# Object detection, classification, and segmentation

Source: [https://docs.qualcomm.com/doc/80-70015-50/topic/object-detection-classification-and-segmentation-python-sample-app.html](https://docs.qualcomm.com/doc/80-70015-50/topic/object-detection-classification-and-segmentation-python-sample-app.html)

The **gst-filesrc-2detection-classification-segmentation-side-by-side.py** script
        identifies an object from a scene in a camera stream, overlays the bounding boxes over the
        detected objects, classifies scenes from the video stream, and produces semantic
        segmentations for the video. The output is displayed side by side on a screen.

Table : Models used for detection and classification

| Purpose | TFLite model | Description |
| :--- | :--- | :--- |
| Object detection | YOLOv8 | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ul_cfw_r4k_bdc"><br>                                <li class="li">Identify the object in a scene from a camera stream.</li><br><br>                                <li class="li">Overlay the bounding boxes over the detected objects.</li><br><br>                            </ol> |
| Image classification | Resnet101 | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ol_jll_v4k_bdc"><br>                                <li class="li">Classify a scene from a camera stream.</li><br><br>                                <li class="li">Overlay the classification labels on the screen.</li><br><br>                            </ol> |
| Image segmentation | FFNet40S | Produce semantic segmentations for the video file. |

## Use cases

1. Ensure that you complete the [Prerequisites](https://docs.qualcomm.com/doc/80-70015-50/topic/python-sample-applications.html#python-sample-applications__section_gm5_s5j_bdc).
2. Run the detection, classification, and segmentation
                    script:

        python3 /usr/bin/gst-filesrc-2detection-classification-segmentation-side-by-side.pyCopy to clipboard

The following are the input videos:

| Input video | Directory |
| --- | --- |
| Object detection | /opt/data/Draw\_720p\_180s\_30FPS.mp4 |
| Image classification | /opt/data/Animals\_000\_720p\_180s\_30FPS.mp4 |
| Image segmentation | /opt/data/Street\_Bridge\_720p\_180s\_30FPS.MOV |

The following are the default file paths in the Python script.

Table : Default directories for model and label files

| Model and label files | Directory |
| :--- | :--- |
| Detection model | <ul class="ul" id="object-detection-classification-and-segmentation-python-sample-app__ul_a35_rsr_bdc"><br>                                    <li class="li"><span class="ph filepath">/opt/data/YoloV8N_Detection_Quantized.tflite</span></li><br><br>                                    <li class="li"><span class="ph filepath">/opt/data/yolov7_quantized.tflite</span></li><br><br>                                </ul> |
| Detection labels | /opt/data/yolov8n.labels |
| Classification model | /opt/data/Resnet101\_Quantized.tflite |
| Classification labels | /opt/data/resnet101.labels |
| Segmentation model | /opt/data/ffnet\_40s\_quantized.tflite |
| Segmentation labels | /opt/data/dv3-argmax.labels |

## Expected output

The four streams are previewed side by side on a local display.

## Pipeline flow

Figure : Pipeline for object detection, image classification, and segmentation
                
                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)

