| Name | Implementation | Platforms | Model | ML engine | Backend | Features |
|---|---|---|---|---|---|---|
| example_classification_and_detection_tflite.cpp | C++ | i.MX 8M Plus i.MX 93 |
mobilenet_v1 mobilenet_ssd_v2 |
TFLite | NPU GPU CPU |
camera gst-launch |
| example_face_and_pose_detection_tflite.cpp | C++ | i.MX 8M Plus i.MX 93 |
UltraFace PoseNet Lightning |
TFLite | NPU GPU CPU |
camera gst-launch |
| example_emotion_and_detection_tflite.cpp | C++ | i.MX 8M Plus | UltraFace Deepface-emotion mobilenet_ssd_v2 |
TFLite | NPU GPU CPU |
camera gst-launch |
NOTES:
- No GPU support on i.MX 93
C++ example script needs to be generated with cross compilation. setup_environment.sh script needs to be executed in nxp-nnstreamer-examples folder to define data paths:
$ . ./tools/setup_environment.shExample of classification and object detection in parallel can be run with both models on NPU, using the following command:
$ ./build/mixed/example_classification_and_detection_tflite -p ${MOBILENETV1_QUANT},${MOBILENETV2_QUANT} -l ${MOBILENETV1_LABELS},${COCO_LABELS} -x ${MOBILENETV2_BOXES} -f ${SAVE_VIDEO_PATH}For i.MX 93 use vela converted model:
$ ./build/mixed/example_classification_and_detection_tflite -p ${MOBILENETV1_QUANT_VELA},${MOBILENETV2_QUANT_VELA} -l ${MOBILENETV1_LABELS},${COCO_LABELS} -x ${MOBILENETV2_BOXES} -f ${SAVE_VIDEO_PATH}To use other backends, refers to classification and object detection README for further details.
The output can't be saved on i.MX 93.
The following execution parameters are available (Run ./example_classification_and_detection_tflite -h to see option details):
| Option | Description |
|---|---|
| -b, --backend CLASS_BACKEND,DET_BACKEND | Use the selected backend (CPU, GPU, NPU) default: NPU,NPU |
| -n, --normalization CLASS_NORM,DET_NORM | Use the selected normalization (none, centered, reduced, centeredReduced, castInt32, castuInt8) default: none,none |
| -c, --camera_device | Use the selected camera device (/dev/video{number}) default: /dev/video0 for i.MX 93 and /dev/video3 for i.MX 8MP |
| -p, --model_path CLASS_MODEL,DET_MODEL | Use the selected model path |
| -l, --labels_path CLASS_LABELS,DET_LABELS | Use the selected labels path |
| -x, --boxes_path DET_BOXES | Use the selected boxes path |
| -d, --display_perf | Display performances, can specify time or freq |
| -t, --text_color | Color of performances displayed, can choose between red, green, blue, and black (white by default) |
| -g, --graph_path | Path to store the result of the OpenVX graph compilation (only for i.MX8MPlus) |
| -f, --video_file | Use the selected path to generate a video (optional) |
Press Esc or ctrl+C to stop the execution of the pipeline.
Example of face and pose detection in parallel can be run with face detection and pose detection on NPU, using the following command:
$ ./build/mixed/example_face_and_pose_detection_tflite -p ${ULTRAFACE_QUANT},${MOVENET_QUANT}For i.MX 93 use vela converted model:
$ ./build/mixed/example_face_and_pose_detection_tflite -p ${ULTRAFACE_QUANT_VELA},${MOVENET_QUANT_VELA}To use other backends, refers to pose detection and face detection README for further details.
The following execution parameters are available (Run ./example_face_and_pose_detection_tflite -h to see option details):
| Option | Description |
|---|---|
| -b, --backend FACE_BACKEND,POSE_BACKEND | Use the selected backend (CPU, GPU, NPU) default: NPU,CPU |
| -n, --normalization FACE_NORM,POSE_NORM | Use the selected normalization (none, centered, reduced, centeredReduced, castInt32, castuInt8) default: none,castInt32 |
| -c, --camera_device | Use the selected camera device (/dev/video{number}) default: /dev/video0 for i.MX 93 and /dev/video3 for i.MX 8MP |
| -p, --model_path FACE_MODEL,POSE_MODEL | Use the selected model path |
| -d, --display_perf | Display performances, can specify time or freq |
| -t, --text_color | Color of performances displayed, can choose between red, green, blue, and black (white by default) |
| -g, --graph_path | Path to store the result of the OpenVX graph compilation (only for i.MX8MPlus) |
Press Esc or ctrl+C to stop the execution of the pipeline.
Example of emotion and object detection in parallel can be run on NPU, using the following command:
$ ./build/mixed/example_emotion_and_detection_tflite -p ${ULTRAFACE_QUANT},${EMOTION_QUANT},${MOBILENETV2_QUANT} -f ${POWER_JUMP_VIDEO} -l ${COCO_LABELS} -x ${MOBILENETV2_BOXES}For i.MX 93 use vela converted model:
$ ./build/mixed/example_emotion_and_detection_tflite -p ${ULTRAFACE_QUANT_VELA},${EMOTION_QUANT_VELA},${MOBILENETV2_QUANT_VELA} -f ${POWER_JUMP_VIDEO} -l ${COCO_LABELS} -x ${MOBILENETV2_BOXES}To use other backends, refers to pose detection and face detection README for further details.
The following execution parameters are available (Run ./example_emotion_and_detection_tflite -h to see option details):
| Option | Description |
|---|---|
| -b, --backend FACE_BACKEND,EMOTION_BACKEND,DETECTION_BACKEND | Use the selected backend (CPU, GPU, NPU) default: NPU,CPU |
| -n, --normalization FACE_NORM,EMOTION_NORM,DETECTION_NORM | Use the selected normalization (none, centered, reduced, centeredReduced, castInt32, castuInt8) default: none,castInt32 |
| -c, --camera_device | Use the selected camera device (/dev/video{number}) default: /dev/video0 for i.MX 93 and /dev/video3 for i.MX 8MP |
| -p, --model_path FACE_MODEL,EMOTION_MODEL,DETECTION_MODEL | Use the selected model path |
| -f, --video_file | Use the selected video file |
| -d, --display_perf | Display performances, can specify time or freq |
| -t, --text_color | Color of performances displayed, can choose between red, green, blue, and black (white by default) |
| -g, --graph_path | Path to store the result of the OpenVX graph compilation (only for i.MX8MPlus) |
Press Esc or ctrl+C to stop the execution of the pipeline.