update readme

README.md

@@ -1,65 +1,172 @@
-# Easy local ALPR (Automatic License Plate Recognition)
+![logo](.git-assets/logo.webp)
+![ALPR](.git-assets/preview-webui.webp)
 
-![ALPR](preview-webui.webp)
+#  Easy Local ALPR (Automatic License Plate Recognition)
+This project is a simple local ALPR (Automatic License Plate Recognition) server that uses the [ultimateALPR-SDK](https://github.com/DoubangoTelecom/ultimateALPR-SDK) to
+process images and return the license plate information found in the image while focusing on being:
+- **Fast** *(~100ms per image on decent CPU)*
+- **Lightweight** *(~100MB of RAM)*
+- **Easy to use** *(REST API)*
+- **Easy to setup** *(one command setup)*
+- **Offline** *(no internet connection required)*
 
-This script is a REST API server that uses [ultimateALPR-SDK](https://github.com/DoubangoTelecom/ultimateALPR-SDK)  to process images and return the license plate
-information. The server is created using Flask and the ultimateALPR SDK is used to process the images.
-
-This script is intended to be used as a faster local alternative to the large and resource heavy [CodeProject AI](https://www.codeproject.com/AI/docs) software.
 > [!IMPORTANT]
-> The ultimateALPR SDK is a lightweight and much faster alternative (on CPU and GPU) to the CodeProject AI software but it has **a few limitations** with it's free version:
-> - The last character of the license plate is masked with an asterisk
-> - The SDK supposedly has a limit of requests per program execution *(never encountered yet)* **but I have implemented a workaround for this by restarting the SDK after 3000 requests just in case.**
+> This project relies on the [ultimateALPR-SDK](https://github.com/DoubangoTelecom/ultimateALPR-SDK), which is a commercial product but has a free version with a few limitations.
+> For any commercial use, you will need to take a look at their licensing terms.  
+> **I am not affiliated with ultimateALPR-SDK in any way, and I am not responsible for any misuse of the software.**
 
+> [!NOTE]
+> The [ultimateALPR-SDK](https://github.com/DoubangoTelecom/ultimateALPR-SDK) is a lightweight and much faster alternative (on CPU and GPU) than existing solutions but it has **one important restriction** with it's free version:
+> - The last character of the license plate is masked with an asterisk *(e.g. ``ABC1234`` -> ``ABC123*``)*
+
+## Installation
+Simply download the latest release from the [releases page](./releases) and run the executable.
+
+The following platforms are currently supported:
+- **Linux** (x86_64)
 
 ## Usage
-The server listens on port 5000 and has one endpoint: /v1/image/alpr. The endpoint accepts POST requests with an
-image file in the 'upload' field. The image is processed using the ultimateALPR SDK and the license plate
-information is returned in JSON format. The reponse follows the CodeProject AI ALPR API format. So it can be used
-as a drop-in replacement for the [CodeProject AI ALPR API](https://www.codeproject.com/AI/docs/api/api_reference.html#license-plate-reader).
+
+The server listens on port 5000 and has a few endpoints documented below, the most important one being [``/v1/image/alpr``](#v1visionalpr).
+
+### /v1/vision/alpr
 
 > POST: http://localhost:5000/v1/vision/alpr
 
+**Description**  
+This endpoint processes an image and returns the license plate information (if any) found in the image.
 **Parameters**
-- upload: (File) The image file to process. (see [Pillow.Image.open()](https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.open) for supported formats)
-- grid_size: (Integer, optional) The grid size to use when no match have been found on the whole image (default: 4)
-- wanted_cells: (String, optional) The wanted cells to use when no match have been found on the whole image (default: all cells)
-    - format: ``1,2,3,4,...`` *(comma separated list of integers, max: grid_size^2)*
+
+- upload: (File) The image file to process. *(
+  see [Pillow.Image.open()](https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.open) for supported
+  formats, almost any image format is supported)*
+- grid_size: (Integer, optional) Size of grid to divide the image into and retry on each cell when no match have been
+  found on the whole image, must be ``>=2`` *(default: 0, disabled)* **[(more info)](#more-information-about-the-grid-parameter)**
+- wanted_cells: (String, optional) The cells you want to process *(default: all cells)* **[(see here for more details)](#v1visionalpr_grid_debug)**
+    - format: ``1,2,3,4,...`` *(comma separated list of integers, max: ``grid_size^2``)*
     - *Example for a grid_size of 3:*
       ```
         1 | 2 | 3
         4 | 5 | 6
         7 | 8 | 9
       ```
-      
+- whole_image_fallback: (Boolean, optional) Only applies when ``grid_size`` is greater than 2.
+  If set to true, the server will first try to detect the plate from the ``wanted_cells`` parameter and if no plate is found, it will then try to detect the plate on the whole image.
+  If set to false, the server will only try to detect the plate on the specified cells. *(default: true)*
 
-  **Response**
+**Response**
+
+```jsonc
+{
+  "duration": (Float) // The time taken to process the image in milliseconds.
+  "plates": List // An array of plates found in the image.
+  "predictions": (Object[]) // An array of objects with the x_max, x_min, y_max, y_min bounds of the plate, image, the plate chars and confidence.
+}
+```
+
+**Example**
 ```json
 {
-  "success": (Boolean) // True if successful.
-  "message": (String) // A summary of the inference operation.
-  "error": (String) // (Optional) An description of the error if success was false.
-  "predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min bounds of the plate, label, the plate chars and confidence.
-  "processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
+  "duration": 142.02,
+  "plates": [
+    "XX12345*",
+    "YY5432*"
+  ],
+  "predictions": [
+    {
+      "confidence": 0.9009034,
+      "image": "data:image/png;base64,xxxxx==",
+      "plate": "XX12345*",
+      "x_max": 680,
+      "x_min": 610,
+      "y_max": 386,
+      "y_min": 355
+    },
+    {
+      "confidence": 0.8930383999999999,
+      "image": "data:image/png;base64,xxxxx==",
+      "plate": "YY5432*",
+      "x_max": 680,
+      "x_min": 483,
+      "y_max": 706,
+      "y_min": 624
+    }
+  ]
 }
 ```
 
