requires the right mix of hardware, software, and optimized configurations. For more details, you can check out the Intel Developer website and some informative LinkedIn blogs.
+
+The Intel® Loss Prevention Reference Package is designed to help with this. It provides the essential components needed to develop and deploy a loss prevention solution using Intel® hardware, software, and open-source tools. This reference implementation includes a pre-configured pipeline that's optimized for Intel® hardware, simplifying the setup of an effective computer vision-based loss prevention system for retailers.
+
+## Next Steps
+
+To begin using the loss prevention solution you can follow the [Getting Started Guide](./getting_started.md). 
diff --git a/mkdocs.yml b/mkdocs.yml
index 94b692d..9970937 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -56,6 +56,10 @@ nav:
       - AI Connect for Scientific Data (AiCSD):
           - Overview: 'use-cases/AiCSD/aicsd.md'
           - GRPC Yolov5s Pipeline: 'use-cases/AiCSD/pipeline-grpc-go.md'
+      - Loss Prevention:
+          - Overview: 'use-cases/loss-prevention/loss-prevention.md'
+          - Getting Started: 'use-cases/loss-prevention/getting_started.md'
+          - Advanced Settings: 'use-cases/loss-prevention/advanced.md'
   - Releases: 'releasenotes.md'
 extra_css:
   - ./stylesheets/index.css

From 95db613e217d8f606ba4f53e1367f6fa0e5a5dec Mon Sep 17 00:00:00 2001
From: Antonio Martinez <jose.antonio.martinez.torres@intel.com>
Date: Tue, 17 Sep 2024 20:16:46 -0700
Subject: [PATCH 2/3] Adding links

Signed-off-by: Antonio Martinez <jose.antonio.martinez.torres@intel.com>
---
 docs_src/use-cases/loss-prevention/advanced.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs_src/use-cases/loss-prevention/advanced.md b/docs_src/use-cases/loss-prevention/advanced.md
index 8fcafbc..77934ec 100644
--- a/docs_src/use-cases/loss-prevention/advanced.md
+++ b/docs_src/use-cases/loss-prevention/advanced.md
@@ -31,5 +31,6 @@ To modify the ROI coordinates, locate the file `roi.json` under src/pipelines/ro
     }
 ]
 ```
+For enviroments variables, follow the same tutorial as the automated self checkout [HERE](../automated-self-checkout/advanced.md)
 
 

From 0d3811902458375c917d63db12938f8b5dad8282 Mon Sep 17 00:00:00 2001
From: Antonio Martinez <jose.antonio.martinez.torres@intel.com>
Date: Mon, 23 Sep 2024 15:33:19 -0700
Subject: [PATCH 3/3] Removing unnecessary information

Signed-off-by: Antonio Martinez <jose.antonio.martinez.torres@intel.com>
---
 docs_src/use-cases/loss-prevention/loss-prevention.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs_src/use-cases/loss-prevention/loss-prevention.md b/docs_src/use-cases/loss-prevention/loss-prevention.md
index 9a6fa5c..dae52bf 100644
--- a/docs_src/use-cases/loss-prevention/loss-prevention.md
+++ b/docs_src/use-cases/loss-prevention/loss-prevention.md
@@ -4,7 +4,7 @@
 
 As computer vision technology becomes more mainstream in industrial and retail settings, using it for loss prevention is becoming increasingly complex. These vision workloads are substantial and require multiple stages of processing. For example, a typical loss prevention pipeline might capture video data, define regions of interest, implement tracking to monitor which products customers interact with, analyze the data using models like YOLOv5 and EfficientNet, and then post-process it to generate metadata that highlights which products are being purchased or potentially stolen. This is just one example of how such models and workflows can be utilized.
 
-Implementing loss prevention solutions in retail isn't straightforward. Retailers, independent software vendors (ISVs), and system integrators (SIs) need a solid understanding of both hardware and software, as well as the costs involved in setting up and scaling these systems. Given the data-intensive nature of vision workloads, systems must be carefully designed, built, and deployed with numerous considerations in mind. Effectively combating shrinkage requires the right mix of hardware, software, and optimized configurations. For more details, you can check out the Intel Developer website and some informative LinkedIn blogs.
+Implementing loss prevention solutions in retail isn't straightforward. Retailers, independent software vendors (ISVs), and system integrators (SIs) need a solid understanding of both hardware and software, as well as the costs involved in setting up and scaling these systems. Given the data-intensive nature of vision workloads, systems must be carefully designed, built, and deployed with numerous considerations in mind. Effectively combating shrinkage requires the right mix of hardware, software, and optimized configurations.
 
 The Intel® Loss Prevention Reference Package is designed to help with this. It provides the essential components needed to develop and deploy a loss prevention solution using Intel® hardware, software, and open-source tools. This reference implementation includes a pre-configured pipeline that's optimized for Intel® hardware, simplifying the setup of an effective computer vision-based loss prevention system for retailers.