Merge pull request #101 from suryam789/main

sachinkaushik · web-flow · commit 7d32ee1f27df · 2025-11-19T10:57:58.000+05:30
REGISTRY info update in ASC, LP and OA
diff --git a/docs_src/use-cases/automated-self-checkout/advanced.md b/docs_src/use-cases/automated-self-checkout/advanced.md
@@ -70,7 +70,6 @@ The table below lists the environment variables (EVs) that can be used as inputs
     |`DOCKER_COMPOSE` | The docker-compose.yml file to run | src/docker-compose.yml |
     |`RETAIL_USE_CASE_ROOT` | The root directory for Automated Self Checkout in relation to the docker-compose.yml | .. |
     |`RESULTS_DIR` | Directory to output results | ../results |
-    |`REGISTRY` | Option to pull the pre-built images rather than creating them locally | true |
 
 === "Docker Compose Parameters"
     This list of parameters that can be set when running docker compose up
@@ -92,6 +91,7 @@ The table below lists the environment variables (EVs) that can be used as inputs
     |`GST_DEBUG` | for running pipeline in gst debugging mode | 0, 1 |
     |`LOG_LEVEL` | log level to be set when running gst pipeline | ERROR, INFO, WARNING, and [more](https://gstreamer.freedesktop.org/documentation/tutorials/basic/debugging-tools.html?gi-language=c#the-debug-log) |
     |`OCR_RECLASSIFY_INTERVAL` | time interval in seconds for OCR classification | Ex: 5 |
+    |`REGISTRY` | Option to pull the pre-built images rather than creating them locally (by default:true) | false, true |
     |`RENDER_MODE` | for displaying pipeline and overlay CV metadata | 1, 0 |
     |`PIPELINE_COUNT` | Number of Automated Self Checkout Docker container instances to launch | Ex: 1 |
     |`PIPELINE_SCRIPT` | Pipeline script to run. | yolo11n.sh, yolo11n_effnetb0.sh, yolo11n_full.sh |
diff --git a/docs_src/use-cases/automated-self-checkout/getting_started.md b/docs_src/use-cases/automated-self-checkout/getting_started.md
@@ -1,5 +1,18 @@
 # Getting Started
 
+### **NOTE:** 
+
+By default the application runs by pulling the pre-built images. If you want to build the images locally and then run the application, set the flag:
+
+```bash
+REGISTRY=false
+
+usage: make <command> REGISTRY=false (applicable for all commands like benchmark, benchmark-stream-density..)
+Example: make run-demo REGISTRY=false
+```
+
+(If this is the first time, it will take some time to download videos, models, docker images and build images)
+
 ## Step by step instructions:
 
 1. Download the models using download_models/downloadModels.sh
@@ -20,13 +33,7 @@
     make download-sample-videos
     ```
 
-4. Build the demo Docker image
-
-    ```bash
-    make build
-    ```
-
-5. Start Automated Self Checkout 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.
+4. Start Automated Self Checkout using the Docker Compose file.
 
     ```bash
     make run-render-mode
@@ -37,6 +44,26 @@
     ```bash
     make run-demo
     ```
+5. To build the images locally step by step:
+    - Follow the following steps:
+      ```bash
+      make download-models REGISTRY=false
+      make update-submodules REGISTRY=false
+      make download-sample-videos
+      ```
+    - Now build the pipeline-runner image locally:
+      ```bash
+      make build REGISTRY=false
+      ```
+    - Finally, start Automated self checkout using docker compose up.
+      ```bash
+      make run-render-mode REGISTRY=false
+      ```
+    - The above series of commands can be executed using only one command:
+    
+      ```bash
+      make run-demo REGISTRY=false
+      ```
 
 6. Verify Docker containers
 
diff --git a/docs_src/use-cases/automated-self-checkout/performance.md b/docs_src/use-cases/automated-self-checkout/performance.md
@@ -15,6 +15,7 @@ make benchmark-quickstart
 ```
 The above command would:<br>
 - Run headless (no display needed: `RENDER_MODE=0`)<br>
+- Pull pre-built images (`REGISTRY=true`)<br>
 - Use full pipeline (`PIPELINE_SCRIPT=obj_detection_age_prediction.sh`)<br>
 - Target GPU by default (`DEVICE_ENV=res/all-gpu.env`)<br>
 - Generate benchmark metrics<br>
@@ -52,7 +53,7 @@ List of EVs:
  |`PIPELINE_COUNT` | number of Automated Self Checkout Docker container instances to launch | Ex: 1 |
  |`PIPELINE_SCRIPT` | pipeline script to run. | yolo11n_effnetb0.sh, obj_detection_age_prediction.sh, etc. |
  |`DEVICE_ENV` | device to use for classification and detection | res/all-cpu.env, res/all-gpu.env, res/det-gpu_class-npu.env, etc. |    
- |`REGISTRY` | option to pull pre-built images from registry rather than building them locally | true |   
+ |`REGISTRY` | option to pull pre-built images from registry rather than building them locally (by default:true) | false, true |   
 
 > **Note:**  
 > Higher the `PIPELINE_COUNT`, higher the stress on the system.  
@@ -86,10 +87,13 @@ make PIPELINE_SCRIPT=obj_detection_age_prediction.sh DEVICE_ENV=res/all-gpu.env
 Stream 1: Object detection + classification on retail video <br>
 Stream 2: Face detection + age/gender prediction on age prediction video
 
-### Benchmark command to pull pre-built images
+### Benchmark command to build images locally
+
+!!! Note
+    By default the images are pulled from public repository.
 
