直达链接

Triton部署YOLOV5笔记（一）
Triton部署YOLOV5笔记（二）
triton部署yolov5笔记（三）
triton部署yolov5笔记（四）

拉取镜像

docker pull tienln/tensorrt:8.0.3_opencv
# docker pull tienln/ubuntu:18.04_conda #根据自己需要是否拉取ubuntu镜像
docker pull nvcr.io/nvidia/tritonserver:21.09-py3

克隆代码

创建自己的工作路径

mkdir myworkspace # 接下来的工作在这个路径下进行

进入路径

cd myworkspace

下载代码

git clone -b v5.0 https://github.com/ultralytics/yolov5.git
git clone -b yolov5-v5.0 https://github.com/wang-xinyu/tensorrtx.git

创建wts文件

首先进入工作路径

cd myworkspace

复制gen_wts.py文件到yolov5的根目录下

cp tensorrtx/yolov5/gen_wts.py yolov5

进入yolov5根目录

cd yolov5
# docker run -it --rm --gpus all -v $PWD:/yolov5 tienln/ubuntu:18.04_conda /bin/bash  #这是容器指向当前目录，也就是yolov5这个目录，如果不拉取ubuntu镜像可以直接在你的服务器端进入目录
cd /yolov5
conda activate yolov5  # 需要提前创建conda虚拟环境，安装目标检测所需要的包
python gen_wts.py -w yolov5s.pt -o yolov5s.wts #执行命令，生成yolov5.wts文件

创建tensorrt engine文件

进入工作目录

cd myworkspace

cp yolov5/yolov5s.wts tensorrtx/yolov5 # 拷贝wts文件到tensorrt的yolov5路径下
cd tensorrtx/yolov5  # 进入该路径

进入tensorrt容器进行下列操作
进入容器

docker run -it --rm --gpus all -v $PWD:/yolov5 tienln/tensorrt:8.0.3_opencv /bin/bash   # 进入容器，映射到tensorrt/yolov5路径

在容器里执行操作

cd /yolov5  # 进入yolov5路径
mkdir build
cd build
cmake ..
make -j16
./yolov5 -s ../yolov5s.wts ../yolov5s.engine s # 生成engine文件

创建Triton Inference Server

cd yourworkingdirectoryhere
mkdir -p triton_deploy/models/yolov5/1/
mkdir triton_deploy/plugins
cp tensorrtx/yolov5/yolov5s.engine triton_deploy/models/yolov5/1/model.plan
cp tensorrtx/yolov5/build/libmyplugins.so triton_deploy/plugins/libmyplugins.so

目录结构如下

.
├── models
│   └── model_0
│       └── 1
│           └── model.plan
└── plugins└── libmyplugins.so

run triton

docker run \
--gpus all \
--rm \
-p9000:8000 -p9001:8001 -p9002:8002 \
-v $(pwd)/triton_deploy/models:/models \
-v $(pwd)/triton_deploy/plugins:/plugins \
--env LD_PRELOAD=/plugins/libmyplugins.so \
nvcr.io/nvidia/tritonserver:21.09-py3 tritonserver \
--model-repository=/models \
--strict-model-config=false \
--log-verbose 1

客户端测试

cd myworkspace
cd clients/yolov5
docker run -it --rm --gpus all --network host -v $PWD:/client tienln/ubuntu:18.04_conda /bin/bash
conda activate yolov5
pip install tritonclient
cd /client
python client.py -o data/dog_result.jpg image data/dog.jpg

