Sync license-plate-recognition from metro-analytics-catalog

LICENSE

@@ -1,56 +1,21 @@
-This directory contains two categories of content under different licenses.
-
-
-Scripts and Documentation
--------------------------
-
-The scripts (export_and_quantize.sh) and documentation (README.md) in this
-directory are original works by Intel Corporation, licensed under the
-MIT License.
-
-    Copyright (C) Intel Corporation
-
-    Permission is hereby granted, free of charge, to any person obtaining a copy
-    of this software and associated documentation files (the "Software"), to deal
-    in the Software without restriction, including without limitation the rights
-    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-    copies of the Software, and to permit persons to whom the Software is
-    furnished to do so, subject to the following conditions:
-
-    The above copyright notice and this permission notice shall be included in
-    all copies or substantial portions of the Software.
-
-    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-    THE SOFTWARE.
-
-
-License Plate Detector Model (yolov8_license_plate_detector)
-------------------------------------------------------------
-
-The YOLOv8 license plate detector weights are distributed by the Intel Edge
-AI Resources project and are based on the Ultralytics YOLOv8 framework,
-licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).
-
-    Source:  https://github.com/open-edge-platform/edge-ai-resources
-    Upstream framework: https://github.com/ultralytics/ultralytics
-    License: https://github.com/ultralytics/ultralytics/blob/main/LICENSE
-    Docs:    https://docs.ultralytics.com/models/yolov8/
-
-Users must comply with the AGPL-3.0 license terms when using, modifying,
-or distributing the YOLOv8 model weights or Ultralytics software.
-For commercial licensing options, see https://www.ultralytics.com/license.
-
-
-OCR Model (ch_PP-OCRv4_rec_infer)
----------------------------------
-
-The PaddleOCR PP-OCRv4 recognition model is developed by PaddlePaddle and
-licensed under the Apache License, Version 2.0.
-
-    Source:  https://github.com/PaddlePaddle/PaddleOCR
-    License: https://github.com/PaddlePaddle/PaddleOCR/blob/main/LICENSE
+MIT License
+
+Copyright (c) Intel Corporation.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE

README.md

@@ -1,5 +1,5 @@
 ---
-license: other
+license: mit
 license_link: LICENSE
 library_name: openvino
 pipeline_tag: object-detection
@@ -10,6 +10,7 @@ tags:
   - paddleocr
   - license-plate-recognition
   - ocr
+  - gstanalytics
   - edge-ai
   - metro
   - dlstreamer
@@ -21,7 +22,7 @@ language:
 
 | Property | Value |
 |---|---|
-| **Category** | Object Detection + Optical Character Recognition |
+| **Category** | Object Detection + Optical Character Recognition (GstAnalytics) |
 | **Source Framework** | PyTorch (Ultralytics YOLOv8), PaddlePaddle (PP-OCRv4) |
 | **Supported Precisions** | FP32 |
 | **Inference Engine** | OpenVINO |
@@ -111,8 +112,9 @@ The PaddleOCR recognizer ships inside the same archive:
 
 > **Note:** PaddleOCR PP-OCRv4 is a CTC sequence model. DLStreamer 2026.0+
 > auto-derives the CTC decoder for the bundled `ch_PP-OCRv4_rec_infer` IR
-> and exposes the decoded plate string as `tensor.label()` on each
-> classified ROI -- no external `model-proc` is required for this sample.
+and exposes the decoded plate string as a `GstAnalytics.ClsMtd` entry
+(with `CONTAIN` relation to the detection) -- no external `model-proc`
+is required for this sample.
 > For other PaddleOCR variants or non-Latin character sets, supply a custom
 > `model-proc` (see
 > [DLStreamer model_proc reference](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/dev_guide/model_proc_file.html))
@@ -127,8 +129,8 @@ It mirrors the structure of the upstream
 sample: `decodebin3 ! queue ! gvadetect ! queue ! videoconvert ! gvaclassify ! queue ! gvawatermark ! ...`.
 The `gvadetect` element runs the license plate detector;
 `gvaclassify` then runs the PaddleOCR recognizer on each detected plate region.
-A buffer probe extracts the recognized text from the inference metadata
-attached to each frame.
+A buffer probe reads the `GstAnalytics` classification metadata
+attached to each detection to extract the recognized plate text.
 The input is `ParkingVideo.mp4`, the short parking-lot clip downloaded by
 `export_and_quantize.sh` into the current directory.
 The annotated stream is muxed into `output_dlstreamer.mp4` with H.264 (OpenH264).
@@ -139,11 +141,11 @@ import os
 import gi
 