 
+### /v1/vision/alpr_grid_debug
+
+> POST: http://localhost:5000/v1/vision/alpr_grid_debug
+
+**Description**
+This endpoint displays the grid and each cell's number on the image.
+It is intended to be used for debugging purposes to see which cells are being processed.
+
+**Parameters**  
+*same as [v1/vision/alpr](#v1visionalpr)*
+
+**Response**
+
+```jsonc
+{
+  "image": (Base64) // The image with the grid and cell numbers drawn on it.
+}
+```
+
+## More information about the grid parameter
+
+Sometimes, the ALPR software cannot find any plate because the image is too big or the plate is too small in the image.
+To solve this problem, you can make use of the ``grid_size`` parameter in each of your requests.
+If you set the ``grid_size`` parameter to a value greater than 2, the server will divide the image into a grid of cells
+and retry the ALPR software on each cell.
+
+You can speed up the processing time by specifying the ``wanted_cells`` parameter. This parameter allows you to specify
+which cells you want to run plate detection on.
+This can be useful if you know the plates can only be in certain areas of the image.
+> [!TIP]
+> You can use the [``/v1/vision/alpr_grid_debug`` endpoint](#v1visionalpr_grid_debug) to see the grid and cell numbers
+> overlaid on your image.
+> You can then specify the ``wanted_cells`` parameter to only process the cells you want.
+
+**If you wish not to use the grid, you can set the ``grid_size`` parameter to
+0 or leave it empty *(and leave the ``wanted_cells`` parameter empty)*.**
+
+### Example
+
+Let's say your driveway camera looks something like this:
+
+![Driveway camera](.git-assets/example_grid.webp)
+
+If you set the ``grid_size`` parameter to 2, the image will be divided into a 2x2 grid like this:
+
+![Driveway camera grid](.git-assets/example_grid_2.webp)
+
+You can see that cell 1 and 2 are empty and cells 3 and 4 might contain license plates.
+You can then set the ``wanted_cells`` parameter to ``3,4`` to only process cells 3 and 4, reducing the processing time
+as only half the image will be processed.
+
 ## Included models in built executable
-When using the built executable, only the **latin** charset models are bundled by default. If you want to use a different 
-charset, you need to set the charset in the JSON_CONFIG variable and rebuild the executable with the according
-models found [here](https://github.com/DoubangoTelecom/ultimateALPR-SDK/tree/master/assets)
-To build the executable, you can use the ``build_alpr_api.sh`` script, which will create an executable named ``alpr_api`` in
-the ``dist`` folder.
+
+When using the built executable, only the **latin** charset models are bundled by default. If you want to use a
+different charset, you need to set the charset in the JSON_CONFIG variable and rebuild the executable with the
+according models found [here](https://github.com/DoubangoTelecom/ultimateALPR-SDK/tree/master/assets)
+To build the executable, you can use the ``build_alpr_api.sh`` script, which will create an executable
+named ``alpr_api`` in the ``dist`` folder.
 
 ## Setup development environment
 
 ### Use automatic setup script
-You can use the ``build_and_setup_ultimatealvr.sh`` script to automatically install the necessary packages and build the ultimateALPR SDK wheel, copy the assets and the libs.
+
 > [!IMPORTANT]
 > Make sure to install the package python3-dev (APT) python3-devel (RPM) before running the build and setup script.
+> You can use the ``build_and_setup_ultimatealvr.sh`` script to automatically install the necessary packages and build
+> the
+> ultimateALPR SDK wheel, copy the assets and the libs.
 
 The end structure should look like this:
+
 ```bash
 .
 ├── alpr_api.py
@@ -74,16 +181,23 @@ The end structure should look like this:
 ```
 
 ### Important notes
-When running, building or developing the script, make sure to set the ``LD_LIBRARY_PATH`` environment variable to the libs folder
+
+When running, building or developing the script, make sure to set the ``LD_LIBRARY_PATH`` environment variable to the
+libs folder
 *(limitation of the ultimateALPR SDK)*.
+
 ```bash
 export LD_LIBRARY_PATH=libs:$LD_LIBRARY_PATH
 ```
 
 ### Error handling
+
 #### GLIBC_ABI_DT_RELR not found
+
 If you encounter an error like this:
+
 ```bash
 /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_ABI_DT_RELR' not found
 ```
+
 Then make sure your GLIBC version is >= 2.36

alpr_api.py

@@ -1,12 +1,15 @@
+import base64
+import io
 import json
 import logging
 import os
 import sys
 import threading
 import time
+import traceback
 
 import ultimateAlprSdk
-from PIL import Image
+from PIL import Image, ImageDraw, ImageFont
 from flask import Flask, request, jsonify, render_template
 
 # Setup logging
@@ -27,9 +30,9 @@ information. The server is created using Flask and the ultimateALPR SDK is used
 See the README.md file for more information on how to run this script.
 """
 
-# Load configuration from a JSON file or environment variables
+# Load configuration
 CONFIG_PATH = os.path.join(bundle_dir,
-                           'config.json')  # TODO: store config file outside of bundle (to avoid compilation by users)
+                           'config.json')  # TODO: store config file outside of bundle (to remove need for compilation by users)
 if os.path.exists(CONFIG_PATH):
     with open(CONFIG_PATH, 'r') as config_file:
         JSON_CONFIG = json.load(config_file)
@@ -38,14 +41,14 @@ else:
         "assets_folder": os.path.join(bundle_dir, "assets"),
         "charset": "latin",
         "car_noplate_detect_enabled": False,
-        "ienv_enabled": False,
+        "ienv_enabled": True,  # night vision enhancements
         "openvino_enabled": True,
         "openvino_device": "CPU",
         "npu_enabled": False,
-        "klass_lpci_enabled": False,
-        "klass_vcr_enabled": False,
-        "klass_vmmr_enabled": False,
-        "klass_vbsr_enabled": False,
+        "klass_lpci_enabled": False,  # License Plate Country Identification
+        "klass_vcr_enabled": False,  # Vehicle Color Recognition (paid)
+        "klass_vmmr_enabled": False,  # Vehicle Make and Model Recognition
+        "klass_vbsr_enabled": False,  # Vehicle Body Style Recognition (paid)
         "license_token_file": "",
         "license_token_data": "",
 
@@ -84,11 +87,11 @@ def start_backend_loop():
         load_engine()
 
         # loop for about an hour or 3000 requests then reload the engine (fix for trial license)
-        while counter < 3000 and time.time() - boot_time < 60 * 60:
+        while counter < 3000 and time.time() - boot_time < 3600:
             # every 120 sec
             if int(time.time()) % 120 == 0:
                 if not is_engine_loaded():
-                    unload_engine()  # just in case
+                    unload_engine()
                     load_engine()
             time.sleep(1)
 
@@ -123,24 +126,16 @@ def process_image(image: Image) -> str:
         counter += 1
 
     width, height = image.size
-
-    if image.mode in IMAGE_TYPES_MAPPING:
-        image_type = IMAGE_TYPES_MAPPING[image.mode]
-    else:
-        raise ValueError("Invalid mode: %s" % image.mode)
+    image_type = IMAGE_TYPES_MAPPING.get(image.mode, None)
+    if image_type is None:
+        raise ValueError(f"Invalid mode: {image.mode}")
 
     result = ultimateAlprSdk.UltAlprSdkEngine_process(
-        image_type,
-        image.tobytes(),
-        width,
-        height,
-        0,  # stride
-        1  # exifOrientation
+        image_type, image.tobytes(), width, height, 0, 1
     )
     if not result.isOK():
-        raise RuntimeError("Process failed: %s" % result.phrase())
-    else:
-        return result.json()
+        raise RuntimeError(f"Process failed: {result.phrase()}")
+    return result.json()
 
 
 def create_rest_server_flask():
@@ -149,44 +144,101 @@ def create_rest_server_flask():
     @app.route('/v1/image/alpr', methods=['POST'])
     def alpr():
         """
-        This function is called when a POST request is made to the /v1/image/alpr endpoint.
         The function receives an image and processes it using the ultimateALPR SDK.
 