client全部代码如下

将所有文件放一个目录下
client.py文件

#!/usr/bin/env python
# client.py
import argparse
import numpy as np
import sys
import cv2
import timeimport tritonclient.grpc as grpcclient
from tritonclient.utils import InferenceServerExceptionfrom processing import preprocess, postprocess
from render import render_box, render_filled_box, get_text_size, render_text, RAND_COLORS, plot_one_box
from labels import COCOLabelsif __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('mode',choices=['dummy', 'image', 'video'],default='dummy',help='Run mode. \'dummy\' will send an emtpy buffer to the server to test if inference works. \'image\' will process an image. \'video\' will process a video.')parser.add_argument('input',type=str,nargs='?',help='Input file to load from in image or video mode')parser.add_argument('-m','--model',type=str,required=False,default='model_0',help='Inference model name, default yolov5')parser.add_argument('--width',type=int,required=False,default=640,help='Inference model input width, default 608')parser.add_argument('--height',type=int,required=False,default=640,help='Inference model input height, default 608')parser.add_argument('-u','--url',type=str,required=False,default='127.0.0.1:8081',help='Inference server URL, default localhost:8001')parser.add_argument('-o','--out',type=str,required=False,default='',help='Write output into file instead of displaying it')parser.add_argument('-c','--confidence',type=float,required=False,default=0.5,help='Confidence threshold for detected objects, default 0.5')parser.add_argument('-n','--nms',type=float,required=False,default=0.45,help='Non-maximum suppression threshold for filtering raw boxes, default 0.45')parser.add_argument('-f','--fps',type=float,required=False,default=24.0,help='Video output fps, default 24.0 FPS')parser.add_argument('-i','--model-info',action="store_true",required=False,default=False,help='Print model status, configuration and statistics')parser.add_argument('-v','--verbose',action="store_true",required=False,default=False,help='Enable verbose client output')parser.add_argument('-t','--client-timeout',type=float,required=False,default=None,help='Client timeout in seconds, default no timeout')parser.add_argument('-s','--ssl',action="store_true",required=False,default=False,help='Enable SSL encrypted channel to the server')parser.add_argument('-r','--root-certificates',type=str,required=False,default=None,help='File holding PEM-encoded root certificates, default none')parser.add_argument('-p','--private-key',type=str,required=False,default=None,help='File holding PEM-encoded private key, default is none')parser.add_argument('-x','--certificate-chain',type=str,required=False,default=None,help='File holding PEM-encoded certicate chain default is none')FLAGS = parser.parse_args()print(FLAGS)# Create server contexttry:triton_client = grpcclient.InferenceServerClient(url=FLAGS.url,verbose=FLAGS.verbose,ssl=FLAGS.ssl,root_certificates=FLAGS.root_certificates,private_key=FLAGS.private_key,certificate_chain=FLAGS.certificate_chain)except Exception as e:print("context creation failed: " + str(e))sys.exit()# Health checkif not triton_client.is_server_live():print("FAILED : is_server_live")sys.exit(1)if not triton_client.is_server_ready():print("FAILED : is_server_ready")sys.exit(1)if not triton_client.is_model_ready(FLAGS.model):print("FAILED : is_model_ready")sys.exit(1)if FLAGS.model_info:# Model metadatatry:metadata = triton_client.get_model_metadata(FLAGS.model)print(metadata)except InferenceServerException as ex:if "Request for unknown model" not in ex.message():print("FAILED : get_model_metadata")print("Got: {}".format(ex.message()))sys.exit(1)else:print("FAILED : get_model_metadata")sys.exit(1)# Model configurationtry:config = triton_client.get_model_config(FLAGS.model)if not (config.config.name == FLAGS.model):print("FAILED: get_model_config")sys.exit(1)print(config)except InferenceServerException as ex:print("FAILED : get_model_config")print("Got: {}".format(ex.message()))sys.exit(1)# DUMMY MODEif FLAGS.mode == 'dummy':print("Running in 'dummy' mode")print("Creating emtpy buffer filled with ones...")inputs = []outputs = []inputs.append(grpcclient.InferInput('data', [1, 3, FLAGS.width, FLAGS.height], "FP32"))inputs[0].set_data_from_numpy(np.ones(shape=(1, 