 gi.require_version("Gst", "1.0")
-gi.require_version("GstVideo", "1.0")
-from gi.repository import Gst
-from gstgva import VideoFrame
+gi.require_version("GstAnalytics", "1.0")
+gi.require_version("DLStreamerWatermarkMeta", "1.0")
+from gi.repository import Gst, GLib, GstAnalytics, DLStreamerWatermarkMeta
 
-Gst.init(None)
+Gst.init([])
 
 MODELS_DIR = os.path.abspath("./models/yolov8_license_plate_detector")
 DETECTOR_XML = (
@@ -163,9 +165,10 @@ pipeline_str = (
     f"gvadetect model={DETECTOR_XML} device={DEVICE} ! queue ! "
     f"videoconvert ! "
     f"gvaclassify model={OCR_XML} device={DEVICE} ! queue ! "
-    f"gvawatermark ! videoconvert ! video/x-raw,format=I420 ! "
+    f"identity name=probe ! "
+    f"gvawatermark displ-cfg=show-labels=false ! videoconvert ! video/x-raw,format=I420 ! "
     f"openh264enc ! h264parse ! "
-    f"mp4mux ! filesink name=sink location=output_dlstreamer.mp4"
+    f"mp4mux ! filesink location=output_dlstreamer.mp4"
 )
 
 pipeline = Gst.parse_launch(pipeline_str)
@@ -173,28 +176,36 @@ pipeline = Gst.parse_launch(pipeline_str)
 
 def on_buffer(pad, info):
     buf = info.get_buffer()
-    caps = pad.get_current_caps()
-    frame = VideoFrame(buf, caps=caps)
-    for region in frame.regions():
-        rect = region.rect()
+    new_buf = buf.copy_deep()
+    rmeta = GstAnalytics.buffer_get_analytics_relation_meta(new_buf)
+    if not rmeta:
+        info.set_buffer(new_buf)
+        return Gst.PadProbeReturn.OK
+    for od in rmeta.iter_on_type(GstAnalytics.ODMtd):
+        _, x, y, w, h, _ = od.get_location()
+        # OCR result is attached as ClsMtd with CONTAIN relation
         text = ""
-        for tensor in region.tensors():
-            if tensor.is_detection():
-                continue
-            try:
-                text = tensor.label() or ""
-            except RuntimeError:
-                continue
-            if text:
-                break
+        for cls in od.iter_direct_related(
+            GstAnalytics.RelTypes.CONTAIN, GstAnalytics.ClsMtd
+        ):
+            if cls.get_length() > 0:
+                q = cls.get_quark(0)
+                text = GLib.quark_to_string(q) if q else ""
+            break
         if text:
-            print(f"Plate: {text}  bbox=({rect.x},{rect.y})", flush=True)
+            DLStreamerWatermarkMeta.text_meta_add(
+                new_buf, x=int(x), y=max(0, int(y) - 10),
+                text=text, font_scale=0.6, font_type=0,
+                r=0, g=255, b=0, thickness=1, draw_bg=True)
+            print(f"Plate: {text}  bbox=({x},{y})", flush=True)
+    info.set_buffer(new_buf)
     return Gst.PadProbeReturn.OK
 
 
-sink = pipeline.get_by_name("sink")
-sink_pad = sink.get_static_pad("sink")
-sink_pad.add_probe(Gst.PadProbeType.BUFFER, on_buffer)
+probe = pipeline.get_by_name("probe")
+probe.get_static_pad("src").add_probe(
+    Gst.PadProbeType.BUFFER, on_buffer
+)
 
 pipeline.set_state(Gst.State.PLAYING)
 bus = pipeline.get_bus()