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yolo_manager.py
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yolo_manager.py
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import torch
import tensorflow as tf
from ultralytics import YOLO
from ultralytics.engine.results import Results
from ultralytics.utils import ops # for postprocess
from pathlib import Path
import cv2
import numpy as np
try:
from pycoral.utils.edgetpu import make_interpreter
from pycoral.adapters import common
except ModuleNotFoundError as m_err:
pass
# .pt files contains names in there but exported onnx/tflite don't have them.
yolo_default_label_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train',
7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign',
12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse',
18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe',
24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee',
30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat',
35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana',
47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog',
53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant',
59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse',
65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster',
71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
class YoloDetector:
def __init__(self, model_path, task='detect'):
self.model = YOLO(model_path, task=task)
self.imgsz = 640 # assume 640 at the moment since it is the default one
if model_path.suffix == '.onnx':
# once exported to onnx, auto resizing doesn't seem to work as expected
# probably there is a better way but I'll just read it from onnx file
# and set the dimension when predict
# note, square images only atm
import onnx
dummy_model = onnx.load(str(model_path))
self.imgsz = dummy_model.graph.input[0].type.tensor_type.shape.dim[-1].dim_value
del dummy_model
def predict(self, frame, conf):
return self.model.predict(source=frame, save=False, conf=conf, save_txt=False, show=False, verbose=False,
imgsz=self.imgsz)
def get_label_names(self):
if self.model.names is None or len(self.model.names) == 0:
return yolo_default_label_names
return self.model.names
class YoloDetectorTFLite:
def __init__(self, model_path, use_coral_tpu=False):
self.name = model_path.name
self.use_coral_tpu = use_coral_tpu
if use_coral_tpu:
# only use coral tpu interpreter if specified
self.interpreter = make_interpreter(str(model_path))
else:
# use normal tf.lite
self.interpreter = tf.lite.Interpreter(model_path=str(model_path))
self.interpreter.allocate_tensors()
def predict(self, frame, conf):
orig_imgs = [frame]
# Get input and output tensors.
input_details = self.interpreter.get_input_details()
output_details = self.interpreter.get_output_details()
# Test the model on random input data.
input_shape = input_details[0]['shape']
# TODO check shape of input_shape and frame.shape
# input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
_, w, h, _ = input_shape
# check width and height
if frame.shape[0] != h or frame.shape[1] != w:
input_img = cv2.resize(frame, (w, h))
else:
input_img = frame
input_img = input_img[np.newaxis, ...] # add batch dim
if self.use_coral_tpu:
params = common.input_details(self.interpreter, 'quantization_parameters')
scale = params['scales']
zero_point = params['zero_points']
input_mean = 128.
input_std = 128.
normalized_input = (input_img - input_mean) / (input_std * scale) + zero_point
np.clip(normalized_input, 0, 255, out=normalized_input)
common.set_input(self.interpreter, normalized_input.astype(np.uint8))
else:
input_img = input_img.astype(np.float32) / 255. # change to float img
self.interpreter.set_tensor(input_details[0]['index'], input_img)
self.interpreter.invoke()
preds = self.interpreter.get_tensor(output_details[0]['index'])
if self.use_coral_tpu:
output_details = self.interpreter.get_output_details()[0]
if np.issubdtype(preds.dtype, np.integer):
scale, zero_point = output_details['quantization']
# Always convert to np.int64 to avoid overflow on subtraction.
preds = scale * (preds.astype(np.int64) - zero_point)
preds = preds.astype(np.float32)
######################################################################
# borrowed from ultralytics\models\yolo\detect\predict.py #postprocess
# convert to torch to use ops.non_max_suppression
# ultralytics is working on none-deeplearning based non_max_suppression
# https://github.com/ultralytics/ultralytics/issues/1777
# maybe someday, but for now, just workaround
preds = torch.from_numpy(preds)
preds = ops.non_max_suppression(preds,
conf,
0.7, # todo, make into arg
agnostic=False,
max_det=300,
classes=None) # hack. just copied values from execution of yolov8n.pt
results = []
for i, pred in enumerate(preds):
orig_img = orig_imgs[i]
# tflite result are in [0, 1]
# scale them by width (w == h)
pred[:, :4] *= w
pred[:, :4] = ops.scale_boxes(input_img.shape[1:], pred[:, :4], orig_img.shape)
img_path = ""
results.append(Results(orig_img, path=img_path, names=yolo_default_label_names, boxes=pred))
return results
def get_label_names(self):
return yolo_default_label_names
class YoloDetectorWrapper:
def __init__(self, model_path, use_coral_tpu=False):
model_path = Path(model_path)
if use_coral_tpu or model_path.suffix == '.tflite':
self.detector = YoloDetectorTFLite(model_path, use_coral_tpu)
else:
self.detector = YoloDetector(model_path)
def predict(self, frame, conf=0.5):
return self.detector.predict(frame, conf=conf)
def get_label_names(self):
return self.detector.get_label_names()