3, FLAGS.width, FLAGS.height), dtype=np.float32))outputs.append(grpcclient.InferRequestedOutput('prob'))print("Invoking inference...")results = triton_client.infer(model_name=FLAGS.model,inputs=inputs,outputs=outputs,client_timeout=FLAGS.client_timeout)if FLAGS.model_info:statistics = triton_client.get_inference_statistics(model_name=FLAGS.model)if len(statistics.model_stats) != 1:print("FAILED: get_inference_statistics")sys.exit(1)print(statistics)print("Done")result = results.as_numpy('prob')print(f"Received result buffer of size {result.shape}")print(f"Naive buffer sum: {np.sum(result)}")# IMAGE MODEif FLAGS.mode == 'image':print("Running in 'image' mode")if not FLAGS.input:print("FAILED: no input image")sys.exit(1)inputs = []outputs = []inputs.append(grpcclient.InferInput('data', [1, 3, FLAGS.width, FLAGS.height], "FP32"))outputs.append(grpcclient.InferRequestedOutput('prob'))print("Creating buffer from image file...")input_image = cv2.imread(str(FLAGS.input))if input_image is None:print(f"FAILED: could not load input image {str(FLAGS.input)}")sys.exit(1)input_image_buffer = preprocess(input_image, [FLAGS.width, FLAGS.height])input_image_buffer = np.expand_dims(input_image_buffer, axis=0)inputs[0].set_data_from_numpy(input_image_buffer)print("Invoking inference...")t1 = time.time()results = triton_client.infer(model_name=FLAGS.model,inputs=inputs,outputs=outputs,client_timeout=FLAGS.client_timeout)if FLAGS.model_info:statistics = triton_client.get_inference_statistics(model_name=FLAGS.model)if len(statistics.model_stats) != 1:print("FAILED: get_inference_statistics")sys.exit(1)print(statistics)print("Done")result = results.as_numpy('prob')print(f"Received result buffer of size {result.shape}")print(f"Naive buffer sum: {np.sum(result)}")detected_objects = postprocess(result, input_image.shape[1], input_image.shape[0], [FLAGS.width, FLAGS.height], FLAGS.confidence, FLAGS.nms)print(f"Detected objects: {len(detected_objects)}")for box in detected_objects:print(f"{COCOLabels(box.classID).name}: {box.confidence}")plot_one_box(box.box(), input_image,color=tuple(RAND_COLORS[box.classID % 64].tolist()), label=f"{COCOLabels(box.classID).name}:{box.confidence:.2f}",)   if FLAGS.out:cv2.imwrite(FLAGS.out, input_image)print(f"Saved result to {FLAGS.out}")else:cv2.imshow('image', input_image)cv2.waitKey(0)cv2.destroyAllWindows()t2 = time.time()print('inference time is: {}ms'.format(1000 * (t2 - t1)))# VIDEO MODEif FLAGS.mode == 'video':print("Running in 'video' mode")if not FLAGS.input:print("FAILED: no input video")sys.exit(1)inputs = []outputs = []inputs.append(grpcclient.InferInput('data', [1, 3, FLAGS.width, FLAGS.height], "FP32"))outputs.append(grpcclient.InferRequestedOutput('prob'))print("Opening input video stream...")cap = cv2.VideoCapture(FLAGS.input)if not cap.isOpened():print(f"FAILED: cannot open video {FLAGS.input}")sys.exit(1)counter = 0out = Noneprint("Invoking inference...")while True:ret, frame = cap.read()if not ret:print("failed to fetch next frame")breakif counter == 0 and FLAGS.out:print("Opening output video stream...")fourcc = cv2.VideoWriter_fourcc('M', 'P', '4', 'V')out = cv2.VideoWriter(FLAGS.out, fourcc, FLAGS.fps, (frame.shape[1], frame.shape[0]))input_image_buffer = preprocess(frame, [FLAGS.width, FLAGS.height])input_image_buffer = np.expand_dims(input_image_buffer, axis=0)inputs[0].set_data_from_numpy(input_image_buffer)results = triton_client.infer(model_name=FLAGS.model,inputs=inputs,outputs=outputs,client_timeout=FLAGS.client_timeout)result = results.as_numpy('prob')detected_objects = postprocess(result, frame.shape[1], frame.shape[0], [FLAGS.width, FLAGS.height], FLAGS.confidence, FLAGS.nms)print(f"Frame {counter}: {len(detected_objects)} objects")counter += 1for box in detected_objects:print(f"{COCOLabels(box.classID).name}:{box.confidence}")plot_one_box(box.box(), frame, color=tuple(RAND_COLORS[box.classID % 64].tolist()), label=f"{COCOLabels(box.classID).name}: {box.confidence:.2f}",)   if FLAGS.out:out.write(frame)else:cv2.imshow('image', frame)if cv2.waitKey(1) == ord('q'):breakif FLAGS.model_info:statistics = triton_client.get_inference_statistics(model_name=FLAGS.model)if len(statistics.model_stats) != 1:print("FAILED: get_inference_statistics")sys.exit(1)print(statistics)print("Done")cap.release()if FLAGS.out:out.release()else:cv2.destroyAllWindows()print("Done")