         Parameters:
             - upload: The image to be processed
-            - grid_size: The number of cells to split the image into (e.g. 4)
+            - grid_size: The number of cells to split the image into (e.g. 3)
             - wanted_cells: The cells to process in the grid separated by commas (e.g. 1,2,3,4) (max: grid_size²)
+            - whole_image_fallback: If set to true, the whole image will be processed if no plates are found in the specified cells. (default: true)
         """
         interference = time.time()
+        whole_image_fallback = request.form.get('whole_image_fallback', 'true').lower() == 'true'
 
-        if 'upload' not in request.files:
-            return jsonify({'error': 'No image found'}), 400
+        try:
+            if 'upload' not in request.files:
+                return jsonify({'error': 'No image found'}), 400
 
-        grid_size = int(request.form.get('grid_size', 3))
-        wanted_cells = request.form.get('wanted_cells')
-        if wanted_cells:
-            wanted_cells = [int(cell) for cell in wanted_cells.split(',')]
-        else:
-            wanted_cells = list(range(1, grid_size * grid_size + 1))
+            grid_size = int(request.form.get('grid_size', 1))
+            wanted_cells = _get_wanted_cells_from_request(request, grid_size)
 
-        image = request.files['upload']
-        if image.filename == '':
-            return jsonify({'error': 'No selected file'}), 400
+            image_file = request.files['upload']
+            if image_file.filename == '':
+                return jsonify({'error': 'No selected file'}), 400
 
-        image = Image.open(image)
-        result = process_image(image)
-        result = convert_to_cpai_compatible(result)
+            image = _load_image_from_request(request)
 
-        if not result['predictions']:
-            logger.debug("No plate found in the image, attempting to split the image")
-            predictions_found = find_best_plate_with_split(image, grid_size, wanted_cells)
+            result = {
+                'predictions': [],
+                'plates': [],
+                'duration': 0
+            }
 
-            if predictions_found:
-                result['predictions'].append(max(predictions_found, key=lambda x: x['confidence']))
+            if grid_size < 2:
+                logger.debug("Grid size < 2, processing the whole image")
+                response = process_image(image)
+                result.update(_parse_result_from_ultimatealpr(response))
+            else:
+                logger.debug(f"Grid size: {grid_size}, processing specified cells: {wanted_cells}")
+                predictions_found = _find_best_plate_with_grid_split(image, grid_size, wanted_cells)
+                result['predictions'].extend(predictions_found)
 
-        result['processMs'] = round((time.time() - interference) * 1000, 2)
-        result['inferenceMs'] = result['processMs']
-        return jsonify(result)
+            if not result['predictions']:
+                if grid_size >= 2 and whole_image_fallback:
+                    logger.debug("No plates found in the specified cells, trying whole image as last resort")
+                    response = process_image(image)
+                    result.update(_parse_result_from_ultimatealpr(response))
+
+            if result['predictions'] and len(result['predictions']) > 0:
+                all_plates = []
+                for plate in result['predictions']:
+                    all_plates.append(plate.get('plate'))
+                    isolated_plate_image = isolate_plate_in_image(image, plate)
+                    plate['image'] = f"data:image/png;base64,{image_to_base64(isolated_plate_image, compress=True)}"
+
+                result['plates'] = all_plates
+
+            duration = round((time.time() - interference) * 1000, 2)
+            result.update({'duration': duration})
+            return jsonify(result)
+        except Exception as e:
+            logger.error(f"Error processing image: {e}")
+            logger.error(traceback.format_exc())
+            return jsonify({'error': 'Error processing image'}), 500
+
+    @app.route('/v1/image/alpr_grid_debug', methods=['POST'])
+    def alpr_grid_debug():
+        """
+        The function receives an image and returns it with the grid overlayed on it (for debugging purposes).
+
+        Parameters:
+            - upload: The image to be processed
+            - grid_size: The number of cells to split the image into (e.g. 3)
+            - wanted_cells: The cells to process in the grid separated by commas (e.g. 1,2,3,4) (max: grid_size²)
+
+        Returns:
+            - The image with the grid overlayed on it
+        """
+        try:
+            if 'upload' not in request.files:
+                return jsonify({'error': 'No image found'}), 400
+
+            grid_size = int(request.form.get('grid_size', 3))
+            wanted_cells = _get_wanted_cells_from_request(request, grid_size)
+
+            image_file = request.files['upload']
+            if image_file.filename == '':
+                return jsonify({'error': 'No selected file'}), 400
+
+            image = _load_image_from_request(request)
+            image = draw_grid_and_cell_numbers_on_image(image, grid_size, wanted_cells)
+
+            image_base64 = image_to_base64(image, compress=True)
+            return jsonify({"image": f"data:image/png;base64,{image_base64}"})
+        except Exception as e:
+            logger.error(f"Error processing image: {e}")
+            logger.error(traceback.format_exc())
+            return jsonify({'error': 'Error processing image'}), 500
 
     @app.route('/')
     def index():
@@ -195,56 +247,78 @@ def create_rest_server_flask():
     return app
 
 
-def convert_to_cpai_compatible(result):
-    result = json.loads(result)
+def _get_wanted_cells_from_request(request, grid_size) -> list:
+    """
+    Helper function to extract wanted cells from the request.
+    If no cells are specified, it returns all cells in the grid.
+    """
+    wanted_cells = request.form.get('wanted_cells')
+    if wanted_cells:
+        wanted_cells = [int(cell) for cell in wanted_cells.split(',')]
+    else:
+        wanted_cells = list(range(1, grid_size * grid_size + 1))
 
+    if not all(1 <= cell <= grid_size * grid_size for cell in wanted_cells):
+        raise ValueError("Invalid cell numbers provided.")
+
+    return wanted_cells
+
+
+def _load_image_from_request(request) -> Image:
+    """
+    Helper function to load an image from the request.
+    It expects the image to be in the 'upload' field of the request.
+    """
+    if 'upload' not in request.files:
+        raise ValueError("No image found in request.")
+
+    image_file = request.files['upload']
+    if image_file.filename == '':
+        raise ValueError("No selected file.")
+
+    try:
+        image = Image.open(image_file)
+        return correct_image_orientation(image)
+    except Exception as e:
+        raise ValueError(f"Error loading image: {e}")
+
+
+def _parse_result_from_ultimatealpr(result) -> dict:
+    result = json.loads(result)
     response = {
-        'success': "true",
-        'processMs': result['duration'],
-        'inferenceMs': result['duration'],
         'predictions': [],
-        'message': '',
-        'moduleId': 'ALPR',
-        'moduleName': 'License Plate Reader',
-        'code': 200,
-        'command': 'alpr',
-        'requestId': 'null',
-        'inferenceDevice': 'none',
-        'analysisRoundTripMs': 0,
-        'processedBy': 'none',
-        'timestamp': ''
     }
 
