一、目标检测介绍
目标检测（Object Detection）是计算机视觉领域的一项重要技术，旨在识别图像或视频中的特定目标并确定其位置。通过训练深度学习模型，如卷积神经网络（CNN），可以实现对各种目标的精确检测。常见的目标检测任务包括：人脸检测、行人检测、车辆检测等。目标检测在安防监控、自动驾驶、智能零售等领域具有广泛应用前景。
二、YOLOv8介绍
YOLOv8 是 Ultralytics 公司在 2023 年 1月 10 号开源的 YOLOv5 的下一个重大更新版本，目前支持图像分类、物体检测和实例分割任务，在还没有开源时就收到了用户的广泛关注。YOLOv8 抛弃了前几代模型的 Anchor-Base，提供了一个全新的 SOTA 模型，包括 P5 640 和 P6 1280 分辨率的目标检测网络和基于 YOLACT 的实例分割模型。和 YOLOv5 一样，基于缩放系数也提供了 N/S/M/L/X 尺度的不同大小模型，用于处理不同大小的输入图像 。
三、源码获取
https://github.com/ultralytics/ultralytics
四、环境搭建
CPU环境安装
conda create -n YOLOv8 python==3.8.1
pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple
GPU环境安装
参考这个链接：点击
# 安装CUDA、CUDNN、Python、Pytorch、Torchvision 这里每个版本要相互对应
pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple
4.1 环境检测
下载yolov8n.pt和bus.jpg
然后命令行输入
yolo predict model=yolov8n.pt source='ultralytics/data/images/bus.jpg'
然后就会看到这个图片
五、数据集准备
这时候说明环境是没问题的了，我们可以准备数据集了，数据集的格式就是TXT标签加原图片，可参考YOLOv5这个博客：点击
六、 模型训练
6.1 方式一
YOLO(“yolov8n.pt”) 表示用预训练模型
YOLO(“yolov8n.yaml”)表示正常训练
from ultralytics import YOLO
# Load a model
# model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
model.train(data="ultralytics/cfg/mask.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("ultralytics/data/images/bus.jpg") # predict on an image
path = model.export(format="onnx") # export the model to ONNX format
6.2 方式二
yolo task=detect mode=train model=yolov8n.pt data=ultralytics/cfg/mask.yaml epochs=3 batch=16
6.3 针对其他任务
包括四种：detect 、segment、classify 、pose
通过修改YOLO()导入正确任务的yaml配置文件，以及通过data来指定需要载入的对应任务的数据集即可。
这里的数据集我都是按照TXT标签和原图来进行划分的，具体格式如下：
from ultralytics import YOLO
# Load a model
model = YOLO("ultralytics/cfg/models/v8/yolov8-seg.yaml") # build a new model from scratch
# Use the model
model.train(data="ultralytics/cfg/custom_seg.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("ultralytics/data/images/bus.jpg") # predict on an image
path = model.export(format="onnx") # export the model to ONNX format
针对实例分割任务也成功运行。
七、模型验证
yolo task=detect mode=val model=runs/detect/train/weights/best.pt data=ultralytics/cfg/mask.yaml device=cpu
八、模型测试
yolo task=detect mode=predict model=runs/detect/train/weights/best.pt source=ultralytics/data/images device=cpu
九、模型转换
9.1 转onnx
9.1.1 方式一
根据YOLOv8官网所给代码来实现
yolo export model=yolov8s.pt format=onnx opset=12
其次，可以通过ultralytics API导出onnx模型，并同时将bbox解码器和NMS等后处理添加到onnx模型中。YOLOv8-TensorRT
python export-det.py \
--weights yolov8s.pt \
--iou-thres 0.65 \
--conf-thres 0.25 \
--topk 100 \
--opset 11 \
--sim \
--input-shape 1 3 640 640 \
--device cuda:0
9.2 转tensorRT
9.2.1 trtexec
最简单的方式是使用TensorRT的bin文件夹下的trtexec.exe可执行文件
trtexec.exe --onnx=best.onnx --saveEngine=best.engine --fp16
9.2.2 代码转换
代码链接：YOLOv8-TensorRT文章来源：https://www.toymoban.com/news/detail-733550.html
python3 build.py \
--weights yolov8s.onnx \
--iou-thres 0.65 \
--conf-thres 0.25 \
--topk 100 \
--fp16 \
--device cuda:0
9.2.3 推理代码
参考博客：点击文章来源地址https://www.toymoban.com/news/detail-733550.html
"""
An example that uses TensorRT's Python api to make inferences.