| Process | Description |
| --- | --- |
| filesrc | Reads the video data from a file. |
| qtdemux | Demultiplexes the video data. |
| h264parse | Parses the H.264 video. |
| [v4l2h264dec](https://docs.qualcomm.com/doc/80-70015-50/topic/v4l2h264dec.html) | Decodes the H.264 video. |
| **Preprocessing** | **Preprocessing** |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvconverter.html) | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ol_i5w_4wl_vbc"><br>                                    <li class="li">Receives the video stream on its sink pad.</li><br><br>                                    <li class="li">Performs preprocessing:<ul class="ul" id="object-detection-classification-and-segmentation-python-sample-app__ol_zdw_qwl_vbc"><br>                                            <li class="li">Color conversion</li><br><br>                                            <li class="li">Scaling down/up</li><br><br>                                            <li class="li">Normalization on the stream data when the model<br>                                                expects the floating point values as an input</li><br><br>                                        </ul><br></li><br><br>                                    <li class="li">Converts the video stream to a tensor stream on its source<br>                                            pad.<p class="p">The object detection, classification, and<br>                                            segmentation models use this tensor stream for<br>                                            inferencing.</p><br></li><br><br>                                </ol> |
| **Inferencing** | **Inferencing** |
| [qtimltflite](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimltflite.html) | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ol_u1l_cxl_vbc"><br>                                    <li class="li">Loads the model.</li><br><br>                                    <li class="li">Modifies the graph for the chosen delegate.</li><br><br>                                    <li class="li">Receives the tensor stream on its sinkpad.</li><br><br>                                    <li class="li">Runs the inference and produces a tensor stream with the<br>                                        inference results on its source pad.</li><br><br>                                </ol> |
| **Postprocessing** | **Postprocessing** |
| [qtimlvdetection](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvdetection.html) | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ol_ky5_grn_vbc"><br>                                    <li class="li"> Receives the inference tensors from the object detection<br>                                        model.</li><br><br>                                    <li class="li">Converts the inference tensors on its sinkpad into formats<br>                                        such as video or text that the multimedia plugins can<br>                                        process later.</li><br><br>                                    <li class="li">Applies the threshold to the chosen number of results.</li><br><br>                                    <li class="li">Loads the corresponding modules for detection models. <p class="p">In<br>                                            this use case, qtimlvdetection does the following:<br>                                            </p><ol class="ol" type="a" id="object-detection-classification-and-segmentation-python-sample-app__ol_jcd_wnk_5bc"><br>                                            <li class="li">Loads the YOLOv8 submodule.</li><br><br>                                            <li class="li">Produces results as structures of text.</li><br><br>                                            <li class="li">Sends them to the sinkpad of qtimetamux.</li><br><br>                                        </ol><br></li><br><br>                                </ol> |
| [qtimlvclassification](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvclassification.html) | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ol_o3v_2xl_vbc"><br>                                    <li class="li">Receives the inference results from a classification model<br>                                        on its sinkpad. </li><br><br>                                    <li class="li">Converts the inference tensors into formats like video or<br>                                        text that the multimedia plugins can process later. </li><br><br>                                    <li class="li">Applies the threshold to the chosen number of results.</li><br><br>                                    <li class="li">Loads the corresponding modules for the classification<br>                                        models. <p class="p">In this use case, qtimlvclassification does the<br>                                            following: </p><ol class="ol" type="a" id="object-detection-classification-and-segmentation-python-sample-app__ol_p3v_2xl_vbc"><br>                                            <li class="li">Loads the submodule of the model.</li><br><br>                                            <li class="li">Produces results as video frames with classification<br>                                                labels.</li><br><br>                                            <li class="li">Sends them to the sinkpad of qtivcomposer.</li><br><br>                                        </ol><br></li><br><br>                                </ol> |
| [qtimlvsegmentation](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvsegmentation.html) | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ol_mtr_k5n_vbc"><br>                                    <li class="li">Receives the inference tensors on its sinkpad.</li><br><br>                                    <li class="li">Converts the inference tensors into video formats that the<br>                                        multimedia plugins can process later.</li><br><br>                                    <li class="li">Produces the semantic segmentations for the frame.</li><br><br>                                    <li class="li">Loads the corresponding modules for the segmentation<br>                                            models.<p class="p">In this use case, qtimlvsegmentation does the<br>                                            following: </p><ol class="ol" type="a" id="object-detection-classification-and-segmentation-python-sample-app__ol_ntr_k5n_vbc"><br>                                            <li class="li">Loads the deeplab-argmax submodule.</li><br><br>                                            <li class="li">Produces video frames with segmentation masks.</li><br><br>                                            <li class="li">Sends them to the sinkpad of qtivcomposer.</li><br><br>                                        </ol><br><br>                                    </li><br><br>                                </ol> |
| [qtimetamux](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimetamux.html) | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ol_ll3_x5l_vbc"><br>                                    <li class="li">Receives video stream and text stream with bounding box<br>                                        results corresponding to the video stream on its<br>                                        sinkpads.</li><br><br>                                    <li class="li">Produces GST buffers with contents of the video stream from<br>                                        its sink pad.</li><br><br>                                    <li class="li">Adds bounding boxes as GstVideoRegionOfInterest from data<br>                                        sinkpad to GST buffers meta (meta muxing) on its source<br>                                        pad.</li><br><br>                                </ol> |
| [qtioverlay](https://docs.qualcomm.com/doc/80-70015-50/topic/qtioverlay.html) | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ol_wst_y5l_vbc"><br>                                    <li class="li">Receives the multiplexed stream.</li><br><br>                                    <li class="li">Overlays the bounding boxes on the VideoFrame using CL.</li><br><br>                                    <li class="li">Produces GST buffers with overlays in its source pad.</li><br><br>                                </ol> |
| [qtivcomposer](https://docs.qualcomm.com/doc/80-70015-50/topic/qtivcomposer.html) | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ol_nmc_lxl_vbc"><br>                                    <li class="li">Receives the original video stream with classification<br>                                        results on its sinkpads. </li><br><br>                                    <li class="li">On its sourcepad, produces GST buffers with contents<br>                                        composed of video streams from its sinkpads.</li><br><br>                                </ol> |
| **Output** | **Output** |
| [Waylandsink](https://docs.qualcomm.com/doc/80-70015-50/topic/waylandsink.html) | <ol class="ol" id="object-detection-classification-and-segmentation-python-sample-app__ol_cgt_mwl_vbc"><br>                                    <li class="li">Receives the video in its sinkpad</li><br><br>                                    <li class="li">Submits the video stream to Weston. </li><br><br>                                    <li class="li">Weston renders the video stream on a local display<br>                                        device.</li><br><br>                                </ol> |

**Parent Topic:** [Python sample applications](https://docs.qualcomm.com/doc/80-70015-50/topic/python-sample-applications.html)

Last Published: Oct 27, 2025

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