-    if 'plates' in result:
-        plates = result['plates']
-        for plate in plates:
-            warpedBox = plate['warpedBox']
-            x_coords = warpedBox[0::2]
-            y_coords = warpedBox[1::2]
-            x_min = min(x_coords)
-            x_max = max(x_coords)
-            y_min = min(y_coords)
-            y_max = max(y_coords)
-
-            response['predictions'].append({
-                'confidence': plate['confidences'][0] / 100,
-                'label': "Plate: " + plate['text'],
-                'plate': plate['text'],
-                'x_min': x_min,
-                'x_max': x_max,
-                'y_min': y_min,
-                'y_max': y_max
-            })
+    for plate in result.get('plates', []):
+        warpedBox = plate['warpedBox']
+        x_coords = warpedBox[0::2]
+        y_coords = warpedBox[1::2]
+        x_min, x_max = min(x_coords), max(x_coords)
+        y_min, y_max = min(y_coords), max(y_coords)
 
+        response['predictions'].append({
+            'confidence': plate['confidences'][0] / 100,
+            'plate': plate['text'],
+            'x_min': x_min,
+            'x_max': x_max,
+            'y_min': y_min,
+            'y_max': y_max
+        })
     return response
 
 
-def find_best_plate_with_split(image: Image, grid_size: int = 3, wanted_cells: list = None):
-    if wanted_cells is None:
-        wanted_cells = list(range(1, grid_size * grid_size + 1))
+def _find_best_plate_with_grid_split(image: Image, grid_size: int = 3, wanted_cells: list = None,
+                                     stop_at_first_match: bool = False) -> list:
+    """
+    Splits the image into a grid and processes each cell to find the best plate.
+    Returns a list of predictions found in the specified cells.
+    """
+
+    if grid_size < 2:
+        logger.debug("Grid size < 2, skipping split")
+        return []
 
     predictions_found = []
-
     width, height = image.size
     cell_width = width // grid_size
     cell_height = height // grid_size
@@ -259,31 +333,120 @@ def find_best_plate_with_split(image: Image, grid_size: int = 3, wanted_cells: l
 
         if cell_index in wanted_cells:
             cell_image = image.crop((left, upper, right, lower))
-            result_cell = json.loads(process_image(cell_image))
+            result = process_image(cell_image)
+            logger.info(f"Processed image with result (grid): {result}")
+            result_cell = json.loads(result)
 
-            if 'plates' in result_cell:
-                for plate in result_cell['plates']:
-                    warpedBox = plate['warpedBox']
-                    x_coords = warpedBox[0::2]
-                    y_coords = warpedBox[1::2]
-                    x_min = min(x_coords) + left
-                    x_max = max(x_coords) + left
-                    y_min = min(y_coords) + upper
-                    y_max = max(y_coords) + upper
+            for plate in result_cell.get('plates', []):
+                warpedBox = plate['warpedBox']
+                x_coords = warpedBox[0::2]
+                y_coords = warpedBox[1::2]
+                x_min = min(x_coords) + left
+                x_max = max(x_coords) + left
+                y_min = min(y_coords) + upper
+                y_max = max(y_coords) + upper
 
-                    predictions_found.append({
-                        'confidence': plate['confidences'][0] / 100,
-                        'label': "Plate: " + plate['text'],
-                        'plate': plate['text'],
-                        'x_min': x_min,
-                        'x_max': x_max,
-                        'y_min': y_min,
-                        'y_max': y_max
-                    })
+                predictions_found.append({
+                    'confidence': plate['confidences'][0] / 100,
+                    'plate': plate['text'],
+                    'x_min': x_min,
+                    'x_max': x_max,
+                    'y_min': y_min,
+                    'y_max': y_max
+                })
+
+                if stop_at_first_match:
+                    logger.debug(f"Found plate in cell {cell_index}: {plate['text']}")
+                    return predictions_found
 
     return predictions_found
 
 
+def draw_grid_and_cell_numbers_on_image(image: Image, grid_size: int = 3, wanted_cells: list = None) -> Image:
+    """
+    Draws a grid on the image and numbers the cells.
+    """
+
+    if grid_size < 1:
+        grid_size = 1
+
+    if wanted_cells is None:
+        wanted_cells = list(range(1, grid_size * grid_size + 1))
+
+    width, height = image.size
+    cell_width = width // grid_size
+    cell_height = height // grid_size
+
+    draw = ImageDraw.Draw(image)
+    font = ImageFont.truetype(os.path.join(bundle_dir, 'assets', 'fonts', 'GlNummernschildEng-XgWd.ttf'),
+                              image.size[0] // 10)
+
+    for cell_index in range(1, grid_size * grid_size + 1):
+        row = (cell_index - 1) // grid_size
+        col = (cell_index - 1) % grid_size
+        left = col * cell_width
+        upper = row * cell_height
+        right = left + cell_width
+        lower = upper + cell_height
+
+        if cell_index in wanted_cells:
+            draw.rectangle([left, upper, right, lower], outline="red", width=4)
+            draw.text((left + 5, upper + 5), str(cell_index), fill="red", font=font)
+
+    return image
+
+
+def isolate_plate_in_image(image: Image, plate: dict, offset=10) -> Image:
+    """
+    Isolates the plate area in the image and returns a cropped and resized image.
+    """
+
+    x_min, x_max = plate.get('x_min'), plate.get('x_max')
+    y_min, y_max = plate.get('y_min'), plate.get('y_max')
+
+    cropped_image = image.crop((max(0, x_min - offset), max(0, y_min - offset), min(image.size[0], x_max + offset),
+                                min(image.size[1], y_max + offset)))
+    resized_image = cropped_image.resize((int(cropped_image.size[0] * 3), int(cropped_image.size[1] * 3)),
+                                         resample=Image.Resampling.LANCZOS)
+
+    return resized_image
+
+
+def image_to_base64(img: Image, compress=False) -> str:
+    """Convert a Pillow image to a base64-encoded string."""
+
+    buffered = io.BytesIO()
+    if compress:
+        img = img.resize((img.size[0] // 2, img.size[1] // 2))
+        img.save(buffered, format="WEBP", quality=35, lossless=False)
+    else:
+        img.save(buffered, format="WEBP")
+
+    return base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+
+from PIL import Image, ExifTags
+
+
+def correct_image_orientation(img):
+    try:
+        exif = img._getexif()
+        if exif is not None:
+            orientation_key = next(
+                (k for k, v in ExifTags.TAGS.items() if v == 'Orientation'), None)
+            if orientation_key is not None:
+                orientation = exif.get(orientation_key)
+                if orientation == 3:
+                    img = img.rotate(180, expand=True)
+                elif orientation == 6:
+                    img = img.rotate(270, expand=True)
+                elif orientation == 8:
+                    img = img.rotate(90, expand=True)
+    except Exception as e:
+        print("EXIF orientation correction failed:", e)
+    return img
+
+
 if __name__ == '__main__':
     engine_thread = threading.Thread(target=start_backend_loop, daemon=True)
     engine_thread.start()

