[DLStreamer] Update license plate detection model and LPR sample (#681)

Author: msmiatac

libraries/dl-streamer/samples/download_public_models.sh

@@ -690,38 +690,23 @@ done
 if [[ "$MODEL" == "yolov8_license_plate_detector" ]] || [[ "$MODEL" == "all" ]]; then
   MODEL_NAME="yolov8_license_plate_detector"
   MODEL_DIR="$MODELS_PATH/public/$MODEL_NAME"
-  DST_FILE1="$MODEL_DIR/FP16/$MODEL_NAME.xml"
-  DST_FILE2="$MODEL_DIR/FP32/$MODEL_NAME.xml"
+  DST_FILE1="$MODEL_DIR/FP32/$MODEL_NAME.xml"
 
-  if [[ ! -f "$DST_FILE1" || ! -f "$DST_FILE2" ]]; then
+  if [[ ! -f "$DST_FILE1" ]]; then
     echo "Downloading and converting: ${MODEL_DIR}"
     mkdir -p "$MODEL_DIR"
     cd "$MODEL_DIR"
 
-    curl -L -k -o ${MODEL_NAME}.pt 'https://drive.usercontent.google.com/uc?export=download&id=1Zmf5ynaTFhmln2z7Qvv-tgjkWQYQ9Zdw'
-
-    python3 - <<EOF "$MODEL_NAME"
-from ultralytics import YOLO
-import openvino, sys, shutil, os
-
-model_name = sys.argv[1]
-weights = model_name + '.pt'
-
-model = YOLO(weights)
-model.info()
-converted_path = model.export(format='openvino')
-converted_model = converted_path + '/' + model_name + '.xml'
-core = openvino.Core()
-ov_model = core.read_model(model=converted_model)
-
-ov_model.set_rt_info('YOLOv8', ['model_info', 'model_type'])
-
-openvino.save_model(ov_model, './FP32/' + model_name + '.xml', compress_to_fp16=False)
-openvino.save_model(ov_model, './FP16/' + model_name + '.xml', compress_to_fp16=True)
-shutil.rmtree(converted_path)
-os.remove(f"{model_name}.pt")
-EOF
+    curl -L -k -o ${MODEL_NAME}.zip 'https://github.com/open-edge-platform/edge-ai-resources/raw/main/models/license-plate-reader.zip'
+    unzip -o ${MODEL_NAME}.zip
+    rm -f ${MODEL_NAME}.zip
 
+    mkdir -p FP32 
+    cp license-plate-reader/models/yolov8n/yolov8n_retrained.bin FP32/${MODEL_NAME}.bin
+    cp license-plate-reader/models/yolov8n/yolov8n_retrained.xml FP32/${MODEL_NAME}.xml
+    chmod -R u+w license-plate-reader
+    rm -rf license-plate-reader
+    cd ..
   else
     echo_color "\nModel already exists: $MODEL_DIR.\n" "yellow"
   fi
@@ -915,47 +900,23 @@ fi
 if [[ "$MODEL" == "ch_PP-OCRv4_rec_infer" ]] || [[ "$MODEL" == "all" ]]; then
   MODEL_NAME="ch_PP-OCRv4_rec_infer"
   MODEL_DIR="$MODELS_PATH/public/$MODEL_NAME"
-  DST_FILE1="$MODEL_DIR/FP32/$MODEL_NAME.xml"
-  DST_FILE2="$MODEL_DIR/FP16/$MODEL_NAME.xml"
+  DST_FILE1="$MODEL_DIR/FP16/$MODEL_NAME.xml"
 
-  if [[ ! -f "$DST_FILE1" || ! -f "$DST_FILE2" ]]; then
-    mkdir -p "$MODEL_DIR"
+  if [[ ! -f "$DST_FILE1" ]]; then
     echo "Downloading and converting: ${MODEL_DIR}"
+    mkdir -p "$MODEL_DIR"
     cd "$MODEL_DIR"
-    curl -L -O "https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/$MODEL_NAME.tar"
-    python3 - <<EOF "$MODEL_NAME" "$MODEL_DIR" "$DST_FILE1"
-import tarfile
-import openvino as ov
-import sys, os, shutil
-
-model_name = sys.argv[1]
-model_dir = sys.argv[2]
-orig_model_path = sys.argv[1]
-
-file = tarfile.open(f"{model_name}.tar")
-res = file.extractall(model_dir)
-file.close()
-if not res:
-    print(f"Model Extracted to {model_dir}.")
-else:
-    print("Error Extracting the model.")
 
