From 472b0ef5d2f1b4a0f0f18e725e3d08bb0d8f4793 Mon Sep 17 00:00:00 2001
From: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
Date: Thu, 24 Oct 2024 16:50:22 -0500
Subject: [PATCH] initial foundation for alpr with paddleocr

---
 .cspell/frigate-dictionary.txt       |   4 +-
 docker/main/requirements-wheels.txt  |   3 +
 frigate/embeddings/alpr/alpr.py      | 858 +++++++++++++++++++++++++++
 frigate/embeddings/functions/onnx.py |  26 +
 4 files changed, 890 insertions(+), 1 deletion(-)
 create mode 100644 frigate/embeddings/alpr/alpr.py

diff --git a/.cspell/frigate-dictionary.txt b/.cspell/frigate-dictionary.txt
index b019f8492..2849bc2ef 100644
--- a/.cspell/frigate-dictionary.txt
+++ b/.cspell/frigate-dictionary.txt
@@ -2,6 +2,7 @@ aarch
 absdiff
 airockchip
 Alloc
+alpr
 Amcrest
 amdgpu
 analyzeduration
@@ -186,6 +187,7 @@ openai
 opencv
 openvino
 OWASP
+paddleocr
 paho
 passwordless
 popleft
@@ -305,4 +307,4 @@ yolo
 yolonas
 yolox
 zeep
-zerolatency
+zerolatency
\ No newline at end of file
diff --git a/docker/main/requirements-wheels.txt b/docker/main/requirements-wheels.txt
index c48b99c75..1d9244f1d 100644
--- a/docker/main/requirements-wheels.txt
+++ b/docker/main/requirements-wheels.txt
@@ -45,3 +45,6 @@ openai == 1.51.*
 # push notifications
 py-vapid == 1.9.*
 pywebpush == 2.0.*
+# alpr
+pyclipper == 1.3.*
+shapely == 2.0.*
diff --git a/frigate/embeddings/alpr/alpr.py b/frigate/embeddings/alpr/alpr.py
new file mode 100644
index 000000000..e8c039542
--- /dev/null
+++ b/frigate/embeddings/alpr/alpr.py
@@ -0,0 +1,858 @@
+import math
+from argparse import ArgumentParser
+from typing import Any, Dict, List, Tuple
+
+import cv2
+import numpy as np
+from pyclipper import ET_CLOSEDPOLYGON, JT_ROUND, PyclipperOffset
+from shapely.geometry import Polygon
+
+from frigate.comms.inter_process import InterProcessRequestor
+from frigate.embeddings.functions.onnx import GenericONNXEmbedding, ModelTypeEnum
+
+
+class LicensePlateRecognition:
+    def __init__(self, config: Dict[str, Any], requestor: InterProcessRequestor):
+        self.config = config
+        self.requestor = requestor
+        self.detection_model = self._create_detection_model()
+        self.classification_model = self._create_classification_model()
+        self.recognition_model = self._create_recognition_model()
+        self.ctc_decoder = CTCDecoder()
+
+        self.batch_size = 6
+
+        # Detection specific parameters
+        self.min_size = 3
+        self.max_size = 960
+        self.box_thresh = 0.8
+        self.mask_thresh = 0.8
+        self.mean = np.array([123.675, 116.28, 103.53]).reshape(1, -1).astype("float64")
+        self.std = 1 / np.array([58.395, 57.12, 57.375]).reshape(1, -1).astype(
+            "float64"
+        )
+
+    def _create_detection_model(self) -> GenericONNXEmbedding:
+        return GenericONNXEmbedding(
+            model_name="paddleocr-onnx",
+            model_file="detection.onnx",
+            download_urls={
+                "detection.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/detection.onnx"
+            },
+            model_size="large",
+            model_type=ModelTypeEnum.alpr_detect,
+            requestor=self.requestor,
+            device="CPU",
+        )
+
+    def _create_classification_model(self) -> GenericONNXEmbedding:
+        return GenericONNXEmbedding(
+            model_name="paddleocr-onnx",
+            model_file="classification.onnx",
+            download_urls={
+                "classification.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/classification.onnx"
+            },
+            model_size="large",
+            model_type=ModelTypeEnum.alpr_classify,
+            requestor=self.requestor,
+            device="CPU",
+        )
+
+    def _create_recognition_model(self) -> GenericONNXEmbedding:
+        return GenericONNXEmbedding(
+            model_name="paddleocr-onnx",
+            model_file="recognition.onnx",
+            download_urls={
+                "recognition.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/recognition.onnx"
+            },
+            model_size="large",
+            model_type=ModelTypeEnum.alpr_recognize,
+            requestor=self.requestor,
+            device="CPU",
+        )
+
+    def detect(self, image: np.ndarray) -> List[np.ndarray]:
+        """
+        Detect possible license plates in the input image by first resizing and normalizing it,
+        running a detection model, and filtering out low-probability regions.