build_alpr_api.sh

@@ -1,2 +1,14 @@
-pyinstaller --noconfirm --console --add-data libs:. --add-data assets:assets --add-data static:static --add-data templates:templates --name easy-local-alpr-1.4.0-openvinocpu_linux_x86_64 "alpr_api.py"
-# optional: --onefile
+#!/bin/bash
+
+VERSION=1.6.0
+
+rm -rf buildenv build dist *.spec
+python3.10 -m venv buildenv
+source buildenv/bin/activate
+python3.10 -m pip install --upgrade pip pyinstaller
+python3.10 -m pip install ./wheel/ultimateAlprSdk-3.14.1-cp310-cp310-linux_x86_64.whl
+pip install -r requirements.txt
+
+pyinstaller --noconfirm --onefile --console --add-data libs:. --add-data assets:assets --add-data static:static --add-data templates:templates --name easy-local-alpr-$VERSION-openvinocpu_linux_x86_64 "alpr_api.py"
+deactivate
+rm -rf buildenv

build_and_setup_ultimatealvr.sh

@@ -1,5 +1,7 @@
 #!/bin/bash
 
+deactivate 2>/dev/null
+
 # Function to create virtual environment, install the wheel, and copy assets and libs
 install_and_setup() {
     echo "Creating virtual environment at the root..."
@@ -55,10 +57,10 @@ prompt_auto_setup() {
     esac
 }
 
-# Directories
+# Variables
 ROOT_DIR=$(pwd)
 BUILD_DIR="$ROOT_DIR/tmp-build-env"
-SDK_ZIP_URL="https://github.com/DoubangoTelecom/ultimateALPR-SDK/archive/8130c76140fe8edc60fe20f875796121a8d22fed.zip"
+SDK_ZIP_URL="https://github.com/DoubangoTelecom/ultimateALPR-SDK/archive/febe9921e7dd37e64901d84cad01d51eca6c6a71.zip" # 3.14.1
 SDK_ZIP="$BUILD_DIR/temp-sdk.zip"
 SDK_DIR="$BUILD_DIR/temp-sdk"
 BIN_DIR="$SDK_DIR/binaries/linux/x86_64"
@@ -69,7 +71,12 @@ cd "$BUILD_DIR" || exit
 
 # Clone SDK
 echo "Downloading SDK..."
-wget "$SDK_ZIP_URL" -O "$SDK_ZIP" >/dev/null 2>&1
+if [ -f "$SDK_ZIP" ]; then
+    echo "SDK zip already exists."
+    rm -R "$SDK_DIR"
+else
+    wget "$SDK_ZIP_URL" -O "$SDK_ZIP" >/dev/null 2>&1
+fi
 if [ $? -ne 0 ]; then
     echo "Failed to download SDK."
     exit 1
@@ -121,26 +128,30 @@ read -r -p "Do you want TensorFlow for CPU or GPU? (cpu/gpu): " tf_choice
 mkdir -p "$BIN_DIR/tensorflow"
 if [ "$tf_choice" == "gpu" ]; then
     echo "Downloading TensorFlow GPU..."
-    wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.6.0.tar.gz >/dev/null 2>&1
+    wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.6.0.tar.gz >/dev/null 2>&1 # Use 2.6 for newer GPU support
     if [ $? -ne 0 ]; then
         echo "Failed to download TensorFlow GPU."
         exit 1
     fi
     echo "Extracting TensorFlow GPU..."
     tar -xf libtensorflow-gpu-linux-x86_64-2.6.0.tar.gz -C "$BIN_DIR/tensorflow" >/dev/null 2>&1
+
+    mv "$BIN_DIR/tensorflow/lib/libtensorflow.so.1" "$BIN_DIR/libs/libtensorflow.so.1"
+    mv "$BIN_DIR/tensorflow/lib/libtensorflow_framework.so.2.6.0" "$BIN_DIR/libs/libtensorflow_framework.so.2"
+
 else
     echo "Downloading TensorFlow CPU..."
-    wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.6.0.tar.gz >/dev/null 2>&1
+    #wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.6.0.tar.gz >/dev/null 2>&1
+    wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.14.0.tar.gz >/dev/null 2>&1 # Use 1.14 as it's smaller in size
     if [ $? -ne 0 ]; then
         echo "Failed to download TensorFlow CPU."
         exit 1
     fi
     echo "Extracting TensorFlow CPU..."
-    tar -xf libtensorflow-cpu-linux-x86_64-2.6.0.tar.gz -C "$BIN_DIR/tensorflow" >/dev/null 2>&1
-fi
+    tar -xf libtensorflow-cpu-linux-x86_64-1.14.0.tar.gz -C "$BIN_DIR/tensorflow" >/dev/null 2>&1
 
-mv "$BIN_DIR/tensorflow/lib/libtensorflow.so.1" "$BIN_DIR/libs/libtensorflow.so.1"
-mv "$BIN_DIR/tensorflow/lib/libtensorflow_framework.so.2.6.0" "$BIN_DIR/libs/libtensorflow_framework.so.2"
+    mv "$BIN_DIR/tensorflow/lib/"* "$BIN_DIR/libs/"
+fi
 
 # Build the wheel
 echo "Building the wheel..."
@@ -157,6 +168,8 @@ mv "$BIN_DIR/plugins.xml" "$BUILD_DIR/libs"
 
 # Move the assets to the root directory
 mv "$SDK_DIR/assets" "$BUILD_DIR/assets"
+# Removes unused models (only keeps TensorFlow and OpenVINO basic license plat recognition)
+rm -Rf "$BUILD_DIR/assets/images" "$BUILD_DIR/assets/models.amlogic_npu" "$BUILD_DIR/assets/models.tensorrt" $BUILD_DIR/assets/models/ultimateALPR-SDK_klass* $BUILD_DIR/assets/models/ultimateALPR-SDK_*mobile* $BUILD_DIR/assets/models/*korean* $BUILD_DIR/assets/models/*chinese* $BUILD_DIR/assets/models/ultimateALPR-SDK_recogn1x100* $BUILD_DIR/assets/models/ultimateALPR-SDK_recogn1x200* $BUILD_DIR/assets/models/ultimateALPR-SDK_recogn1x300* $BUILD_DIR/assets/models/ultimateALPR-SDK_recogn1x400* $BUILD_DIR/assets/models.openvino/ultimateALPR-SDK_klass*
 