-ov_model = ov.convert_model("ch_PP-OCRv4_rec_infer/inference.pdmodel")
-ov_model.reshape({"x": [-1, 3, 48, 192]})
+    curl -L -k -o ${MODEL_NAME}.zip 'https://github.com/open-edge-platform/edge-ai-resources/raw/main/models/license-plate-reader.zip'
+    unzip -o ${MODEL_NAME}.zip
+    rm -f ${MODEL_NAME}.zip
 
-ov_model.set_rt_info("paddle_ocr", ['model_info', 'model_type'])
-ov_model.set_rt_info("58.395, 57.12, 57.375", ['model_info', 'scale_values'])  #std = [0.229, 0.224, 0.225]
-ov_model.set_rt_info("123.675, 116.28, 103.53", ['model_info', 'mean_values'])  #mean = [0.485, 0.456, 0.406]
-ov_model.set_rt_info("true", ['model_info', 'reverse_input_channels'])
-ov_model.set_rt_info("standard", ['model_info', 'resize_type'])
-
-ov.save_model(ov_model, './FP32/' + 'ch_PP-OCRv4_rec_infer.xml', compress_to_fp16=False)
-ov.save_model(ov_model, './FP16/' + 'ch_PP-OCRv4_rec_infer.xml', compress_to_fp16=True)
-
-shutil.rmtree(model_name)
-os.remove(f"{model_name}.tar")
-
-EOF
+    mkdir -p FP32 
+    cp license-plate-reader/models/ch_PP-OCRv4_rec_infer/ch_PP-OCRv4_rec_infer.bin FP32/${MODEL_NAME}.bin
+    cp license-plate-reader/models/ch_PP-OCRv4_rec_infer/ch_PP-OCRv4_rec_infer.xml FP32/${MODEL_NAME}.xml
+    chmod -R u+w license-plate-reader
+    rm -rf license-plate-reader
+    cd ..
   else
     echo_color "\nModel already exists: $MODEL_DIR.\n" "yellow"
   fi

libraries/dl-streamer/samples/gstreamer/gst_launch/license_plate_recognition/README.md

@@ -23,7 +23,9 @@ This sample builds a GStreamer pipeline of the following elements:
 
 ## Models
 
-The sample uses the [`yolov8_license_plate_detector`](https://github.com/Muhammad-Zeerak-Khan/Automatic-License-Plate-Recognition-using-YOLOv8) model for license plate detection and the [`ch_PP-OCRv4_rec_infer`](https://github.com/PaddlePaddle/PaddleOCR) model for optical character recognition (OCR). The necessary conversion to the OpenVINO™ format is performed by the `download_public_models.sh` script located in the `samples` directory.
+The sample uses the [`yolov8_license_plate_detector`](https://github.com/open-edge-platform/edge-ai-resources/blob/main/models/license-plate-reader.zip) model for license plate detection and the [`ch_PP-OCRv4_rec_infer`](https://github.com/PaddlePaddle/PaddleOCR) model for optical character recognition (OCR). The necessary conversion to the OpenVINO™ format is performed by the `download_public_models.sh` script located in the `samples` directory.
+
+The accuracy of license plate detection depends on whether the applied model has been trained using data representing license plates from a specific region. In addition to the default model used in this example, the same pipeline can be used with other license plate detectors, such as the one available [`here`](https://github.com/Muhammad-Zeerak-Khan/Automatic-License-Plate-Recognition-using-YOLOv8).
 
 ## Running
 

libraries/dl-streamer/samples/gstreamer/gst_launch/license_plate_recognition/license_plate_recognition.sh

@@ -18,7 +18,7 @@ else
 fi
 
 # Command-line parameters
-INPUT=${1:-https://videos.pexels.com/video-files/30787543/13168475_1280_720_25fps.mp4}
+INPUT=${1:-https://github.com/open-edge-platform/edge-ai-resources/raw/main/videos/ParkingVideo.mp4}
 DEVICE=${2:-GPU}     # Device for decode and inference in OpenVINO(TM) format, examples: AUTO, CPU, GPU, GPU.0
 OUTPUT=${3:-fps}     # Output type, valid values: display, fps, json, display-and-json, file