# Ultralytics YOLO 🚀, AGPL-3.0 license
import argparse
import cv2
import numpy as np
from tflite_runtime import interpreter as tflite
from ultralytics.utils import ASSETS, yaml_load
from ultralytics.utils.checks import check_yaml
# Declare as global variables, can be updated based trained model image size
img_width = 640
img_height = 640
class LetterBox:
def __init__(
self, new_shape=(img_width, img_height), auto=False, scaleFill=False, scaleup=True, center=True, stride=32
):
self.new_shape = new_shape
self.auto = auto
self.scaleFill = scaleFill
self.scaleup = scaleup
self.stride = stride
self.center = center # Put the image in the middle or top-left
def __call__(self, labels=None, image=None):
"""Return updated labels and image with added border."""
if labels is None:
labels = {}
img = labels.get("img") if image is None else image
shape = img.shape[:2] # current shape [height, width]
new_shape = labels.pop("rect_shape", self.new_shape)
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not self.scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if self.auto: # minimum rectangle
dw, dh = np.mod(dw, self.stride), np.mod(dh, self.stride) # wh padding
elif self.scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
if self.center:
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)) if self.center else 0, int(round(dh + 0.1))
left, right = int(round(dw - 0.1)) if self.center else 0, int(round(dw + 0.1))
img = cv2.copyMakeBorder(
img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)
) # add border
if labels.get("ratio_pad"):
labels["ratio_pad"] = (labels["ratio_pad"], (left, top)) # for evaluation
if len(labels):
labels = self._update_labels(labels, ratio, dw, dh)
labels["img"] = img
labels["resized_shape"] = new_shape
return labels
else:
return img
def _update_labels(self, labels, ratio, padw, padh):
"""Update labels."""
labels["instances"].convert_bbox(format="xyxy")
labels["instances"].denormalize(*labels["img"].shape[:2][::-1])
labels["instances"].scale(*ratio)
labels["instances"].add_padding(padw, padh)
return labels
class Yolov8TFLite:
def __init__(self, tflite_model, input_image, confidence_thres, iou_thres):
"""
Initializes an instance of the Yolov8TFLite class.
Args:
tflite_model: Path to the TFLite model.
input_image: Path to the input image.
confidence_thres: Confidence threshold for filtering detections.
iou_thres: IoU (Intersection over Union) threshold for non-maximum suppression.
"""
self.tflite_model = tflite_model
self.input_image = input_image
self.confidence_thres = confidence_thres
self.iou_thres = iou_thres
# Load the class names from the COCO dataset
self.classes = yaml_load(check_yaml("coco128.yaml"))["names"]
# Generate a color palette for the classes
self.color_palette = np.random.uniform(0, 255, size=(len(self.classes), 3))
def draw_detections(self, img, box, score, class_id):
"""
Draws bounding boxes and labels on the input image based on the detected objects.
Args:
img: The input image to draw detections on.
box: Detected bounding box.
score: Corresponding detection score.
class_id: Class ID for the detected object.
Returns:
None
"""
# Extract the coordinates of the bounding box
x1, y1, w, h = box
# Retrieve the color for the class ID
color = self.color_palette[class_id]
# Draw the bounding box on the image
cv2.rectangle(img, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color, 2)
# Create the label text with class name and score
label = f"{self.classes[class_id]}: {score:.2f}"
# Calculate the dimensions of the label text
(label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
# Calculate the position of the label text
label_x = x1
label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10
# Draw a filled rectangle as the background for the label text
cv2.rectangle(
img,
(int(label_x), int(label_y - label_height)),
(int(label_x + label_width), int(label_y + label_height)),
color,
cv2.FILLED,
)
# Draw the label text on the image
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):
"""
Preprocesses the input image before performing inference.
Returns:
image_data: Preprocessed image data ready for inference.