 # Deactivate and clean up the build virtual environment
 echo "Deactivating and cleaning up virtual environment..."

requirements.txt

@@ -1,3 +1,3 @@
 flask
-pillow
+Pillow
 ultimateAlprSdk

templates/index.html

@@ -1,11 +1,11 @@
 <!DOCTYPE html>
 <html lang="en">
+
 <head>
     <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <meta content="width=device-width, initial-scale=1.0" name="viewport">
     <title>Easy Local ALPR - API</title>
     <script src="https://cdn.tailwindcss.com"></script>
-    <!-- Include Google Sans font -->
     <link href="https://fonts.googleapis.com/css2?family=Google+Sans:wght@400;500;700&display=swap" rel="stylesheet">
     <style>
         body {
@@ -14,144 +14,343 @@
             background-size: 20px 20px;
             font-family: 'Google Sans', sans-serif;
         }
-        .loading-circle {
-            border: 4px solid rgba(0, 0, 0, 0.1);
-            border-left-color: #000;
-            border-radius: 50%;
-            width: 24px;
-            height: 24px;
-            animation: spin 1s linear infinite;
+
+        .grid-cell {
+            border: 2px solid #e5e7eb; /* Tailwind gray-200 */
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            cursor: pointer;
+            border-radius: 0.5rem; /* Rounded corners */
+            transition: background-color 0.2s ease, color 0.2s ease, border-color 0.2s ease; /* Smooth transition */
+            padding-top: 25%; /* More compact rectangular shape */
+            position: relative;
+            overflow: hidden;
+            box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); /* Subtle shadow for modern look */
         }
-        @keyframes spin {
-            to { transform: rotate(360deg); }
+
+        .grid-cell span {
+            position: absolute;
+            top: 50%;
+            left: 50%;
+            transform: translate(-50%, -50%);
+            font-weight: 500; /* Semi-bold text */
         }
-        input[type="text"], input[type="number"] {
-            background-color: white;
-            color: black;
-        }
-        input[type="text"].dark, input[type="number"].dark {
-            background-color: #3b3b3b;
+
+        .grid-cell.selected {
+            background-color: #1f2937; /* Tailwind gray-800 */
             color: white;
+            border-color: #1f2937; /* Match border color with background */
+        }
+
+        .grid-cell:hover {
+            background-color: #9ca3af; /* Tailwind gray-400 for hover effect */
+            color: white;
+            border-color: #9ca3af; /* Match border color with hover effect */
+        }
+
+        .grid-container {
+            display: grid;
+            grid-template-columns: repeat(auto-fill, minmax(50px, 1fr));
+            gap: 8px; /* Adjust gap between cells */
+            margin-top: 1rem;
         }
     </style>
 </head>
-<body class="bg-neutral-100 dark:bg-neutral-900 dark:text-white flex items-center justify-center min-h-screen p-4">
-    <!-- Logo -->
-    <div class="absolute top-4 left-4 z-50">
-        <img id="logo" src="{{ url_for('static', filename='logo_black.webp') }}" alt="Logo" class="h-12 dark:hidden">
-        <img id="logoDark" src="{{ url_for('static', filename='logo_white.webp') }}" alt="Logo" class="h-12 hidden dark:block">
-    </div>
 
-    <div class="bg-white dark:bg-neutral-800 p-6 rounded-lg shadow-lg w-full max-w-md mt-16">
-        <h1 class="text-2xl font-bold mb-4 text-center dark:text-gray-200">Upload Image for ALPR</h1>
-        <form id="uploadForm" enctype="multipart/form-data" class="space-y-4">
+<body class="bg-neutral-100 dark:bg-neutral-900 dark:text-white flex items-center justify-center min-h-screen p-4">
+<div class="absolute top-4 left-4 z-50">
+    <img alt="Logo" class="h-12 dark:hidden" id="logo" src="{{ url_for('static', filename='logo_black.webp') }}">
+    <img alt="Logo" class="h-12 hidden dark:block" id="logoDark"
+         src="{{ url_for('static', filename='logo_white.webp') }}">
+</div>
+
+<div class="bg-white dark:bg-neutral-800 p-6 rounded-lg shadow-lg w-full max-w-xl mt-16">
+    <h1 class="text-2xl font-bold mb-4 text-center dark:text-gray-200">Select Service</h1>
+    <form class="space-y-4" id="serviceForm">
+        <div>
+            <label class="block text-sm font-medium text-gray-700 dark:text-gray-300" for="service">Choose a
+                service:</label>
+            <select class="mt-1 block w-full py-2 px-3 border border-gray-300 bg-white dark:bg-neutral-800 dark:border-neutral-700 rounded-md shadow-sm focus:outline-none focus:ring-indigo-500 focus:border-indigo-500 sm:text-sm"
+                    id="service" name="service" onchange="updateFormFields()">
+                <option value="alpr">Plate Recognition (ALPR)</option>
+                <option value="alpr_grid_debug">Grid Size Helper</option>
+            </select>
+        </div>
+
+        <div class="service-fields hidden" id="alprFields">
             <div>
-                <label for="upload" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Choose an image:</label>
+                <label for="upload_alpr" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Choose an
+                    image:</label>
                 <div class="mt-1 flex items-center">
-                    <input type="file" id="upload" name="upload" accept="image/*" class="hidden" onchange="updateFileName()">
-                    <label for="upload" class="cursor-pointer inline-flex items-center justify-center px-4 py-2 border border-gray-400 rounded-md shadow-sm text-sm font-medium text-gray-700 dark:text-gray-300 bg-white dark:bg-neutral-800 hover:bg-neutral-50 dark:hover:bg-neutral-600">
-                        Select file
-                    </label>
-                    <span id="fileName" class="ml-2 text-sm text-gray-600 dark:text-gray-300"></span>
+                    <input type="file" id="upload_alpr" name="upload" accept="image/*" class="hidden"
+                           onchange="updateFileName();">
+                    <label for="upload_alpr"
+                           class="cursor-pointer inline-flex items-center justify-center px-4 py-2 border border-gray-400 rounded-md shadow-sm text-sm font-medium text-gray-700 dark:text-gray-300 bg-white dark:bg-neutral-800 hover:bg-neutral-50 dark:hover:bg-neutral-600">Select
+                        file</label>
+                    <span id="fileName_alpr" class="ml-2 text-sm text-gray-600 dark:text-gray-300"></span>
+                </div>
+            </div>
+            <div class="mt-4">
+                <label for="grid_size_alpr" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Grid
+                    Size:</label>
+                <input type="number" id="grid_size_alpr" name="grid_size" value="3"
+                       class="mt-1 block w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm focus:outline-none focus:ring-indigo-500 focus:border-indigo-500 sm:text-sm dark:bg-neutral-800 dark:border-neutral-700">
+            </div>
+            <div class="mt-4">
+                <label for="wanted_cells_alpr" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Wanted
+                    Cells:</label>
+                <div id="gridContainer_alpr" class="grid-container"></div>
+                <input type="hidden" id="wanted_cells_alpr" name="wanted_cells">
+            </div>
+            <div class="mt-4 flex flex-row space-between">
+                <div>
+                    <label for="whole_image_fallback_alpr" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Fallback to whole image if no plate is found in specified cells?</label>
+                <span class="text-sm text-gray-500 dark:text-gray-400">Only applies if grid size >=2</span>
+                </div>
+
+                <div id="gridContainer_alpr" class="grid-container"></div>
+                <input type="checkbox" id="whole_image_fallback_alpr" checked>
+            </div>
+
+            <input id="plate_image_alpr" name="plate_image_alpr" type="hidden" value="true">
+        </div>
+
+        <div class="service-fields hidden" id="alpr_grid_debugFields">
+            <div>
+                <label for="upload_alpr_grid_debug" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Choose
+                    an image:</label>
+                <div class="mt-1 flex items-center">
+                    <input type="file" id="upload_alpr_grid_debug" name="upload" accept="image/*" class="hidden"
+                           onchange="updateFileName();">
+                    <label for="upload_alpr_grid_debug"
+                           class="cursor-pointer inline-flex items-center justify-center px-4 py-2 border border-gray-400 rounded-md shadow-sm text-sm font-medium text-gray-700 dark:text-gray-300 bg-white dark:bg-neutral-800 hover:bg-neutral-50 dark:hover:bg-neutral-600">Select
+                        file</label>
+                    <span id="fileName_alpr_grid_debug" class="ml-2 text-sm text-gray-600 dark:text-gray-300"></span>
                 </div>
             </div>
             <div>
-                <label for="grid_size" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Grid Size:</label>
-                <input type="number" id="grid_size" name="grid_size" value="3" class="mt-1 block w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm focus:outline-none focus:ring-indigo-500 focus:border-indigo-500 sm:text-sm dark">
+                <label for="grid_size_alpr_grid_debug"
+                       class="block text-sm font-medium text-gray-700 dark:text-gray-300">Grid Size:</label>
+                <input type="number" id="grid_size_alpr_grid_debug" name="grid_size" value="1"
+                       class="mt-1 block w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm focus:outline-none focus:ring-indigo-500 focus:border-indigo-500 sm:text-sm dark:bg-neutral-800 dark:border-neutral-700">
             </div>
             <div>
-                <label for="wanted_cells" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Wanted Cells:</label>
-                <input type="text" id="wanted_cells" name="wanted_cells" class="mt-1 block w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm focus:outline-none focus:ring-indigo-500 focus:border-indigo-500 sm:text-sm dark">
+                <label for="wanted_cells_alpr_grid_debug"
+                       class="block text-sm font-medium text-gray-700 dark:text-gray-300">Wanted Cells:</label>
+                <div id="gridContainer_alpr_grid_debug" class="grid-container"></div>
+                <input type="hidden" id="wanted_cells_alpr_grid_debug" name="wanted_cells">
             </div>
-            <div id="imagePreview" class="mt-4 hidden">
-                <img id="previewImage" src="#" alt="Preview" class="max-w-full h-auto rounded-lg">
-            </div>
-            <button id="submitButton" type="submit" class="w-full py-2 px-4 bg-black text-white font-semibold rounded-md shadow-sm hover:bg-neutral-900 dark:bg-neutral-900 dark:hover:bg-neutral-950 relative flex justify-center items-center">
-                <span>Upload</span>
-                <div id="loadingCircle" class="loading-circle absolute hidden"></div>
-            </button>
-        </form>
-        <div class="mt-6">
-            <h2 class="text-xl font-semibold mb-2 dark:text-gray-200">Response</h2>
-            <pre id="responseBox" class="bg-neutral-100 dark:bg-neutral-900 p-4 border rounded-lg text-sm text-gray-900 dark:text-gray-200 overflow-x-auto"></pre>
         </div>
+
+        <div id="imagePreview" class="mt-4 hidden">
+            <label id="imagePreviewLabel"
+                   class="block text-sm font-medium text-gray-700 dark:text-gray-300 mb-2">Identified plate images:</label>
+            <div id="previewImageContainer" class="grid grid-cols-1 sm:grid-cols-2 gap-4"></div>
+            <img id="previewImageDebug" src="#" alt="" class="max-w-full h-auto rounded-lg">
+        </div>
+
+
+        <button class="w-full py-2 px-4 bg-black text-white font-semibold rounded-md shadow-sm hover:bg-neutral-900 dark:bg-neutral-900 dark:hover:bg-neutral-950"
+                id="submitButton" type="submit">Submit
+        </button>
+    </form>
+
+    <div class="mt-6">
+        <h2 class="text-xl font-semibold mb-2 dark:text-gray-200">
+            Response
+            <span class="text-sm font-normal" id="timer"></span>
+        </h2>
+        <pre class="bg-neutral-100 dark:bg-neutral-900 p-4 border rounded-lg text-sm text-gray-900 dark:text-gray-200 overflow-x-auto"
+             id="responseBox"></pre>
     </div>
+</div>
 