 ```bash
-make REGISTRY=true PIPELINE_COUNT=1 benchmark
+make REGISTRY=false PIPELINE_COUNT=1 benchmark
 ```
 
 
diff --git a/docs_src/use-cases/loss-prevention/advanced.md b/docs_src/use-cases/loss-prevention/advanced.md
@@ -9,6 +9,7 @@ make benchmark-quickstart
 ```
 The above command would:<br>
 - Run headless (no display needed: `RENDER_MODE=0`)<br>
+- Pull pre-built images (`REGISTRY=true`)<br>
 - Target GPU by default (`WORKLOAD_DIST=workload_to_pipeline_gpu.json`)<br>
 - Run 6 streams, each with different workload (`CAMERA_STREAM=camera_to_workload_full.json`)<br>
 - Generate benchmark metrics<br>
@@ -32,7 +33,7 @@ Runs with:<br>
 - `CAMERA_STREAM=camera_to_workload.json`<br>
 - `WORKLOAD_DIST=workload_to_pipeline.json`<br>
 - `PIPELINE_COUNT=1`<br>
-- `REGISTRY=false`<br>
+- `REGISTRY=true`<br>
 
 You can override these values through the following Environment Variables.
 
@@ -42,7 +43,7 @@ You can override these values through the following Environment Variables.
 |`PIPELINE_COUNT` | number of Loss Prevention Docker container instances to launch | Ex: 1 |
 |`WORKLOAD_DIST` | to define how each workload is assigned to a specific processing unit (CPU, GPU, NPU) | workload_to_pipeline_cpu.json, workload_to_pipeline_gpu.json, workload_to_pipeline_gpu-npu.json, workload_to_pipeline_hetero.json, workload_to_pipeline.json |  
 |`CAMERA_STREAM` | to define camera settings and their associated workloads for the pipeline | camera_to_workload.json, camera_to_workload_full.json |    
-|`REGISTRY` | option to pull the pre-built images rather than creating them locally | true, false | 
+|`REGISTRY` | option to pull the pre-built images rather than creating them locally | false, true | 
 
 > **Note:**  
 > Higher the `PIPELINE_COUNT`, higher the stress on the system.  
@@ -99,11 +100,12 @@ You can override these values through the following Environment Variables.
 ### Benchmark command with environment variable overrides
 
 ```bash
-make benchmark WORKLOAD_DIST=workload_to_pipeline_gpu-npu.json CAMERA_STREAM=camera_to_workload_full.json
+make benchmark WORKLOAD_DIST=workload_to_pipeline_gpu-npu.json CAMERA_STREAM=camera_to_workload_full.json REGISTRY=false
 ```
 
 Runs with:<br>
 - `RENDER_MODE=0`<br>
+- `REGISTRY=false` (Builds images locally)<br> 
 - `CAMERA_STREAM=camera_to_workload_full.json`<br>
 - `WORKLOAD_DIST=workload_to_pipeline_gpu-npu.json`<br>
 - `PIPELINE_COUNT=1`<br>
@@ -112,7 +114,7 @@ Runs with:<br>
 ### See the benchmarking results
 
 ```sh
-make  consolidate-metrics
+make consolidate-metrics
 
 cat benchmark/metrics.csv
 ```
diff --git a/docs_src/use-cases/loss-prevention/getting_started.md b/docs_src/use-cases/loss-prevention/getting_started.md
@@ -1,5 +1,18 @@
 # Getting Started
 
+### **NOTE:** 
+
+By default the application runs by pulling the pre-built images. If you want to build the images locally and then run the application, set the flag:
+
+```bash
+REGISTRY=false
+
+usage: make <command> REGISTRY=false (applicable for all commands like benchmark, benchmark-stream-density..)
+Example: make run-lp REGISTRY=false
+```
+
+(If this is the first time, it will take some time to download videos, models, docker images and build images)
+
 ## Step by step instructions:
 
 1. Download the models using download_models/downloadModels.sh
@@ -28,19 +41,33 @@
 
     **NOTE:- User can directly run single make command that internally called all above command and run the Loss Prevention application.**
 
-5.  Run Loss Prevention appliaction with single command.   
+- Run Loss Prevention appliaction with single command.   
 