+
+        Args:
+            image (np.ndarray): The input image in which license plates will be detected.
+
+        Returns:
+            List[np.ndarray]: A list of bounding box coordinates representing detected license plates.
+        """
+        h, w = image.shape[:2]
+
+        if sum([h, w]) < 64:
+            image = self.zero_pad(image)
+
+        resized_image = self.resize_image(image)
+        normalized_image = self.normalize_image(resized_image)
+
+        outputs = self.detection_model([normalized_image])[0]
+        outputs = outputs[0, :, :]
+
+        boxes, _ = self.boxes_from_bitmap(outputs, outputs > self.mask_thresh, w, h)
+        return self.filter_polygon(boxes, (h, w))
+
+    def classify(
+        self, images: List[np.ndarray]
+    ) -> Tuple[List[np.ndarray], List[Tuple[str, float]]]:
+        """
+        Classify the orientation or category of each detected license plate.
+
+        Args:
+            images (List[np.ndarray]): A list of images of detected license plates.
+
+        Returns:
+            Tuple[List[np.ndarray], List[Tuple[str, float]]]: A tuple of rotated/normalized plate images
+                                                            and classification results with confidence scores.
+        """
+        num_images = len(images)
+        indices = np.argsort([x.shape[1] / x.shape[0] for x in images])
+
+        for i in range(0, num_images, self.batch_size):
+            norm_images = []
+            for j in range(i, min(num_images, i + self.batch_size)):
+                norm_img = self._preprocess_classification_image(images[indices[j]])
+                norm_img = norm_img[np.newaxis, :]
+                norm_images.append(norm_img)
+
+        outputs = self.classification_model(norm_images)
+
+        return self._process_classification_output(images, outputs)
+
+    def recognize(
+        self, images: List[np.ndarray]
+    ) -> Tuple[List[str], List[List[float]]]:
+        """
+        Recognize the characters on the detected license plates using the recognition model.
+
+        Args:
+            images (List[np.ndarray]): A list of images of license plates to recognize.
+
+        Returns:
+            Tuple[List[str], List[List[float]]]: A tuple of recognized license plate texts and confidence scores.
+        """
+        input_shape = [3, 48, 320]
+        num_images = len(images)
+
+        # sort images by aspect ratio for processing
+        indices = np.argsort(np.array([x.shape[1] / x.shape[0] for x in images]))
+
+        for index in range(0, num_images, self.batch_size):
+            input_h, input_w = input_shape[1], input_shape[2]
+            max_wh_ratio = input_w / input_h
+            norm_images = []
+
+            # calculate the maximum aspect ratio in the current batch
+            for i in range(index, min(num_images, index + self.batch_size)):
+                h, w = images[indices[i]].shape[0:2]
+                max_wh_ratio = max(max_wh_ratio, w * 1.0 / h)
+
+            # preprocess the images based on the max aspect ratio
+            for i in range(index, min(num_images, index + self.batch_size)):
+                norm_image = self._preprocess_recognition_image(
+                    images[indices[i]], max_wh_ratio
+                )
+                norm_image = norm_image[np.newaxis, :]
+                norm_images.append(norm_image)
+
+        outputs = self.recognition_model(norm_images)
+        return self.ctc_decoder(outputs)
+
+    def process_license_plate(
+        self, image: np.ndarray
+    ) -> Tuple[List[str], List[float], List[int]]:
+        """
+        Complete pipeline for detecting, classifying, and recognizing license plates in the input image.