-    <script src="https://code.jquery.com/jquery-3.5.1.min.js"></script>
-    <script>
-        function updateFileName() {
-            var input = document.getElementById('upload');
-            var fileName = document.getElementById('fileName');
-            var imagePreview = document.getElementById('imagePreview');
-            var previewImage = document.getElementById('previewImage');
+<script src="https://code.jquery.com/jquery-3.5.1.min.js"></script>
+<script>
+    function updateFormFields() {
+        const service = document.getElementById('service').value;
+        localStorage.setItem('selectedService', service);
 
-            fileName.textContent = input.files[0] ? input.files[0].name : '';
+        document.querySelectorAll('.service-fields').forEach(field => {
+            field.classList.add('hidden');
+            field.querySelectorAll('input, select').forEach(field => field.disabled = true);
+        });
 
-            if (input.files && input.files[0]) {
-                var reader = new FileReader();
+        const selectedServiceFields = document.getElementById(service + 'Fields');
+        selectedServiceFields.classList.remove('hidden');
+        selectedServiceFields.querySelectorAll('input, select').forEach(field => field.disabled = false);
 
-                reader.onload = function (e) {
-                    previewImage.src = e.target.result;
-                    imagePreview.classList.remove('hidden');
+        ['responseBox', 'timer', 'fileName_' + service, 'previewImage', 'imagePreview', 'upload_' + service]
+            .forEach(id => {
+                const element = document.getElementById(id);
+                if (element) {
+                    if (element.tagName === 'DIV') element.classList.add('hidden');
+                    if (element.tagName === 'INPUT') element.value = '';
+                    if (element.tagName === 'SPAN' || element.tagName === 'PRE') element.textContent = '';
+                    if (element.tagName === 'IMG') element.src = '';
                 }
+            });
 
-                reader.readAsDataURL(input.files[0]);
-            }
+        updateGrid(service);
+    }
+
+    function initializeForm() {
+        const savedService = localStorage.getItem('selectedService');
+        if (savedService) {
+            document.getElementById('service').value = savedService;
+            updateFormFields();
         }
+    }
 
-        const prefersDarkScheme = window.matchMedia("(prefers-color-scheme: dark)");
-        function toggleLogo() {
-            const logo = document.getElementById('logo');
-            const logoDark = document.getElementById('logoDark');
-            const upload = document.getElementById('upload');
-            const gridSize = document.getElementById('grid_size');
-            const wantedCells = document.getElementById('wanted_cells');
-
-            if (prefersDarkScheme.matches) {
-                logo.style.display = 'none';
-                logoDark.style.display = 'block';
-                upload.classList.add('dark');
-                gridSize.classList.add('dark');
-                wantedCells.classList.add('dark');
-            } else {
-                logo.style.display = 'block';
-                logoDark.style.display = 'none';
-                upload.classList.remove('dark');
-                gridSize.classList.remove('dark');
-                wantedCells.classList.remove('dark');
-            }
+    function toggleLogo() {
+        const logo = document.getElementById('logo');
+        const logoDark = document.getElementById('logoDark');
+        if (window.matchMedia("(prefers-color-scheme: dark)").matches) {
+            logo.style.display = 'none';
+            logoDark.style.display = 'block';
+        } else {
+            logo.style.display = 'block';
+            logoDark.style.display = 'none';
         }
+    }
 