boundingbox.py文件

# boundingbox.py
class BoundingBox:def __init__(self, classID, confidence, x1, x2, y1, y2, image_width, image_height):self.classID = classIDself.confidence = confidenceself.x1 = x1self.x2 = x2self.y1 = y1self.y2 = y2self.u1 = x1 / image_widthself.u2 = x2 / image_widthself.v1 = y1 / image_heightself.v2 = y2 / image_heightdef box(self):return (self.x1, self.y1, self.x2, self.y2)def width(self):return self.x2 - self.x1def height(self):return self.y2 - self.y1def center_absolute(self):return (0.5 * (self.x1 + self.x2), 0.5 * (self.y1 + self.y2))def center_normalized(self):return (0.5 * (self.u1 + self.u2), 0.5 * (self.v1 + self.v2))def size_absolute(self):return (self.x2 - self.x1, self.y2 - self.y1)def size_normalized(self):return (self.u2 - self.u1, self.v2 - self.v1)

labels.py文件

from enum import Enumclass COCOLabels(Enum):person = 0bicycle = 1car = 2motorcycle = 3airplane = 4bus = 5train = 6truck = 7boat = 8traffic_light = 9fire_hydrant = 10stop_sign = 11parking_meter = 12bench = 13bird = 14cat = 15dog = 16horse = 17sheep = 18cow = 19elephant = 20bear = 21zebra = 22giraffe = 23backpack = 24umbrella = 25handbag = 26tie = 27suitcase = 28frisbee = 29skis = 30snowboard = 31sports_ball = 32kite = 33baseball_bat = 34baseball_glove = 35skateboard = 36surfboard = 37tennis_racket = 38bottle = 39wine_glass = 40cup = 41fork = 42knife = 43spoon = 44bowl = 45banana = 46apple = 47sandwich = 48orange = 49broccoli = 50carrot = 51hot_dog = 52pizza = 53donut = 54cake = 55chair = 56sofa = 57pottedplant = 58bed = 59diningtable = 60toilet = 61tvmonitor = 62laptop = 63mouse = 64remote = 65keyboard = 66cell_phone = 67microwave = 68oven = 69toaster = 70sink = 71refrigerator = 72book = 73clock = 74vase = 75scissors = 76teddy_bear = 77hair_drier = 78toothbrush = 79

