--- license: mit license_link: LICENSE library_name: openvino pipeline_tag: object-detection tags: - openvino - intel - yolo - yolo26 - object-counting - coco - edge-ai - metro - dlstreamer language: - en --- # Object Counting | Property | Value | |---|---| | **Category** | Object Detection + Counting (80-class COCO) | | **Base Model** | [YOLO26](https://docs.ultralytics.com/models/yolo26/) (Ultralytics) | | **Source Framework** | PyTorch (Ultralytics) | | **Supported Precisions** | FP32, FP16, INT8 (mixed-precision) | | **Inference Engine** | OpenVINO | | **Hardware** | CPU, GPU, NPU | | **Detected Class(es)** | All 80 COCO classes (counted per class) | --- ## Overview Object Counting is a Metro Analytics use case that detects objects and reports how many of each class are present in an image or per video frame. It is built on [YOLO26](https://docs.ultralytics.com/models/yolo26/), a state-of-the-art real-time object detector, quantized to INT8 for efficient inference on Intel hardware. Counting is implemented as a thin aggregation layer on top of the strongest general-purpose detector, which keeps it accurate and reusable across classes. The DLStreamer sample below demonstrates this on a traffic scene sample video, counting **person**, **bicycle**, and **car** detections per frame. Typical Metro deployments include: - **Occupancy Counting** -- count people on a platform or in a waiting area. - **Vehicle Counting** -- count cars, buses, and trucks at an intersection. - **Inventory Counting** -- count bags, bottles, or other items in a zone. - **Throughput Metrics** -- aggregate per-frame counts into time series. Available variants: `yolo26n`, `yolo26s`, `yolo26m`, `yolo26l`, `yolo26x`. Smaller variants (`yolo26n`, `yolo26s`) are recommended for high-FPS edge deployment; larger variants improve recall for small or distant objects. For line-crossing counts (directional entry/exit), see the [vehicle-entry-exit-logging](../vehicle-entry-exit-logging/) use case. --- ## Prerequisites - Python 3.11+ - [Install OpenVINO](https://docs.openvino.ai/2026/get-started/install-openvino.html) (latest version) - [Install Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/get_started/install/install_guide_ubuntu.html) (latest version) Create and activate a Python virtual environment before running the scripts: ```bash python3 -m venv .venv --system-site-packages source .venv/bin/activate ``` > **Note:** The `--system-site-packages` flag is required so the virtual > environment can access the system-installed OpenVINO and DLStreamer Python > packages. --- ## Getting Started ### Download and Quantize Model Run the provided script to download, export to OpenVINO IR, and optionally quantize: ```bash chmod +x export_and_quantize.sh ./export_and_quantize.sh ``` This exports the default **yolo26n** model in **FP16** precision. #### Optional: Select a Different Variant or Precision ```bash ./export_and_quantize.sh yolo26n FP32 # full-precision ./export_and_quantize.sh yolo26n INT8 # quantized ./export_and_quantize.sh yolo26s # larger variant, default FP16 ``` Replace `yolo26n` with any variant (`yolo26s`, `yolo26m`, `yolo26l`, `yolo26x`). The second argument selects the precision (`FP32`, `FP16`, `INT8`); the default is **FP16**. The script performs the following steps: 1. Installs dependencies (`openvino`, `ultralytics`; adds `nncf` for INT8). 2. Downloads a sample test image (`test.jpg`) and a sample test video (`test_video.mp4`, the [`person-bicycle-car-detection.mp4`](https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4) street scene from Intel IoT DevKit's sample-videos repository). 3. Downloads the PyTorch weights and exports to OpenVINO IR. 4. *(INT8 only)* Quantizes the model using NNCF post-training quantization. Output files: - `yolo26n_openvino_model/` -- FP32 or FP16 OpenVINO IR model directory. - `yolo26n_objcount_int8.xml` / `yolo26n_objcount_int8.bin` -- INT8 quantized model *(only when `INT8` is selected)*. #### Precision / Device Compatibility | Precision | CPU | GPU | NPU | |---|---|---|---| | FP32 | Yes | Yes | No | | FP16 | Yes | Yes | Yes | | INT8 | Yes | Yes | Yes | > **Note:** The INT8 calibration uses the bundled sample image. > For production accuracy, replace it with a representative set of frames from > the target deployment site. ### OpenVINO Sample The sample below runs YOLO26 inference, then aggregates detections into a per-class count and a total count for a single image. YOLO26 is end-to-end (NMS-free), so no manual non-maximum suppression is needed. Change the `device` string to run on CPU, GPU, or NPU. ```python from collections import Counter import cv2 import numpy as np import openvino as ov CONF_THRESHOLD = 0.4 INPUT_SIZE = 640 core = ov.Core() model = core.read_model("yolo26n_openvino_model/yolo26n.xml") # YOLO26 embeds the 80 COCO class names in rt_info -- read them instead of # hardcoding the list. Ultralytics separates multi-word names with # underscores (e.g. "traffic_light"), so restore spaces for display. COCO_NAMES = [ name.replace("_", " ") for name in model.get_rt_info()["model_info"]["labels"].value.split() ] # Change device to "GPU" or "NPU" to run on integrated GPU or NPU. compiled = core.compile_model(model, "CPU") image = cv2.imread("test.jpg") h0, w0 = image.shape[:2] blob = cv2.resize(image, (INPUT_SIZE, INPUT_SIZE)) blob = cv2.cvtColor(blob, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0 blob = blob.transpose(2, 0, 1)[np.newaxis, ...] # NCHW # YOLO26 end-to-end output: [1, 300, 6] = [x1, y1, x2, y2, confidence, class_id] output = compiled([blob])[compiled.output(0)][0] dets = output[output[:, 4] >= CONF_THRESHOLD] sx, sy = w0 / INPUT_SIZE, h0 / INPUT_SIZE counts = Counter(COCO_NAMES[int(d[5])] for d in dets) print(f"Total objects: {len(dets)}") print("Object counts:") for name, n in sorted(counts.items(), key=lambda kv: (-kv[1], kv[0])): print(f" {name}: {n}") colors = np.random.RandomState(42).randint(0, 255, (80, 3)).tolist() for det in dets: x1, y1, x2, y2 = (int(det[0] * sx), int(det[1] * sy), int(det[2] * sx), int(det[3] * sy)) cid = int(det[5]) cv2.rectangle(image, (x1, y1), (x2, y2), colors[cid], 2) cv2.putText(image, COCO_NAMES[cid], (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, colors[cid], 2) summary = ", ".join(f"{n} {name}" for name, n in counts.items()) cv2.putText(image, summary[:60], (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) cv2.imwrite("output_openvino.jpg", image) ``` **Device targets:** - `"CPU"` -- default, works on all Intel platforms. - `"GPU"` -- Intel integrated or discrete GPU. - `"NPU"` -- Intel NPU (validate with `benchmark_app -d NPU`). ### Try It on a Sample Image The `export_and_quantize.sh` script downloads `test.jpg` automatically. Re-run the OpenVINO sample above. The script reads `test.jpg`, prints the per-class counts to the console, and writes the annotated frame to `output_openvino.jpg`. Expected console output (representative): ```text Total objects: 5 Object counts: person: 4 bus: 1 ``` #### Expected Output ![OpenVINO expected output](expected_output_openvino.jpg) ### DLStreamer Sample The pipeline below runs the FP16 YOLO26 detector on the sample video via `gvadetect`, overlays bounding boxes with `gvawatermark`, saves the annotated result to `output_dlstreamer.mp4`, and prints the per-frame **person**, **bicycle**, and **car** counts by reading the `GstAnalytics` detection metadata. The sample video ([`person-bicycle-car-detection.mp4`](https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4)) is a street scene containing pedestrians, a cyclist, and cars, matching the three classes counted below. > **Notes on running this sample:** > > - Use the FP16 IR (`yolo26n_openvino_model/yolo26n.xml`). Class names are > read automatically from the model's embedded `metadata.yaml` by > DLStreamer 2026.0+ -- no external `labels-file` is required. > - Export `PYTHONPATH` so the DLStreamer Python module is importable: > > ```bash > source /opt/intel/openvino_2026/setupvars.sh > source /opt/intel/dlstreamer/scripts/setup_dls_env.sh > export PYTHONPATH=/opt/intel/dlstreamer/python:\ > /opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:${PYTHONPATH:-} > ``` ```python from collections import Counter import gi gi.require_version("Gst", "1.0") gi.require_version("GstAnalytics", "1.0") from gi.repository import Gst, GLib, GstAnalytics Gst.init([]) INPUT_VIDEO = "test_video.mp4" # Only these classes are counted; the sample video contains pedestrians, # a cyclist, and cars. CLASSES_OF_INTEREST = {"person", "bicycle", "car"} # For CPU: change device=GPU to device=CPU. # For NPU: change device=GPU to device=NPU (batch-size=1, nireq=4 recommended). pipeline_str = ( f"filesrc location={INPUT_VIDEO} ! decodebin3 ! " "videoconvert ! " "gvadetect model=yolo26n_openvino_model/yolo26n.xml " "device=GPU " "threshold=0.4 ! queue ! " "gvawatermark ! videoconvert ! video/x-raw,format=I420 ! " "openh264enc ! h264parse ! " "mp4mux ! filesink name=sink location=output_dlstreamer.mp4" ) pipeline = Gst.parse_launch(pipeline_str) def on_buffer(pad, info): buf = info.get_buffer() rmeta = GstAnalytics.buffer_get_analytics_relation_meta(buf) if rmeta is None: return Gst.PadProbeReturn.OK counts = Counter() idx = 1 while True: ok, od = rmeta.get_od_mtd(idx) if not ok: break label = GLib.quark_to_string(od.get_obj_type()) if label in CLASSES_OF_INTEREST: counts[label] += 1 idx += 1 if counts: summary = ", ".join(f"{n} {name}" for name, n in counts.items()) print(f"Object counts: {summary}", flush=True) return Gst.PadProbeReturn.OK sink = pipeline.get_by_name("sink") sink.get_static_pad("sink").add_probe(Gst.PadProbeType.BUFFER, on_buffer) pipeline.set_state(Gst.State.PLAYING) bus = pipeline.get_bus() bus.timed_pop_filtered( Gst.CLOCK_TIME_NONE, Gst.MessageType.EOS | Gst.MessageType.ERROR, ) pipeline.set_state(Gst.State.NULL) ``` **Device targets:** - `device=GPU` -- default in the sample code. - `device=CPU` -- change `device=GPU` to `device=CPU`. - `device=NPU` -- change `device=GPU` to `device=NPU`; use `batch-size=1` and `nireq=4` for best NPU utilization. #### Expected Output ![DLStreamer expected output](expected_output_dlstreamer.gif) --- ## License Licensed under the MIT License. See [LICENSE](LICENSE) for details. ## References - [YOLO26 Documentation](https://docs.ultralytics.com/models/yolo26/) - [Ultralytics Object Counting Guide](https://docs.ultralytics.com/guides/object-counting/) - [Intel DLStreamer gvadetect](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/elements/gvadetect.html) - [OpenVINO Documentation](https://docs.openvino.ai/) - [NNCF Post-Training Quantization](https://docs.openvino.ai/latest/nncf_ptq_introduction.html) - [Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/index.html)