+    function updateFileName() {
+        const service = document.getElementById('service').value;
+        const input = document.getElementById('upload_' + service);
+        const fileName = document.getElementById('fileName_' + service);
+        const imagePreview = document.getElementById('imagePreview');
+        const previewImage = document.getElementById('previewImage');
+        const imagePreviewLabel = document.getElementById('imagePreviewLabel');
+
+        fileName.textContent = input.files[0] ? input.files[0].name : '';
+        imagePreviewLabel.textContent = 'Preview:';
+
+        if (input.files && input.files[0]) {
+            const reader = new FileReader();
+            reader.onload = (e) => {
+                previewImage.src = e.target.result;
+                imagePreview.classList.remove('hidden');
+            }
+            reader.readAsDataURL(input.files[0]);
+        }
+    }
+
+    function updateGrid(service) {
+        const gridSize = parseInt(document.getElementById('grid_size_' + service).value);
+        const gridContainer = document.getElementById('gridContainer_' + service);
+        gridContainer.innerHTML = '';
+        gridContainer.style.gridTemplateColumns = `repeat(${gridSize}, minmax(0, 1fr))`;
+        const wantedCellsInput = document.getElementById('wanted_cells_' + service);
+        const selectedCells = wantedCellsInput.value ? wantedCellsInput.value.split(',').map(Number) : [];
+
+        for (let i = 0; i < gridSize * gridSize; i++) {
+            const cell = document.createElement('div');
+            cell.classList.add('grid-cell');
+
+            const cellSpan = document.createElement('span');
+            cellSpan.textContent = i + 1;
+            cell.appendChild(cellSpan);
+
+            if (selectedCells.includes(i + 1)) cell.classList.add('selected');
+            cell.addEventListener('click', () => {
+                cell.classList.toggle('selected');
+                updateWantedCells(service);
+            });
+            gridContainer.appendChild(cell);
+        }
+    }
+
+    function updateWantedCells(service) {
+        const gridContainer = document.getElementById('gridContainer_' + service);
+        const selectedCells = [];
+        gridContainer.querySelectorAll('.grid-cell.selected').forEach(cell => {
+            selectedCells.push(cell.textContent);
+        });
+        document.getElementById('wanted_cells_' + service).value = selectedCells.join(',');
+    }
+
+    $(document).ready(function () {
+        initializeForm();
         toggleLogo();
-        prefersDarkScheme.addEventListener('change', toggleLogo);
+        window.matchMedia("(prefers-color-scheme: dark)").addEventListener('change', toggleLogo);
 
-        $(document).ready(function () {
-            $('#uploadForm').on('submit', function (e) {
-                e.preventDefault();
-                var formData = new FormData(this);
-                $('#submitButton').prop('disabled', true);
-                $('#loadingCircle').removeClass('hidden');
+        $('#grid_size_alpr, #grid_size_alpr_grid_debug').on('input', function () {
+            updateGrid(document.getElementById('service').value);
+        });
 
-                $.ajax({
-                    url: '/v1/image/alpr',
-                    type: 'POST',
-                    data: formData,
-                    processData: false,
-                    contentType: false,
-                    success: function (data) {
-                        $('#responseBox').text(JSON.stringify(data, null, 2));
-                    },
-                    error: function (xhr, status, error) {
-                        var err = JSON.parse(xhr.responseText);
-                        $('#responseBox').text(JSON.stringify(err, null, 2));
-                    },
-                    complete: function () {
-                        $('#submitButton').prop('disabled', false);
-                        $('#loadingCircle').addClass('hidden');
+        $('#serviceForm').on('submit', function (e) {
+            e.preventDefault();
+            const service = $('#service').val();
+            const formData = new FormData(this);
+            formData.append('whole_image_fallback', $("#whole_image_fallback_alpr").is(":checked") ? "true" : "false");
+            var url;
+            if (service === 'alpr') {
+                url = '/v1/image/alpr';
+                type = 'POST';
+            } else if (service === 'alpr_grid_debug') {
+                url = '/v1/image/alpr_grid_debug';
+                type = 'POST';
+            }
+            $('#submitButton').prop('disabled', true).html('<svg class="animate-spin h-5 w-5 text-white mx-auto" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none"><circle class="opacity-25" cx="12" cy="12" r="10" stroke="currentColor" stroke-width="4"></circle><path class="opacity-75" fill="currentColor" d="M12 2a10 10 0 00-8 4.9l1.5 1A8 8 0 0112 4V2z"></path></svg>');
+
+            const startTime = Date.now();
+
+            $.ajax({
+                url: url,
+                type: type,
+                data: formData,
+                processData: false,
+                contentType: false,
+                success: function (data) {
+                    const endTime = Date.now();
+                    const elapsedTime = endTime - startTime;
+                    $('#responseBox').text(JSON.stringify(data, null, 2));
+                    $('#timer').text(`(${elapsedTime} ms)`);
+                    $('#submitButton').prop('disabled', false).text('Submit');
+
+                    $('#previewImageDebug').attr('src', '');
+                    $('#previewImageContainer').empty();
+
+                    if (data.image) {
+                        $('#previewImageDebug').attr('src', data.image);
+                        $('#imagePreview').removeClass('hidden');
                     }
-                });
+
+                    if (Array.isArray(data.predictions) && data.predictions.length > 0) {
+                        data.predictions.forEach((prediction, index) => {
+                            if (prediction.image) {
+                                const img = $('<img>')
+                                    .attr('src', prediction.image)
+                                    .addClass('max-w-full h-auto rounded-lg border border-gray-300 dark:border-gray-700 shadow');
+
+                                const wrapper = $('<div>').append(
+                                    $('<p>').addClass('text-sm mb-1 text-gray-600 dark:text-gray-300').text(`Plate ${index + 1}`),
+                                    img
+                                );
+
+                                $('#previewImageContainer').append(wrapper);
+                            }
+                        });
+
+                        $('#imagePreview').removeClass('hidden');
+                        $('#imagePreviewLabel').text('Identified plate images:');
+                    } else {
+                        updateFileName(); // fallback if no images found
+                    }
+                },
+                error: function (xhr) {
+                    const endTime = Date.now();
+                    const elapsedTime = endTime - startTime;
+                    const err = JSON.parse(xhr.responseText);
+                    $('#responseBox').text(JSON.stringify(err, null, 2));
+                    $('#timer').text(`(${elapsedTime} ms)`);
+                    $('#submitButton').prop('disabled', false).text('Submit');
+                }
             });
         });
-    </script>
+    });
+</script>
 </body>
+
 </html>