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https://github.com/azaion/ai-training.git
synced 2026-04-22 07:06:36 +00:00
add export to FP16
add inference with possibility to have different
This commit is contained in:
zxsanny
@@ -1,227 +0,0 @@
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import json
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import sys
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import time
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from enum import Enum
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from os.path import join, dirname
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import cv2
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import numpy as np
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import onnxruntime as onnx
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class Detection:
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def __init__(self, x, y, w, h, cls, confidence):
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self.x = x
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self.y = y
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self.w = w
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self.h = h
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self.cls = cls
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self.confidence = confidence
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def overlaps(self, det2):
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overlap_x = 0.5 * (self.w + det2.w) - abs(self.x - det2.x)
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overlap_y = 0.5 * (self.h + det2.h) - abs(self.y - det2.y)
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overlap_area = max(0, overlap_x) * max(0, overlap_y)
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min_area = min(self.w * self.h, det2.w * det2.h)
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return overlap_area / min_area > 0.6
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class Annotation:
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def __init__(self, frame, image_bytes, time, detections: list[Detection]):
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self.frame = frame
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self.image = image_bytes
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self.time = time
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self.detections = detections if detections is not None else []
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class WeatherMode(Enum):
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Norm = 0
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Wint = 20
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Night = 40
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class AnnotationClass:
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def __init__(self, id, name, color):
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self.id = id
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self.name = name
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self.color = color
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color_str = color.lstrip('#')
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self.opencv_color = (int(color_str[4:6], 16), int(color_str[2:4], 16), int(color_str[0:2], 16))
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@staticmethod
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def read_json():
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classes_path = join(dirname(dirname(__file__)), 'classes.json')
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with open(classes_path, 'r', encoding='utf-8') as f:
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j = json.loads(f.read())
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annotations_dict = {}
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for mode in WeatherMode:
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for cl in j:
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id = mode.value + cl['Id']
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name = cl['Name'] if mode.value == 0 else f'{cl["Name"]}({mode.name})'
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annotations_dict[id] = AnnotationClass(id, name, cl['Color'])
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return annotations_dict
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@property
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def color_tuple(self):
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color = self.color[3:]
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lv = len(color)
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xx = range(0, lv, lv // 3)
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return tuple(int(color[i:i + lv // 3], 16) for i in xx)
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class Inference:
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def __init__(self, onnx_model, batch_size, confidence_thres, iou_thres):
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self.onnx_model = onnx_model
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self.batch_size = batch_size
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self.confidence_thres = confidence_thres
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self.iou_thres = iou_thres
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self.model_width = None
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self.model_height = None
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self.classes = AnnotationClass.read_json()
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def draw(self, annotation: Annotation):
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img = annotation.frame
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img_height, img_width = img.shape[:2]
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for d in annotation.detections:
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x1 = int(img_width * (d.x - d.w / 2))
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y1 = int(img_height * (d.y - d.h / 2))
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x2 = int(x1 + img_width * d.w)
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y2 = int(y1 + img_height * d.h)
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color = self.classes[d.cls].opencv_color
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cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
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label = f"{self.classes[d.cls].name}: {d.confidence:.2f}"
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(label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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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, (x1, label_y - label_height), (x1 + label_width, label_y + label_height), color, cv2.FILLED
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)
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cv2.putText(img, label, (x1, label_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
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cv2.imshow('Video', img)
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def preprocess(self, frames):
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blobs = [cv2.dnn.blobFromImage(frame,
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scalefactor=1.0 / 255.0,
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size=(self.model_width, self.model_height),
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mean=(0, 0, 0),
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swapRB=True,
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crop=False)
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for frame in frames]
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return np.vstack(blobs)
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def postprocess(self, batch_frames, batch_timestamps, output):
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anns = []
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for i in range(len(output[0])):
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frame = batch_frames[i]
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timestamp = batch_timestamps[i]
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detections = []
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for det in output[0][i]:
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if det[4] == 0: # if confidence is 0 then valid points are over.
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break
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x1 = max(0, det[0] / self.model_width)
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y1 = max(0, det[1] / self.model_height)
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x2 = min(1, det[2] / self.model_width)
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y2 = min(1, det[3] / self.model_height)
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conf = round(det[4],2)
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class_id = int(det[5])
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x = (x1 + x2) / 2
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y = (y1 + y2) / 2
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w = x2 - x1
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h = y2 - y1
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detections.append(Detection(x, y, w, h, class_id, conf))
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filtered_detections = self.remove_overlapping_detections(detections)
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if len(filtered_detections) > 0:
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_, image = cv2.imencode('.jpg', frame)
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image_bytes = image.tobytes()
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annotation = Annotation(frame, image_bytes, timestamp, filtered_detections)
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anns.append(annotation)
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return anns
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def process(self, video):
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session = onnx.InferenceSession(self.onnx_model, providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
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model_inputs = session.get_inputs()
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input_name = model_inputs[0].name
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input_shape = model_inputs[0].shape
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self.model_width = input_shape[2]
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self.model_height = input_shape[3]
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frame_count = 0
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batch_frames = []
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batch_timestamps = []
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v_input = cv2.VideoCapture(video)
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while v_input.isOpened():
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ret, frame = v_input.read()
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if not ret or frame is None:
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break
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frame_count += 1
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if frame_count % 4 == 0:
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batch_frames.append(frame)
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batch_timestamps.append(int(v_input.get(cv2.CAP_PROP_POS_MSEC)))
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if len(batch_frames) == self.batch_size:
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input_blob = self.preprocess(batch_frames)
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outputs = session.run(None, {input_name: input_blob})
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annotations = self.postprocess(batch_frames, batch_timestamps, outputs)
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for annotation in annotations:
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self.draw(annotation)
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print(f'video: {annotation.time/1000:.3f}s')
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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batch_frames.clear()
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batch_timestamps.clear()
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def remove_overlapping_detections(self, detections):
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filtered_output = []
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filtered_out_indexes = []
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for det1_index in range(len(detections)):
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if det1_index in filtered_out_indexes:
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continue
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det1 = detections[det1_index]
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res = det1_index
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for det2_index in range(det1_index + 1, len(detections)):
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det2 = detections[det2_index]
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if det1.overlaps(det2):
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if det1.confidence > det2.confidence or (det1.confidence == det2.confidence and det1.cls < det2.cls): # det1 has higher confidence or lower class_id
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filtered_out_indexes.append(det2_index)
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else:
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filtered_out_indexes.append(res)
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res = det2_index
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filtered_output.append(detections[res])
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filtered_out_indexes.append(res)
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return filtered_output
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def overlap_tests(self):
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detections = [
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Detection(10, 10, 200, 200, 0, 0.5),
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Detection(10, 10, 200, 200, 0, 0.6),
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Detection(10, 10, 200, 200, 0, 0.4),
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Detection(10, 10, 200, 200, 0, 0.8),
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Detection(10, 10, 200, 200, 0, 0.3),
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]
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result = self.remove_overlapping_detections(detections)
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detections = [
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Detection(10, 10, 100, 100, 0, 0.5),
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Detection(50, 50, 120, 110, 0, 0.6)
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]
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result2 = self.remove_overlapping_detections(detections)
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pass
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if __name__ == "__main__":
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model = 'azaion-2024-10-26.onnx'
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input_video = 'ForAI_test.mp4'
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inf = Inference(model, batch_size=2, confidence_thres=0.5, iou_thres=0.35)
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# inf.overlap_tests()
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inf.process(input_video)
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cv2.waitKey(0)
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