+
+        Args:
+            image (np.ndarray): The input image in which to detect, classify, and recognize license plates.
+
+        Returns:
+            Tuple[List[str], List[float], List[int]]: Detected license plate texts, confidence scores, and areas of the plates.
+        """
+        plate_points = self.detect(image)
+        if len(plate_points) == 0:
+            return [], [], []
+
+        plate_points = self.sort_polygon(list(plate_points))
+        plate_images = [self._crop_license_plate(image, x) for x in plate_points]
+        rotated_images, _ = self.classify(plate_images)
+
+        # keep track of the index of each image for correct area calc later
+        sorted_indices = np.argsort([x.shape[1] / x.shape[0] for x in rotated_images])
+        reverse_mapping = {
+            idx: original_idx for original_idx, idx in enumerate(sorted_indices)
+        }
+
+        results, confidences = self.recognize(rotated_images)
+
+        if results:
+            license_plates = [""] * len(rotated_images)
+            average_confidences = [0.0] * len(rotated_images)
+            areas = [0] * len(rotated_images)
+
+            # map results back to original image order
+            for i, (plate, conf) in enumerate(zip(results, confidences)):
+                original_idx = reverse_mapping[i]
+
+                height, width = rotated_images[original_idx].shape[:2]
+                area = height * width
+
+                average_confidence = sum(conf) / len(conf) if conf else 0
+
+                # TODO: remove
+                if False:
+                    save_image = cv2.cvtColor(
+                        rotated_images[original_idx], cv2.COLOR_RGB2BGR
+                    )
+                    filename = f"/config/plate_{original_idx}_{plate}_{area}.jpg"
+                    cv2.imwrite(filename, save_image)
+
+                license_plates[original_idx] = plate
+                average_confidences[original_idx] = average_confidence
+                areas[original_idx] = area
+
+            return license_plates, average_confidences, areas
+
+        return [], [], []
+
+    def resize_image(self, image: np.ndarray) -> np.ndarray:
+        """
+        Resize the input image while maintaining the aspect ratio, ensuring dimensions are multiples of 32.
+
+        Args:
+            image (np.ndarray): The input image to resize.
+
+        Returns:
+            np.ndarray: The resized image.
+        """
+        h, w = image.shape[:2]
+        ratio = min(self.max_size / max(h, w), 1.0)
+        resize_h = max(int(round(int(h * ratio) / 32) * 32), 32)
+        resize_w = max(int(round(int(w * ratio) / 32) * 32), 32)
+        return cv2.resize(image, (resize_w, resize_h))
+
+    def normalize_image(self, image: np.ndarray) -> np.ndarray:
+        """
+        Normalize the input image by subtracting the mean and multiplying by the standard deviation.
+
+        Args:
+            image (np.ndarray): The input image to normalize.
+
+        Returns:
+            np.ndarray: The normalized image, transposed to match the model's expected input format.
+        """
+        image = image.astype("float32")
+        cv2.subtract(image, self.mean, image)
+        cv2.multiply(image, self.std, image)
+        return image.transpose((2, 0, 1))[np.newaxis, ...]
+
+    def boxes_from_bitmap(
+        self, output: np.ndarray, mask: np.ndarray, dest_width: int, dest_height: int
+    ) -> Tuple[np.ndarray, List[float]]:
+        """
+        Process the binary mask to extract bounding boxes and associated confidence scores.
+
+        Args:
+            output (np.ndarray): Output confidence map from the model.
+            mask (np.ndarray): Binary mask of detected regions.
+            dest_width (int): Target width for scaling the box coordinates.
+            dest_height (int): Target height for scaling the box coordinates.
+
+        Returns:
+            Tuple[np.ndarray, List[float]]: Array of bounding boxes and list of corresponding scores.
+        """
+
+        mask = (mask * 255).astype(np.uint8)
+        height, width = mask.shape
+        outs = cv2.findContours(mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+
+        # handle different return values of findContours between OpenCV versions
+        contours = outs[0] if len(outs) == 2 else outs[1]
+
+        boxes = []
+        scores = []
+
+        for index in range(len(contours)):
+            contour = contours[index]
+
+            # get minimum bounding box (rotated rectangle) around the contour and the smallest side length.
