Model not giving output from RGB Packed Grayscale Image

#1

Question/Issue:
I’m processing 16-bit image data and converting it to a packed RGB format (0x00RRGGBB), which is then stored as a float and passed into a classification model. The model is deployed as a C++ library using an impulse from Edge Impulse. The DSP block expects input as float values that internally represent RGB-packed pixels.

However, the model output doesn’t seem correct, and I’m unsure if the conversion step is being handled properly.

Is there a recommended way to convert raw input data into RGB-packed float values for such models? Should any normalization, clamping, or filtering be applied before mapping to RGB? Also, is value-casting uint32_t to float (instead of type-punning) a reliable method here?

Environment:

Controller: ESP32
Framework: FreeRTOS (VSCode)
Model Type: Classification model (Edge Impulse Impulse)
Deployment: C++ library
Language: C/C++
Code Snippet:

uint16_t raw_swapped = __builtin_bswap16(input[idx_in]);
float temp = raw_swapped;

float norm = (temp - min_C) / (max_C - min_C);
norm = (norm < 0.0f) ? 0.0f : (norm > 1.0f) ? 1.0f : norm;

uint8_t gray = (uint8_t)(norm * 255.0f);
uint32_t rgb_packed = (0x00 << 24) | (gray << 16) | (gray << 8) | gray;

output[idx_out] = static_cast(rgb_packed);
Additional Info:
Looking for any best practices, filters, or recommended preprocessing methods to ensure proper data format and model performance. Any guidance would be appreciated. The model was trained on PNG grayscale images.

#2

Hi @rajeevkr

For conversion, we don’t have a specific guide, but you can see how the block functions for training in our public repo for the server side training here and customise if needed if you wanted to do a more integrated change.

if the model was trained on grayscale, so you want to make sure you match the expected format, or convert appropriately to match the expected, as you are trying to.

Let me try and point you to the right entry points to confirm your conversion steps →

Training on the server side

You can see the normalization steps in our python server side image processing code here

Inference side (C++ embedded SDK)

Check what your model expects here for the on-device in the C++ library logic in image/image.cpp’s extract_image_features() function

Hope this helps, please let me know how you get on.

Best

Eoin