"""
# Read the input image using OpenCV
self.img = cv2.imread(self.input_image)
print("image before", self.img)
# Get the height and width of the input image
self.img_height, self.img_width = self.img.shape[:2]
letterbox = LetterBox(new_shape=[img_width, img_height], auto=False, stride=32)
image = letterbox(image=self.img)
image = [image]
image = np.stack(image)
image = image[..., ::-1].transpose((0, 3, 1, 2))
img = np.ascontiguousarray(image)
# n, h, w, c
image = img.astype(np.float32)
return image / 255
def postprocess(self, input_image, output):
"""
Performs post-processing on the model's output to extract bounding boxes, scores, and class IDs.
Args:
input_image (numpy.ndarray): The input image.
output (numpy.ndarray): The output of the model.
Returns:
numpy.ndarray: The input image with detections drawn on it.
"""
boxes = []
scores = []
class_ids = []
for pred in output:
pred = np.transpose(pred)
for box in pred:
x, y, w, h = box[:4]
x1 = x - w / 2
y1 = y - h / 2
boxes.append([x1, y1, w, h])
idx = np.argmax(box[4:])
scores.append(box[idx + 4])
class_ids.append(idx)
indices = cv2.dnn.NMSBoxes(boxes, scores, self.confidence_thres, self.iou_thres)
for i in indices:
# Get the box, score, and class ID corresponding to the index
box = boxes[i]
gain = min(img_width / self.img_width, img_height / self.img_height)
pad = (
round((img_width - self.img_width * gain) / 2 - 0.1),
round((img_height - self.img_height * gain) / 2 - 0.1),
)
box[0] = (box[0] - pad[0]) / gain
box[1] = (box[1] - pad[1]) / gain
box[2] = box[2] / gain
box[3] = box[3] / gain
score = scores[i]
class_id = class_ids[i]
if score > 0.25:
print(box, score, class_id)
# Draw the detection on the input image
self.draw_detections(input_image, box, score, class_id)
return input_image
def main(self):
"""
Performs inference using a TFLite model and returns the output image with drawn detections.
Returns:
output_img: The output image with drawn detections.
"""
# Create an interpreter for the TFLite model
interpreter = tflite.Interpreter(model_path=self.tflite_model)
self.model = interpreter
interpreter.allocate_tensors()
# Get the model inputs
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Store the shape of the input for later use
input_shape = input_details[0]["shape"]
self.input_width = input_shape[1]
self.input_height = input_shape[2]
# Preprocess the image data
img_data = self.preprocess()
img_data = img_data
# img_data = img_data.cpu().numpy()
# Set the input tensor to the interpreter
print(input_details[0]["index"])
print(img_data.shape)
img_data = img_data.transpose((0, 2, 3, 1))
scale, zero_point = input_details[0]["quantization"]
interpreter.set_tensor(input_details[0]["index"], img_data)
# Run inference
interpreter.invoke()
# Get the output tensor from the interpreter
output = interpreter.get_tensor(output_details[0]["index"])
scale, zero_point = output_details[0]["quantization"]
output = (output.astype(np.float32) - zero_point) * scale
output[:, [0, 2]] *= img_width
output[:, [1, 3]] *= img_height
print(output)
# Perform post-processing on the outputs to obtain output image.
return self.postprocess(self.img, output)
if __name__ == "__main__":
# Create an argument parser to handle command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument(
"--model", type=str, default="yolov8n_full_integer_quant.tflite", help="Input your TFLite model."
)
parser.add_argument("--img", type=str, default=str(ASSETS / "bus.jpg"), help="Path to input image.")
parser.add_argument("--conf-thres", type=float, default=0.5, help="Confidence threshold")
parser.add_argument("--iou-thres", type=float, default=0.5, help="NMS IoU threshold")
args = parser.parse_args()
# Create an instance of the Yolov8TFLite class with the specified arguments
detection = Yolov8TFLite(args.model, args.img, args.conf_thres, args.iou_thres)
# Perform object detection and obtain the output image
output_image = detection.main()
# Display the output image in a window
cv2.imshow("Output", output_image)
# Wait for a key press to exit
cv2.waitKey(0)