+            points, min_side = self.get_min_boxes(contour)
+
+            if min_side < self.min_size:
+                continue
+
+            points = np.array(points)
+
+            score = self.box_score(output, contour)
+            if self.box_thresh > score:
+                continue
+
+            polygon = Polygon(points)
+            distance = polygon.area / polygon.length
+
+            # Use pyclipper to shrink the polygon slightly based on the computed distance.
+            offset = PyclipperOffset()
+            offset.AddPath(points, JT_ROUND, ET_CLOSEDPOLYGON)
+            points = np.array(offset.Execute(distance * 1.5)).reshape((-1, 1, 2))
+
+            # get the minimum bounding box around the shrunken polygon.
+            box, min_side = self.get_min_boxes(points)
+
+            if min_side < self.min_size + 2:
+                continue
+
+            box = np.array(box)
+
+            # normalize and clip box coordinates to fit within the destination image size.
+            box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width)
+            box[:, 1] = np.clip(
+                np.round(box[:, 1] / height * dest_height), 0, dest_height
+            )
+
+            boxes.append(box.astype("int32"))
+            scores.append(score)
+
+        return np.array(boxes, dtype="int32"), scores
+
+    @staticmethod
+    def get_min_boxes(contour: np.ndarray) -> Tuple[List[Tuple[float, float]], float]:
+        """
+        Calculate the minimum bounding box (rotated rectangle) for a given contour.
+
+        Args:
+            contour (np.ndarray): The contour points of the detected shape.
+
+        Returns:
+            Tuple[List[Tuple[float, float]], float]: A list of four points representing the
+            corners of the bounding box, and the length of the shortest side.
+        """
+        bounding_box = cv2.minAreaRect(contour)
+        points = sorted(cv2.boxPoints(bounding_box), key=lambda x: x[0])
+        index_1, index_4 = (0, 1) if points[1][1] > points[0][1] else (1, 0)
+        index_2, index_3 = (2, 3) if points[3][1] > points[2][1] else (3, 2)
+        box = [points[index_1], points[index_2], points[index_3], points[index_4]]
+        return box, min(bounding_box[1])
+
+    @staticmethod
+    def box_score(bitmap: np.ndarray, contour: np.ndarray) -> float:
+        """
+        Calculate the average score within the bounding box of a contour.
+
+        Args:
+            bitmap (np.ndarray): The output confidence map from the model.
+            contour (np.ndarray): The contour of the detected shape.
+
+        Returns:
+            float: The average score of the pixels inside the contour region.
+        """
+        h, w = bitmap.shape[:2]
+        contour = contour.reshape(-1, 2)
+        x1, y1 = np.clip(contour.min(axis=0), 0, [w - 1, h - 1])
+        x2, y2 = np.clip(contour.max(axis=0), 0, [w - 1, h - 1])
+        mask = np.zeros((y2 - y1 + 1, x2 - x1 + 1), dtype=np.uint8)
+        cv2.fillPoly(mask, [contour - [x1, y1]], 1)
+        return cv2.mean(bitmap[y1 : y2 + 1, x1 : x2 + 1], mask)[0]
+
+    @staticmethod
+    def expand_box(points: List[Tuple[float, float]]) -> np.ndarray:
+        """
+        Expand a polygonal shape slightly by a factor determined by the area-to-perimeter ratio.
+
+        Args:
+            points (List[Tuple[float, float]]): Points of the polygon to expand.
+
+        Returns:
+            np.ndarray: Expanded polygon points.
+        """
+        polygon = Polygon(points)
+        distance = polygon.area / polygon.length
+        offset = PyclipperOffset()
+        offset.AddPath(points, JT_ROUND, ET_CLOSEDPOLYGON)
+        expanded = np.array(offset.Execute(distance * 1.5)).reshape((-1, 2))
+        return expanded
+
+    def filter_polygon(
+        self, points: List[np.ndarray], shape: Tuple[int, int]
+    ) -> np.ndarray:
+        """
+        Filter a set of polygons to include only valid ones that fit within an image shape
+        and meet size constraints.
+
+        Args:
+            points (List[np.ndarray]): List of polygons to filter.
+            shape (Tuple[int, int]): Shape of the image (height, width).
+
+        Returns:
+            np.ndarray: List of filtered polygons.
+        """
+        height, width = shape
+        return np.array(
+            [
+                self.clockwise_order(point)
+                for point in points
+                if self.is_valid_polygon(point, width, height)
+            ]
+        )
+
+    @staticmethod
+    def is_valid_polygon(point: np.ndarray, width: int, height: int) -> bool:
+        """
+        Check if a polygon is valid, meaning it fits within the image bounds
+        and has sides of a minimum length.
+
+        Args:
+            point (np.ndarray): The polygon to validate.
+            width (int): Image width.
+            height (int): Image height.
+
+        Returns:
+            bool: Whether the polygon is valid or not.
+        """
+        return (
+            point[:, 0].min() >= 0
+            and point[:, 0].max() < width
+            and point[:, 1].min() >= 0
+            and point[:, 1].max() < height
+            and np.linalg.norm(point[0] - point[1]) > 3
+            and np.linalg.norm(point[0] - point[3]) > 3
+        )
+
+    @staticmethod
+    def clockwise_order(point: np.ndarray) -> np.ndarray:
+        """
+        Arrange the points of a polygon in clockwise order based on their angular positions
+        around the polygon's center.
+
+        Args:
+            point (np.ndarray): Array of points of the polygon.
+
+        Returns:
+            np.ndarray: Points ordered in clockwise direction.
+        """
+        center = point.mean(axis=0)
+        return point[
+            np.argsort(np.arctan2(point[:, 1] - center[1], point[:, 0] - center[0]))
+        ]
+
+    @staticmethod
+    def sort_polygon(points):
+        """
+        Sort polygons based on their position in the image. If polygons are close in vertical
+        position (within 10 pixels), sort them by horizontal position.
+
+        Args:
+            points: List of polygons to sort.
+
+        Returns:
+            List: Sorted list of polygons.
+        """
+        points.sort(key=lambda x: (x[0][1], x[0][0]))
+        for i in range(len(points) - 1):
+            for j in range(i, -1, -1):
+                if abs(points[j + 1][0][1] - points[j][0][1]) < 10 and (
+                    points[j + 1][0][0] < points[j][0][0]
+                ):
+                    temp = points[j]
+                    points[j] = points[j + 1]
+                    points[j + 1] = temp
+                else:
+                    break
+        return points
+
+    @staticmethod
+    def zero_pad(image: np.ndarray) -> np.ndarray:
+        """
+        Apply zero-padding to an image, ensuring its dimensions are at least 32x32.
+        The padding is added only if needed.
+
+        Args:
+            image (np.ndarray): Input image.
+
+        Returns:
+            np.ndarray: Zero-padded image.
+        """
+        h, w, c = image.shape
+        pad = np.zeros((max(32, h), max(32, w), c), np.uint8)
+        pad[:h, :w, :] = image
+        return pad
+
+    @staticmethod
+    def _preprocess_classification_image(image: np.ndarray) -> np.ndarray:
+        """
+        Preprocess a single image for classification by resizing, normalizing, and padding.
+
+        This method resizes the input image to a fixed height of 48 pixels while adjusting
+        the width dynamically up to a maximum of 192 pixels. The image is then normalized and
+        padded to fit the required input dimensions for classification.
+
+        Args:
+            image (np.ndarray): Input image to preprocess.
+
+        Returns:
+            np.ndarray: Preprocessed and padded image.
+        """
+        # fixed height of 48, dynamic width up to 192
+        input_shape = (3, 48, 192)
+        input_c, input_h, input_w = input_shape
+
+        h, w = image.shape[:2]
+        ratio = w / h
+        resized_w = min(input_w, math.ceil(input_h * ratio))
+
+        resized_image = cv2.resize(image, (resized_w, input_h))
+
+        # handle single-channel images (grayscale) if needed
+        if input_c == 1 and resized_image.ndim == 2:
+            resized_image = resized_image[np.newaxis, :, :]
+        else:
+            resized_image = resized_image.transpose((2, 0, 1))
+
+        # normalize
+        resized_image = (resized_image.astype("float32") / 255.0 - 0.5) / 0.5
+
+        padded_image = np.zeros((input_c, input_h, input_w), dtype=np.float32)
+        padded_image[:, :, :resized_w] = resized_image
+
+        return padded_image
+
+    def _process_classification_output(
+        self, images: List[np.ndarray], outputs: List[np.ndarray]
+    ) -> Tuple[List[np.ndarray], List[Tuple[str, float]]]:
+        """
+        Process the classification model output by matching labels with confidence scores.
+
+        This method processes the outputs from the classification model and rotates images
+        with high confidence of being labeled "180". It ensures that results are mapped to
+        the original image order.
+
+        Args:
+            images (List[np.ndarray]): List of input images.
+            outputs (List[np.ndarray]): Corresponding model outputs.
+
+        Returns:
+            Tuple[List[np.ndarray], List[Tuple[str, float]]]: A tuple of processed images and
+            classification results (label and confidence score).
+        """
+        labels = ["0", "180"]
+        results = [["", 0.0]] * len(images)
+        indices = np.argsort(np.array([x.shape[1] / x.shape[0] for x in images]))
+
+        outputs = np.stack(outputs)
+
+        outputs = [
+            (labels[idx], outputs[i, idx])
+            for i, idx in enumerate(outputs.argmax(axis=1))
+        ]
+
+        for i in range(0, len(images), self.batch_size):
+            for j in range(len(outputs)):
+                label, score = outputs[j]
+                results[indices[i + j]] = [label, score]
+                if "180" in label and score > self.config.get(
+                    "classification_threshold", 0.98
+                ):
+                    images[indices[i + j]] = cv2.rotate(images[indices[i + j]], 1)
+
+        return images, results
+
+    def _preprocess_recognition_image(
+        self, image: np.ndarray, max_wh_ratio: float
+    ) -> np.ndarray:
+        """
+        Preprocess an image for recognition by dynamically adjusting its width.
+
+        This method adjusts the width of the image based on the maximum width-to-height ratio
+        while keeping the height fixed at 48 pixels. The image is then normalized and padded
+        to fit the required input dimensions for recognition.
+
+        Args:
+            image (np.ndarray): Input image to preprocess.
+            max_wh_ratio (float): Maximum width-to-height ratio for resizing.
+
+        Returns:
+            np.ndarray: Preprocessed and padded image.
+        """
+        # fixed height of 48, dynamic width based on ratio
+        input_shape = [3, 48, 320]
+        input_h, input_w = input_shape[1], input_shape[2]
+
+        assert image.shape[2] == input_shape[0], "Unexpected number of image channels."
+
+        # dynamically adjust input width based on max_wh_ratio
+        input_w = int(input_h * max_wh_ratio)
+
+        # check for model-specific input width
+        model_input_w = self.recognition_model.runner.ort.get_inputs()[0].shape[3]
+        if isinstance(model_input_w, int) and model_input_w > 0:
+            input_w = model_input_w
+
+        h, w = image.shape[:2]
+        aspect_ratio = w / h
+        resized_w = min(input_w, math.ceil(input_h * aspect_ratio))
+
+        resized_image = cv2.resize(image, (resized_w, input_h))
+        resized_image = resized_image.transpose((2, 0, 1))
+        resized_image = (resized_image.astype("float32") / 255.0 - 0.5) / 0.5
+
+        padded_image = np.zeros((input_shape[0], input_h, input_w), dtype=np.float32)
+        padded_image[:, :, :resized_w] = resized_image
+
+        return padded_image
+
+    @staticmethod
+    def _crop_license_plate(image: np.ndarray, points: np.ndarray) -> np.ndarray:
+        """
+        Crop the license plate from the image using four corner points.
+
+        This method crops the region containing the license plate by using the perspective
+        transformation based on four corner points. If the resulting image is significantly
+        taller than wide, the image is rotated to the correct orientation.
+
+        Args:
+            image (np.ndarray): Input image containing the license plate.
+            points (np.ndarray): Four corner points defining the plate's position.
+
+        Returns:
+            np.ndarray: Cropped and potentially rotated license plate image.
+        """
+        assert len(points) == 4, "shape of points must be 4*2"
+        points = points.astype(np.float32)
+        crop_width = int(
+            max(
+                np.linalg.norm(points[0] - points[1]),
+                np.linalg.norm(points[2] - points[3]),
+            )
+        )
+        crop_height = int(
+            max(
+                np.linalg.norm(points[0] - points[3]),
+                np.linalg.norm(points[1] - points[2]),
+            )
+        )
+        pts_std = np.float32(
+            [[0, 0], [crop_width, 0], [crop_width, crop_height], [0, crop_height]]
+        )
+        matrix = cv2.getPerspectiveTransform(points, pts_std)
+        image = cv2.warpPerspective(
+            image,
+            matrix,
+            (crop_width, crop_height),
+            borderMode=cv2.BORDER_REPLICATE,
+            flags=cv2.INTER_CUBIC,
+        )
+        height, width = image.shape[0:2]
+        if height * 1.0 / width >= 1.5:
+            image = np.rot90(image, k=3)
+        return image
+
+
+class CTCDecoder:
+    """
+    A decoder for interpreting the output of a CTC (Connectionist Temporal Classification) model.
+
+    This decoder converts the model's output probabilities into readable sequences of characters
+    while removing duplicates and handling blank tokens. It also calculates the confidence scores
+    for each decoded character sequence.
+    """
+
+    def __init__(self):
+        """
+        Initialize the CTCDecoder with a list of characters and a character map.
+
+        The character set includes digits, letters, special characters, and a "blank" token
+        (used by the CTC model for decoding purposes). A character map is created to map
+        indices to characters.
+        """
+        self.characters = [
+            "blank",
+            "0",
+            "1",
+            "2",
+            "3",
+            "4",
+            "5",
+            "6",
+            "7",
+            "8",
+            "9",
+            ":",
+            ";",
+            "<",
+            "=",
+            ">",
+            "?",
+            "@",
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+            "H",
+            "I",
+            "J",
+            "K",
+            "L",
+            "M",
+            "N",
+            "O",
+            "P",
+            "Q",
+            "R",
+            "S",
+            "T",
+            "U",
+            "V",
+            "W",
+            "X",
+            "Y",
+            "Z",
+            "[",
+            "\\",
+            "]",
+            "^",
+            "_",
+            "`",
+            "a",
+            "b",
+            "c",
+            "d",
+            "e",
+            "f",
+            "g",
+            "h",
+            "i",
+            "j",
+            "k",
+            "l",
+            "m",
+            "n",
+            "o",
+            "p",
+            "q",
+            "r",
+            "s",
+            "t",
+            "u",
+            "v",
+            "w",
+            "x",
+            "y",
+            "z",
+            "{",
+            "|",
+            "}",
+            "~",
+            "!",
+            '"',
+            "#",
+            "$",
+            "%",
+            "&",
+            "'",
+            "(",
+            ")",
+            "*",
+            "+",
+            ",",
+            "-",
+            ".",
+            "/",
+            " ",
+            " ",
+        ]
+        self.char_map = {i: char for i, char in enumerate(self.characters)}
+
+    def __call__(
+        self, outputs: List[np.ndarray]
+    ) -> Tuple[List[str], List[List[float]]]:
+        """
+        Decode a batch of model outputs into character sequences and their confidence scores.
+
+        The method takes the output probability distributions for each time step and uses
+        the best path decoding strategy. It then merges repeating characters and ignores
+        blank tokens. Confidence scores for each decoded character are also calculated.
+
+        Args:
+            outputs (List[np.ndarray]): A list of model outputs, where each element is
+                                        a probability distribution for each time step.
+
+        Returns:
+            Tuple[List[str], List[List[float]]]: A tuple of decoded character sequences
+                                                and confidence scores for each sequence.
+        """
+        results = []
+        confidences = []
+        for output in outputs:
+            seq_log_probs = np.log(output + 1e-8)
+            best_path = np.argmax(seq_log_probs, axis=1)
+
+            merged_path = []
+            merged_probs = []
+            for t, char_index in enumerate(best_path):
+                if char_index != 0 and (t == 0 or char_index != best_path[t - 1]):
+                    merged_path.append(char_index)
+                    merged_probs.append(seq_log_probs[t, char_index])
+
+            result = "".join(self.char_map[idx] for idx in merged_path)
+            results.append(result)
+
+            confidence = np.exp(merged_probs).tolist()
+            confidences.append(confidence)
+
+        return results, confidences
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument("filepath", type=str, help="image file path")
+    args = parser.parse_args()
+
+    lpr_config = {
+        "detection_model_path": "/workspace/frigate/frigate/embeddings/weights/detection.onnx",
+        "classification_model_path": "/workspace/frigate/frigate/embeddings/weights/classification.onnx",
+        "recognition_model_path": "/workspace/frigate/frigate/embeddings/weights/recognition.onnx",
+        "mask_thresh": 0.8,
+        "box_thresh": 0.8,
+        "min_size": 3,
+        "classification_threshold": 0.98,
+    }
+
+    # Initialize LPR
+    license_plate_recognition = LicensePlateRecognition(lpr_config, {})
+
+    # Read and process image
+    frame = cv2.imread(args.filepath)
+    if frame is None:
+        print(f"Error: Could not read image file: {args.filepath}")
+        return
+
+    cv2.cvtColor(frame, cv2.COLOR_BGR2RGB, frame)
+
+    # Process the license plate
+    license_plates, confidences, areas = (
+        license_plate_recognition.process_license_plate(frame)
+    )
+
+    # Print debug information to ensure data structure
+    print(f"License plates: {license_plates}")
+    print(f"Confidences: {confidences}")
+    print(f"Areas: {areas}")
+
+    if license_plates:
+        for plate, confidence, area in zip(license_plates, confidences, areas):
+            print(
+                f"Detected license plate: {plate} (average confidence: {confidence:.2f}, area: {area} pixels)"
+            )
+    else:
+        print("No license plate detected")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/frigate/embeddings/functions/onnx.py b/frigate/embeddings/functions/onnx.py
index 9fc71d502..b0cae62e2 100644
--- a/frigate/embeddings/functions/onnx.py
+++ b/frigate/embeddings/functions/onnx.py
@@ -38,6 +38,9 @@ class ModelTypeEnum(str, Enum):
     face = "face"
     vision = "vision"
     text = "text"
+    alpr_detect = "alpr_detect"
+    alpr_classify = "alpr_classify"
+    alpr_recognize = "alpr_recognize"
 
 
 class GenericONNXEmbedding:
@@ -139,6 +142,12 @@ class GenericONNXEmbedding:
                 self.feature_extractor = self._load_feature_extractor()
             elif self.model_type == ModelTypeEnum.face:
                 self.feature_extractor = []
+            elif self.model_type == ModelTypeEnum.alpr_detect:
+                self.feature_extractor = []
+            elif self.model_type == ModelTypeEnum.alpr_classify:
+                self.feature_extractor = []
+            elif self.model_type == ModelTypeEnum.alpr_recognize:
+                self.feature_extractor = []
 
             self.runner = ONNXModelRunner(
                 os.path.join(self.download_path, self.model_file),
@@ -214,9 +223,26 @@ class GenericONNXEmbedding:
 
             frame = np.expand_dims(frame, axis=0)
             return [{"image_input": frame}]
+        elif self.model_type == ModelTypeEnum.alpr_detect:
+            preprocessed = []
+            for x in raw_inputs:
+                preprocessed.append(x)
+            return [{"x": preprocessed[0]}]
+        elif self.model_type == ModelTypeEnum.alpr_classify:
+            processed = []
+            for img in raw_inputs:
+                processed.append({"x": img})
+            return processed
+        elif self.model_type == ModelTypeEnum.alpr_recognize:
+            processed = []
+            for img in raw_inputs:
+                processed.append({"x": img})
+            return processed
         else:
             raise ValueError(f"Unable to preprocess inputs for {self.model_type}")
 
+    # for face recognition
+
     def _process_image(self, image, output: str = "RGB") -> Image.Image:
         if isinstance(image, str):
             if image.startswith("http"):