     ```bash
     make run-lp
     ```
 
     - Running Loss Prevention application with ENV variables:
       ```bash
-      REGISTRY=true CAMERA_STREAM=camera_to_workload_full.json WORKLOAD_DIST=workload_to_pipeline_cpu.json make run-lp
+      CAMERA_STREAM=camera_to_workload_full.json WORKLOAD_DIST=workload_to_pipeline_cpu.json make run-lp
       ```
-      `REGISTRY=true`: pre-built images are pulled. <br>
       `CAMERA_STREAM=camera_to_workload_full.json`: runs all 6 workloads. <br>
       `WORKLOAD_DIST=workload_to_pipeline_cpu.json`: all workloads run on CPU. <br>
+
+5. To build the images locally step by step:
+    - Follow the following steps:
+      ```bash
+      make download-models REGISTRY=false
+      make update-submodules REGISTRY=false
+      make download-sample-videos
+      make run-render-mode REGISTRY=false
+      ```
+      
+    - The above series of commands can be executed using only one command:
+    
+      ```bash
+      make run-lp REGISTRY=false
+      ```
       
 6. View the Dynamically Generated GStreamer Pipeline.
     >*Since the GStreamer pipeline is generated dynamically based on the provided configuration(camera_to_workload and workload_to_pipeline json), the pipeline.sh file gets updated every time the user runs make run-lp or make benchmark. This ensures that the pipeline reflects the latest changes.*
diff --git a/docs_src/use-cases/order-accuracy/advanced.md b/docs_src/use-cases/order-accuracy/advanced.md
@@ -9,6 +9,7 @@ make benchmark-quickstart
 ```
 The above command would:<br>
 - Run headless (no display needed: `RENDER_MODE=0`)<br>
+- Pull pre-built images (`REGISTRY=true`)<br>
 - Target GPU by default (`DEVICE_ENV=res/all-gpu.env`)<br>
 - Generate benchmark metrics<br>
 - Run `make consolidate-metrics` automatically<br>
@@ -28,6 +29,7 @@ make benchmark
 ```
 Runs with:<br>
 - `RENDER_MODE=0`<br>
+- `REGISTRY=true`<br>
 - `DEVICE_ENV=res/all-cpu.env`<br>
 - `PIPELINE_COUNT=1`<br>
 
@@ -36,6 +38,7 @@ You can override these values through the following Environment Variables.
 | Variable | Description | Values |
 |:----|:----|:---|
 |`RENDER_MODE` | for displaying pipeline and overlay CV metadata | 1, 0 |
+|`REGISTRY` | to pull pre-built images from public registry | false, true |
 |`PIPELINE_COUNT` | number of Loss Prevention Docker container instances to launch | Ex: 1 |
 |`DEVICE_ENV` | path to device specific environment file that will be loaded into the pipeline container | res/all-cpu.env, res/all-gpu.env, res/all-npu.env, res/all-dgpu.env |
 
@@ -53,10 +56,16 @@ make DEVICE_ENV=res/all-gpu.env benchmark
 make DEVICE_ENV=res/all-npu.env benchmark
 ```
 
+### Benchmark command to build images locally
+
+```bash
+make REGISTRY=false benchmark
+```
+
 ## See the benchmarking results.
 
 ```sh
-make  consolidate-metrics
+make consolidate-metrics
 
 cat benchmark/metrics.csv
 ```
diff --git a/docs_src/use-cases/order-accuracy/getting_started.md b/docs_src/use-cases/order-accuracy/getting_started.md
@@ -1,5 +1,18 @@
 # Getting Started
 
+### **NOTE:** 
+
+By default the application runs by pulling the pre-built images. If you want to build the images locally and then run the application, set the flag:
+
+```bash
+REGISTRY=false
+
+usage: make <command> REGISTRY=false (applicable for all commands like benchmark, benchmark-stream-density..)
+Example: make run-demo REGISTRY=false
+```
+
+(If this is the first time, it will take some time to download videos, models, docker images and build images)
+
 ## Step by step instructions:
 
 1. Download the models using download_models/downloadModels.sh
@@ -26,12 +39,23 @@
     make run-render-mode
     ```
 
-    **NOTE:- User can directly run single make command that internally called all above command and run the Order Accuracy application.**
-
-5.  Run Order Accuracy appliaction with single command.   
-
+- The above series of commands can be executed using only one command:
+    
+  ```bash
+  make run-demo
+  ```
+
+5. To build the images locally step by step:
+- Follow the following steps:
+  ```bash
+  make download-models REGISTRY=false
+  make update-submodules REGISTRY=false
+  make download-sample-videos
+  make run-render-mode REGISTRY=false
+  ```
+- The above series of commands can be executed using only one command:
     ```bash
-    make run-demo
+    make run-demo REGISTRY=false
     ```
 
 6. Verify Docker containers