processing.py文件

# processing.py
from boundingbox import BoundingBoximport cv2
import numpy as npdef preprocess(raw_bgr_image, input_shape):"""description: Preprocess an image before TRT YOLO inferencing.Convert BGR image to RGB,resize and pad it to target size, normalize to [0,1],transform to NCHW format.          param:raw_bgr_image: int8 numpy array of shape (img_h, img_w, 3)input_shape: a tuple of (H, W)return:image:  the processed image float32 numpy array of shape (3, H, W)"""input_w, input_h = input_shapeimage_raw = raw_bgr_imageh, w, c = image_raw.shapeimage = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB)# Calculate widht and height and paddingsr_w = input_w / wr_h = input_h / hif r_h > r_w:tw = input_wth = int(r_w * h)tx1 = tx2 = 0ty1 = int((input_h - th) / 2)ty2 = input_h - th - ty1else:tw = int(r_h * w)th = input_htx1 = int((input_w - tw) / 2)tx2 = input_w - tw - tx1ty1 = ty2 = 0# Resize the image with long side while maintaining ratioimage = cv2.resize(image, (tw, th))# Pad the short side with (128,128,128)image = cv2.copyMakeBorder(image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, (128, 128, 128))image = image.astype(np.float32)# Normalize to [0,1]image /= 255.0# HWC to CHW format:image = np.transpose(image, [2, 0, 1])return imagedef xywh2xyxy(x, origin_h, origin_w, input_w, input_h):"""description:    Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-rightparam:origin_h:   height of original imageorigin_w:   width of original imagex:          A boxes numpy, each row is a box [center_x, center_y, w, h]return:y:          A boxes numpy, each row is a box [x1, y1, x2, y2]"""y = np.zeros_like(x)r_w = input_w / origin_wr_h = input_h / origin_hif r_h > r_w:y[:, 0] = x[:, 0] - x[:, 2] / 2y[:, 2] = x[:, 0] + x[:, 2] / 2y[:, 1] = x[:, 1] - x[:, 3] / 2 - (input_h - r_w * origin_h) / 2y[:, 3] = x[:, 1] + x[:, 3] / 2 - (input_h - r_w * origin_h) / 2y /= r_welse:y[:, 0] = x[:, 0] - x[:, 2] / 2 - (input_w - r_h * origin_w) / 2y[:, 2] = x[:, 0] + x[:, 2] / 2 - (input_w - r_h * origin_w) / 2y[:, 1] = x[:, 1] - x[:, 3] / 2y[:, 3] = x[:, 1] + x[:, 3] / 2y /= r_hreturn ydef bbox_iou(box1, box2, x1y1x2y2=True):"""description: compute the IoU of two bounding boxesparam:box1: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))box2: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))            x1y1x2y2: select the coordinate formatreturn:iou: computed iou"""if not x1y1x2y2:# Transform from center and width to exact coordinatesb1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2else:# Get the coordinates of bounding boxesb1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]# Get the coordinates of the intersection rectangleinter_rect_x1 = np.maximum(b1_x1, b2_x1)inter_rect_y1 = np.maximum(b1_y1, b2_y1)inter_rect_x2 = np.minimum(b1_x2, b2_x2)inter_rect_y2 = np.minimum(b1_y2, b2_y2)# Intersection areainter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, None) * \np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, None)# Union Areab1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)return ioudef non_max_suppression(prediction, origin_h, origin_w, input_w, input_h, conf_thres=0.5, nms_thres=0.4):"""description: Removes detections with lower object confidence score than 'conf_thres' and performsNon-Maximum Suppression to further filter detections.param:prediction: detections, (x1, y1, x2, y2, conf, cls_id)origin_h: original image heightorigin_w: original image widthconf_thres: a confidence threshold to filter detectionsnms_thres: a iou threshold to filter detectionsreturn:boxes: output after nms with the shape (x1, y1, x2, y2, conf, cls_id)"""# Get the boxes that score > CONF_THRESHboxes = prediction[prediction[:, 4] >= conf_thres]
#     print(boxes)# Trandform bbox from [center_x, center_y, w, h] to [x1, y1, x2, y2]boxes[:, :4] = xywh2xyxy(boxes[:, :4], origin_h, origin_w, input_w, input_h )# clip the coordinatesboxes[:, 0] = np.clip(boxes[:, 0], 0, origin_w -1)boxes[:, 2] = np.clip(boxes[:, 2], 0, origin_w -1)boxes[:, 1] = np.clip(boxes[:, 1], 0, origin_h -1)boxes[:, 3] = np.clip(boxes[:, 3], 0, origin_h -1)# Object confidenceconfs = boxes[:, 4]# Sort by the confsboxes = boxes[np.argsort(-confs)]# Perform non-maximum suppressionkeep_boxes = []while boxes.shape[0]:large_overlap = bbox_iou(np.expand_dims(boxes[0, :4], 0), boxes[:, :4]) > nms_threslabel_match = boxes[0, -1] == boxes[:, -1]# Indices of boxes with lower confidence scores, large IOUs and matching labelsinvalid = large_overlap & label_matchkeep_boxes += [boxes[0]]boxes = boxes[~invalid]boxes = np.stack(keep_boxes, 0) if len(keep_boxes) else np.array([])return boxesdef postprocess(output, origin_w, origin_h, input_shape, conf_th=0.5, nms_threshold=0.5, letter_box=False):"""Postprocess TensorRT outputs.# Argsoutput: list of detections with schema [num_boxes,cx,cy,w,h,conf,cls_id, cx,cy,w,h,conf,cls_id, ...] conf_th: confidence thresholdletter_box: boolean, referring to _preprocess_yolo()# Returnslist of bounding boxes with all detections above threshold and after nms, see class BoundingBox"""# Get the num of boxes detected# Here we use the first row of output in that batch_size = 1output = output[0]num = int(output[0])# Reshape to a two dimentional ndarraypred = np.reshape(output[1:], (-1, 6))[:num, :]# Do nmsboxes = non_max_suppression(pred, origin_h, origin_w, input_shape[0], input_shape[1], conf_thres=conf_th, nms_thres=nms_threshold)result_boxes = boxes[:, :4] if len(boxes) else np.array([])result_scores = boxes[:, 4] if len(boxes) else np.array([])result_classid = boxes[:, 5].astype(np.int) if len(boxes) else np.array([])detected_objects = []for box, score, label in zip(result_boxes, result_scores, result_classid):detected_objects.append(BoundingBox(label, score, box[0], box[2], box[1], box[3], origin_h, origin_w))return detected_objects

