Merge pull request #1942 from jasonrandrews/review

Review blur and low-light camera Learning Path

Author: jasonrandrews

content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/1-prerequisites.md

@@ -8,7 +8,7 @@ layout: learningpathall
 
 ## Host machine requirements
 
-This Learning Path demonstrates how to improve the performance of camera pipelines using KleidiAI and KleidiCV in applications running on Arm. You will need an Arm64 machine, preferably running an Ubuntu-based distribution. These instructions have been tested on Ubuntu.
+This Learning Path demonstrates how to improve the performance of camera pipelines using KleidiAI and KleidiCV in applications running on Arm. You will need an Arm64 machine, preferably running an Ubuntu-based distribution. The instructions have been tested on Ubuntu 24.04.
 
 ## Install required software
 
@@ -24,14 +24,14 @@ These tools can be installed by running the following command, depending on your
 
 {{< tabpane code=true >}}
   {{< tab header="Linux/Ubuntu" language="bash">}}
-sudo apt install git git-lfs
+sudo apt install git git-lfs -y
   {{< /tab >}}
   {{< tab header="macOS" language="bash">}}
 brew install git git-lfs
   {{< /tab >}}
 {{< /tabpane >}}
 
-### docker
+### Docker
 
 Start by checking that `docker` is installed on your machine by typing the following command line in a terminal:
 
@@ -52,23 +52,19 @@ Once you have confirmed that Docker is installed on your machine, you can check
 docker run hello-world
 Unable to find image 'hello-world:latest' locally
 latest: Pulling from library/hello-world
-478afc919002: Pull complete
-Digest: sha256:305243c734571da2d100c8c8b3c3167a098cab6049c9a5b066b6021a60fcb966
+c9c5fd25a1bd: Pull complete 
+Digest: sha256:c41088499908a59aae84b0a49c70e86f4731e588a737f1637e73c8c09d995654
 Status: Downloaded newer image for hello-world:latest
 
 Hello from Docker!
 This message shows that your installation appears to be working correctly.
 
-To generate this message, Docker followed these steps:
-
+To generate this message, Docker took the following steps:
  1. The Docker client contacted the Docker daemon.
-
- 2. The Docker daemon pulled the "hello-world" image from Docker Hub.
+ 2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
     (arm64v8)
-
  3. The Docker daemon created a new container from that image which runs the
     executable that produces the output you are currently reading.
-
  4. The Docker daemon streamed that output to the Docker client, which sent it
     to your terminal.
 
@@ -82,13 +78,15 @@ For more examples and ideas, visit:
  https://docs.docker.com/get-started/
 ```
 
-### `libomp`
+### OpenMP library
+
+`libomp` is the OpenMP library developed by the LLVM project. It provides the necessary support for executing OpenMP parallel programs.
 
 `libomp` can be installed by running the following command, depending on your OS:
 
 {{< tabpane code=true >}}
   {{< tab header="Linux/Ubuntu" language="bash">}}
-sudo apt install libomp-19-dev
+sudo apt install libomp-19-dev -y
   {{< /tab >}}
   {{< tab header="macOS" language="bash">}}
 brew install libomp

content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/5-performances.md

@@ -20,14 +20,32 @@ These benchmarks demonstrate the performance improvements enabled by KleidiCV an
 
 ## Performances with KleidiCV and KleidiAI
 
-By default, the OpenCV library is built with KleidiCV support, and TFLite+xnnpack is built with KleidiAI support. You can run the benchmarks using the applications you built earlier:
+By default, the OpenCV library is built with KleidiCV support, and TFLite+xnnpack is built with KleidiAI support. 
+
+You can run the benchmarks using the applications you built earlier.
+
+Run the first benchmark:
 
 ```bash
-$ bin/cinematic_mode_benchmark 20 resources/depth_and_saliency_v3_2_assortedv2_w_augment_mobilenetv2_int8_only_ptq.tflite
+bin/cinematic_mode_benchmark 20 resources/depth_and_saliency_v3_2_assortedv2_w_augment_mobilenetv2_int8_only_ptq.tflite
+```
+
+The output is similar to:
+
+```output
 INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
 Total run time over 20 iterations: 2023.39 ms
+```
+
+Run the second benchmark:
+
+```bash
+bin/low_light_image_enhancement_benchmark 20 resources/HDRNetLIME_lr_coeffs_v1_1_0_mixed_low_light_perceptual_l2_loss_int8_only_ptq.tflite
+```
 
-$ bin/low_light_image_enhancement_benchmark 20 resources/HDRNetLIME_lr_coeffs_v1_1_0_mixed_low_light_perceptual_l2_loss_int8_only_ptq.tflite
+The output is similar to:
+
+```output
 INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
 Total run time over 20 iterations: 54.3546 ms
 ```
@@ -43,14 +61,28 @@ To measure the performance without these optimizations, recompile the pipelines
 -DENABLE_KLEIDICV:BOOL=OFF -DXNNPACK_ENABLE_KLEIDIAI:BOOL=OFF
 ```
 
-Then rerun the benchmarks:
+Re-run the first benchmark:
 
 ```bash
-$ bin/cinematic_mode_benchmark 20 resources/depth_and_saliency_v3_2_assortedv2_w_augment_mobilenetv2_int8_only_ptq.tflite
+bin/cinematic_mode_benchmark 20 resources/depth_and_saliency_v3_2_assortedv2_w_augment_mobilenetv2_int8_only_ptq.tflite
+```
+
+The new output is similar to:
+
+```output
 INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
 Total run time over 20 iterations: 2029.25 ms
+```
+
+Re-run the second benchmark:
+
+```bash
+bin/low_light_image_enhancement_benchmark 20 resources/HDRNetLIME_lr_coeffs_v1_1_0_mixed_low_light_perceptual_l2_loss_int8_only_ptq.tflite
+```
+
+The new output is similar to:
 
-$ bin/low_light_image_enhancement_benchmark 20 resources/HDRNetLIME_lr_coeffs_v1_1_0_mixed_low_light_perceptual_l2_loss_int8_only_ptq.tflite
+```output
 INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
 Total run time over 20 iterations: 79.431 ms
 ```

content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/_index.md

@@ -1,9 +1,5 @@
 ---
-title: Accelerate Background Blur and Low-Light Camera Effects on Android
-
-draft: true
-cascade:
-    draft: true
+title: Accelerate Background Blur and Low-Light Camera Effects 
 
 minutes_to_complete: 30
 
@@ -14,15 +10,12 @@ learning_objectives:
     - Use KleidiCV and KleidiAI to improve the performance of real-time camera pipelines.
 
 prerequisites:
-    - CMake.
-    - Git + Git LFS.
-    - Docker.
+    - A computer running Arm Linux or macOS with Docker installed.
 
 author: Arnaud de Grandmaison.
 
 test_images:
     - ubuntu:latest
-test_link: null
 test_maintenance: false
 
 ### Tags
@@ -35,7 +28,6 @@ tools_software_languages:
 operatingsystems:
     - Linux
     - macOS
-    - Windows
 
 further_reading: