# Ultralytics YOLO 🚀, AGPL-3.0 license import argparse from typing import Tuple, Union import cv2 import numpy as np import tensorflow as tf import yaml from ultralytics.utils import ASSETS try: from tflite_runtime.interpreter import Interpreter except ImportError: import tensorflow as tf Interpreter = tf.lite.Interpreter class YOLOv8TFLite: """ YOLOv8TFLite. A class for performing object detection using the YOLOv8 model with TensorFlow Lite. Attributes: model (str): Path to the TensorFlow Lite model file. conf (float): Confidence threshold for filtering detections. iou (float): Intersection over Union threshold for non-maximum suppression. metadata (Optional[str]): Path to the metadata file, if any. Methods: detect(img_path: str) -> np.ndarray: Performs inference and returns the output image with drawn detections. """ def __init__(self, model: str, conf: float = 0.25, iou: float = 0.45, metadata: Union[str, None] = None): """ Initializes an instance of the YOLOv8TFLite class. Args: model (str): Path to the TFLite model. conf (float, optional): Confidence threshold for filtering detections. Defaults to 0.25. iou (float, optional): IoU (Intersection over Union) threshold for non-maximum suppression. Defaults to 0.45. metadata (Union[str, None], optional): Path to the metadata file or None if not used. Defaults to None. """ self.conf = conf self.iou = iou if metadata is None: self.classes = {i: i for i in range(1000)} else: with open(metadata) as f: self.classes = yaml.safe_load(f)["names"] np.random.seed(42) self.color_palette = np.random.uniform(128, 255, size=(len(self.classes), 3)) self.model = Interpreter(model_path=model) self.model.allocate_tensors() input_details = self.model.get_input_details()[0] self.in_width, self.in_height = input_details["shape"][1:3] self.in_index = input_details["index"] self.in_scale, self.in_zero_point = input_details["quantization"] self.int8 = input_details["dtype"] == np.int8 output_details = self.model.get_output_details()[0] self.out_index = output_details["index"] self.out_scale, self.out_zero_point = output_details["quantization"] def letterbox(self, img: np.ndarray, new_shape: Tuple = (640, 640)) -> Tuple[np.ndarray, Tuple[float, float]]: """Resizes and reshapes images while maintaining aspect ratio by adding padding, suitable for YOLO models.""" shape = img.shape[:2] # current shape [height, width] # Scale ratio (new / old) r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) # Compute padding new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) dw, dh = (new_shape[1] - new_unpad[0]) / 2, (new_shape[0] - new_unpad[1]) / 2 # wh padding if shape[::-1] != new_unpad: # resize img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)) return img, (top / img.shape[0], left / img.shape[1]) def draw_detections(self, img: np.ndarray, box: np.ndarray, score: np.float32, class_id: int) -> None: """ Draws bounding boxes and labels on the input image based on the detected objects. Args: img (np.ndarray): The input image to draw detections on. box (np.ndarray): Detected bounding box in the format [x1, y1, width, height]. score (np.float32): Corresponding detection score. class_id (int): Class ID for the detected object. Returns: None """ x1, y1, w, h = box color = self.color_palette[class_id] cv2.rectangle(img, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color, 2) label = f"{self.classes[class_id]}: {score:.2f}" (label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1) label_x = x1 label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10 cv2.rectangle( img, (int(label_x), int(label_y - label_height)), (int(label_x + label_width), int(label_y + label_height)), color, cv2.FILLED, ) cv2.putText(img, label, (int(label_x), int(label_y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA) def preprocess(self, img: np.ndarray) -> Tuple[np.ndarray, Tuple[float, float]]: """ Preprocesses the input image before performing inference. Args: img (np.ndarray): The input image to be preprocessed. Returns: Tuple[np.ndarray, Tuple[float, float]]: A tuple containing: - The preprocessed image (np.ndarray). - A tuple of two float values representing the padding applied (top/bottom, left/right). """ img, pad = self.letterbox(img, (self.in_width, self.in_height)) img = img[..., ::-1][None] # N,H,W,C for TFLite img = np.ascontiguousarray(img) img = img.astype(np.float32) return img / 255, pad def postprocess(self, img: np.ndarray, outputs: np.ndarray, pad: Tuple[float, float]) -> np.ndarray: """ Performs post-processing on the model's output to extract bounding boxes, scores, and class IDs. Args: img (numpy.ndarray): The input image. outputs (numpy.ndarray): The output of the model. pad (Tuple[float, float]): Padding used by letterbox. Returns: numpy.ndarray: The input image with detections drawn on it. """ outputs[:, 0] -= pad[1] outputs[:, 1] -= pad[0] outputs[:, :4] *= max(img.shape) outputs = outputs.transpose(0, 2, 1) outputs[..., 0] -= outputs[..., 2] / 2 outputs[..., 1] -= outputs[..., 3] / 2 for out in outputs: scores = out[:, 4:].max(-1) keep = scores > self.conf boxes = out[keep, :4] scores = scores[keep] class_ids = out[keep, 4:].argmax(-1) indices = cv2.dnn.NMSBoxes(boxes, scores, self.conf, self.iou).flatten() [self.draw_detections(img, boxes[i], scores[i], class_ids[i]) for i in indices] return img def detect(self, img_path: str) -> np.ndarray: """ Performs inference using a TFLite model and returns the output image with drawn detections. Args: img_path (str): The path to the input image file. Returns: np.ndarray: The output image with drawn detections. """ img = cv2.imread(img_path) x, pad = self.preprocess(img) if self.int8: x = (x / self.in_scale + self.in_zero_point).astype(np.int8) self.model.set_tensor(self.in_index, x) self.model.invoke() y = self.model.get_tensor(self.out_index) if self.int8: y = (y.astype(np.float32) - self.out_zero_point) * self.out_scale return self.postprocess(img, y, pad) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--model", type=str, default="yolov8n_saved_model/yolov8n_full_integer_quant.tflite", help="Path to TFLite model.", ) parser.add_argument("--img", type=str, default=str(ASSETS / "bus.jpg"), help="Path to input image") parser.add_argument("--conf", type=float, default=0.25, help="Confidence threshold") parser.add_argument("--iou", type=float, default=0.45, help="NMS IoU threshold") parser.add_argument("--metadata", type=str, default="yolov8n_saved_model/metadata.yaml", help="Metadata yaml") args = parser.parse_args() detector = YOLOv8TFLite(args.model, args.conf, args.iou, args.metadata) result = detector.detect(str(ASSETS / "bus.jpg"))[..., ::-1] cv2.imshow("Output", result) cv2.waitKey(0)