Refactor TFLite example. Support FP32, Fp16, INT8 models (#17317)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Mohammed Yasin
GitHub
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5 changed files with 277 additions and 374 deletions
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examples/YOLOv8-TFLite-Python/main.py
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examples/YOLOv8-TFLite-Python/main.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import argparse
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from typing import Tuple, Union
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import cv2
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import numpy as np
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import tensorflow as tf
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import yaml
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from ultralytics.utils import ASSETS
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try:
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from tflite_runtime.interpreter import Interpreter
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except ImportError:
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import tensorflow as tf
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Interpreter = tf.lite.Interpreter
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class YOLOv8TFLite:
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"""
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YOLOv8TFLite.
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A class for performing object detection using the YOLOv8 model with TensorFlow Lite.
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Attributes:
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model (str): Path to the TensorFlow Lite model file.
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conf (float): Confidence threshold for filtering detections.
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iou (float): Intersection over Union threshold for non-maximum suppression.
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metadata (Optional[str]): Path to the metadata file, if any.
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Methods:
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detect(img_path: str) -> np.ndarray:
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Performs inference and returns the output image with drawn detections.
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"""
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def __init__(self, model: str, conf: float = 0.25, iou: float = 0.45, metadata: Union[str, None] = None):
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"""
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Initializes an instance of the YOLOv8TFLite class.
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Args:
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model (str): Path to the TFLite model.
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conf (float, optional): Confidence threshold for filtering detections. Defaults to 0.25.
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iou (float, optional): IoU (Intersection over Union) threshold for non-maximum suppression. Defaults to 0.45.
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metadata (Union[str, None], optional): Path to the metadata file or None if not used. Defaults to None.
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"""
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self.conf = conf
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self.iou = iou
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if metadata is None:
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self.classes = {i: i for i in range(1000)}
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else:
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with open(metadata) as f:
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self.classes = yaml.safe_load(f)["names"]
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np.random.seed(42)
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self.color_palette = np.random.uniform(128, 255, size=(len(self.classes), 3))
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self.model = Interpreter(model_path=model)
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self.model.allocate_tensors()
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input_details = self.model.get_input_details()[0]
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self.in_width, self.in_height = input_details["shape"][1:3]
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self.in_index = input_details["index"]
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self.in_scale, self.in_zero_point = input_details["quantization"]
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self.int8 = input_details["dtype"] == np.int8
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output_details = self.model.get_output_details()[0]
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self.out_index = output_details["index"]
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self.out_scale, self.out_zero_point = output_details["quantization"]
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def letterbox(self, img: np.ndarray, new_shape: Tuple = (640, 640)) -> Tuple[np.ndarray, Tuple[float, float]]:
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"""Resizes and reshapes images while maintaining aspect ratio by adding padding, suitable for YOLO models."""
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shape = img.shape[:2] # current shape [height, width]
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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# Compute padding
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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dw, dh = (new_shape[1] - new_unpad[0]) / 2, (new_shape[0] - new_unpad[1]) / 2 # wh padding
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if shape[::-1] != new_unpad: # resize
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img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114))
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return img, (top / img.shape[0], left / img.shape[1])
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def draw_detections(self, img: np.ndarray, box: np.ndarray, score: np.float32, class_id: int) -> None:
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"""
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Draws bounding boxes and labels on the input image based on the detected objects.
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Args:
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img (np.ndarray): The input image to draw detections on.
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box (np.ndarray): Detected bounding box in the format [x1, y1, width, height].
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score (np.float32): Corresponding detection score.
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class_id (int): Class ID for the detected object.
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Returns:
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None
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"""
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x1, y1, w, h = box
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color = self.color_palette[class_id]
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cv2.rectangle(img, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color, 2)
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label = f"{self.classes[class_id]}: {score:.2f}"
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(label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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label_x = x1
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label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10
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cv2.rectangle(
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img,
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(int(label_x), int(label_y - label_height)),
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(int(label_x + label_width), int(label_y + label_height)),
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color,
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cv2.FILLED,
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)
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cv2.putText(img, label, (int(label_x), int(label_y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
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def preprocess(self, img: np.ndarray) -> Tuple[np.ndarray, Tuple[float, float]]:
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"""
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Preprocesses the input image before performing inference.
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Args:
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img (np.ndarray): The input image to be preprocessed.
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Returns:
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Tuple[np.ndarray, Tuple[float, float]]: A tuple containing:
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- The preprocessed image (np.ndarray).
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- A tuple of two float values representing the padding applied (top/bottom, left/right).
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"""
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img, pad = self.letterbox(img, (self.in_width, self.in_height))
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img = img[..., ::-1][None] # N,H,W,C for TFLite
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img = np.ascontiguousarray(img)
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img = img.astype(np.float32)
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return img / 255, pad
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def postprocess(self, img: np.ndarray, outputs: np.ndarray, pad: Tuple[float, float]) -> np.ndarray:
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"""
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Performs post-processing on the model's output to extract bounding boxes, scores, and class IDs.
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Args:
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img (numpy.ndarray): The input image.
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outputs (numpy.ndarray): The output of the model.
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pad (Tuple[float, float]): Padding used by letterbox.
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Returns:
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numpy.ndarray: The input image with detections drawn on it.
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"""
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outputs[:, 0] -= pad[1]
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outputs[:, 1] -= pad[0]
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outputs[:, :4] *= max(img.shape)
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outputs = outputs.transpose(0, 2, 1)
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outputs[..., 0] -= outputs[..., 2] / 2
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outputs[..., 1] -= outputs[..., 3] / 2
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for out in outputs:
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scores = out[:, 4:].max(-1)
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keep = scores > self.conf
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boxes = out[keep, :4]
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scores = scores[keep]
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class_ids = out[keep, 4:].argmax(-1)
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indices = cv2.dnn.NMSBoxes(boxes, scores, self.conf, self.iou).flatten()
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[self.draw_detections(img, boxes[i], scores[i], class_ids[i]) for i in indices]
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return img
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def detect(self, img_path: str) -> np.ndarray:
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"""
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Performs inference using a TFLite model and returns the output image with drawn detections.
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Args:
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img_path (str): The path to the input image file.
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Returns:
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np.ndarray: The output image with drawn detections.
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"""
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img = cv2.imread(img_path)
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x, pad = self.preprocess(img)
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if self.int8:
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x = (x / self.in_scale + self.in_zero_point).astype(np.int8)
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self.model.set_tensor(self.in_index, x)
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self.model.invoke()
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y = self.model.get_tensor(self.out_index)
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if self.int8:
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y = (y.astype(np.float32) - self.out_zero_point) * self.out_scale
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return self.postprocess(img, y, pad)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model",
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type=str,
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default="yolov8n_saved_model/yolov8n_full_integer_quant.tflite",
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help="Path to TFLite model.",
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)
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parser.add_argument("--img", type=str, default=str(ASSETS / "bus.jpg"), help="Path to input image")
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parser.add_argument("--conf", type=float, default=0.25, help="Confidence threshold")
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parser.add_argument("--iou", type=float, default=0.45, help="NMS IoU threshold")
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parser.add_argument("--metadata", type=str, default="yolov8n_saved_model/metadata.yaml", help="Metadata yaml")
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args = parser.parse_args()
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detector = YOLOv8TFLite(args.model, args.conf, args.iou, args.metadata)
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result = detector.detect(str(ASSETS / "bus.jpg"))[..., ::-1]
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cv2.imshow("Output", result)
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cv2.waitKey(0)
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