render.py文件

# render.py
import numpy as npimport cv2from math import sqrt_LINE_THICKNESS_SCALING = 500.0np.random.seed(69)
RAND_COLORS = np.random.randint(10, 255, (80, 3), "int")  # used for class visudef render_box(img, box, color=(200, 200, 200)):"""Render a box. Calculates scaling and thickness automatically.:param img: image to render into:param box: (x1, y1, x2, y2) - box coordinates:param color: (b, g, r) - box color:return: updated image"""x1, y1, x2, y2 = boxthickness = int(round((img.shape[0] * img.shape[1])/ (_LINE_THICKNESS_SCALING * _LINE_THICKNESS_SCALING)))thickness = max(1, thickness)img = cv2.rectangle(img,(int(x1), int(y1)),(int(x2), int(y2)),color,thickness=thickness)return imgdef render_filled_box(img, box, color=(200, 200, 200)):"""Render a box. Calculates scaling and thickness automatically.:param img: image to render into:param box: (x1, y1, x2, y2) - box coordinates:param color: (b, g, r) - box color:return: updated image"""x1, y1, x2, y2 = boximg = cv2.rectangle(img,(int(x1), int(y1)),(int(x2), int(y2)),color,thickness=cv2.FILLED)return img_TEXT_THICKNESS_SCALING = 700.0
_TEXT_SCALING = 520.0def get_text_size(img, text, normalised_scaling=1.0):"""Get calculated text size (as box width and height):param img: image reference, used to determine appropriate text scaling:param text: text to display:param normalised_scaling: additional normalised scaling. Default 1.0.:return: (width, height) - width and height of text box"""thickness = int(round((img.shape[0] * img.shape[1])/ (_TEXT_THICKNESS_SCALING * _TEXT_THICKNESS_SCALING))* normalised_scaling)thickness = max(1, thickness)scaling = img.shape[0] / _TEXT_SCALING * normalised_scalingreturn cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, scaling, thickness)[0]def render_text(img, text, pos, color=(200, 200, 200), normalised_scaling=1.0):"""Render a text into the image. Calculates scaling and thickness automatically.:param img: image to render into:param text: text to display:param pos: (x, y) - upper left coordinates of render position:param color: (b, g, r) - text color:param normalised_scaling: additional normalised scaling. Default 1.0.:return: updated image"""x, y = posthickness = int(round((img.shape[0] * img.shape[1])/ (_TEXT_THICKNESS_SCALING * _TEXT_THICKNESS_SCALING))* normalised_scaling)thickness = max(2, thickness)scaling = img.shape[0] / _TEXT_SCALING * normalised_scalingsize = get_text_size(img, text, normalised_scaling)cv2.putText(img,text,(int(x), int(y + size[1])),cv2.FONT_HERSHEY_SIMPLEX,scaling,color,thickness=thickness,)return imgdef plot_one_box(x, img, color=None, label=None, line_thickness=None):"""description: Plots one bounding box on image img,this function comes from YoLov5 project.param: x:      a box likes [x1,y1,x2,y2]img:    a opencv image objectcolor:  color to draw rectangle, such as (0,255,0)label:  strline_thickness: intreturn:no return"""tl = (line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1)  # line/font thicknessif color == None:color = [np.random.randint(0, 255) for _ in range(3)]c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)if label:tf = max(tl - 1, 1)  # font thicknesst_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filledcv2.putText(img,label,(c1[0], c1[1] - 2),0,tl / 3,[225, 255, 255],thickness=tf,lineType=cv2.LINE_AA,)

参考文献：大佬的github链接
火焰烟雾检测链接robmarkcole/fire-detection-from-images
spacewalk01/yolov5-fire-detection
zk2ly/Smoke_Fire_Detection
imsaksham-c/Fire-Smoke-Detection
DeepQuestAI/Fire-Smoke-Dataset
Tatowo/Smoke_Fire_Detection

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