"""
import ctypes
import os
import shutil
import random
import sys
import threading
import time
import cv2
import numpy as np
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt
CONF_THRESH = 0.5
IOU_THRESHOLD = 0.45
LEN_ALL_RESULT = 705600##42000 ##(20*20+40*40+80*80)*(num_cls+4) 一个batch长度
NUM_CLASSES = 80 ##1
OBJ_THRESH = 0.4
def get_img_path_batches(batch_size, img_dir):
ret = []
batch = []
for root, dirs, files in os.walk(img_dir):
for name in files:
if len(batch) == batch_size:
ret.append(batch)
batch = []
batch.append(os.path.join(root, name))
if len(batch) > 0:
ret.append(batch)
return ret
def 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 object
color: color to draw rectangle, such as (0,255,0)
label: str
line_thickness: int
return:
no return
"""
tl = (
line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1
) # line/font thickness
color = color or [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 thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
cv2.putText(
img,
label,
(c1[0], c1[1] - 2),
0,
tl / 3,
[225, 255, 255],
thickness=tf,
lineType=cv2.LINE_AA,
)
class YoLov8TRT(object):
"""
description: A YOLOv5 class that warps TensorRT ops, preprocess and postprocess ops.
"""
def __init__(self, engine_file_path):
# Create a Context on this device,
self.ctx = cuda.Device(0).make_context()
stream = cuda.Stream()
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
runtime = trt.Runtime(TRT_LOGGER)
# Deserialize the engine from file
with open(engine_file_path, "rb") as f:
engine = runtime.deserialize_cuda_engine(f.read())
context = engine.create_execution_context()
host_inputs = []
cuda_inputs = []
host_outputs = []
cuda_outputs = []
bindings = []
for binding in engine:
print('bingding:', binding, engine.get_tensor_shape(binding))
size = trt.volume(engine.get_tensor_shape(binding)) * engine.max_batch_size
dtype = trt.nptype(engine.get_tensor_dtype(binding))
# Allocate host and device buffers
host_mem = cuda.pagelocked_empty(size, dtype)
cuda_mem = cuda.mem_alloc(host_mem.nbytes)
# Append the device buffer to device bindings.
bindings.append(int(cuda_mem))
# Append to the appropriate list.
if engine.binding_is_input(binding):
self.input_w = engine.get_tensor_shape(binding)[-1]
self.input_h = engine.get_tensor_shape(binding)[-2]
host_inputs.append(host_mem)
cuda_inputs.append(cuda_mem)
else:
host_outputs.append(host_mem)
cuda_outputs.append(cuda_mem)
# Store
self.stream = stream
self.context = context
self.engine = engine
self.host_inputs = host_inputs
self.cuda_inputs = cuda_inputs
self.host_outputs = host_outputs
self.cuda_outputs = cuda_outputs
self.bindings = bindings
self.batch_size = engine.max_batch_size
def infer(self, raw_image_generator):
threading.Thread.__init__(self)
# Make self the active context, pushing it on top of the context stack.
self.ctx.push()
# Restore
stream = self.stream
context = self.context
engine = self.engine
host_inputs = self.host_inputs
cuda_inputs = self.cuda_inputs
host_outputs = self.host_outputs
cuda_outputs = self.cuda_outputs
bindings = self.bindings
# Do image preprocess
batch_image_raw = []
batch_origin_h = []
batch_origin_w = []
batch_input_image = np.empty(shape=[self.batch_size, 3, self.input_h, self.input_w])
for i, image_raw in enumerate(raw_image_generator):
input_image, image_raw, origin_h, origin_w = self.preprocess_image(image_raw)
batch_image_raw.append(image_raw)
batch_origin_h.append(origin_h)
batch_origin_w.append(origin_w)
np.copyto(batch_input_image[i], input_image)
batch_input_image = np.ascontiguousarray(batch_input_image)
# Copy input image to host buffer
np.copyto(host_inputs[0], batch_input_image.ravel())
start = time.time()
# Transfer input data to the GPU.
cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream)
# Run inference.
context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
# context.execute_async(batch_size=self.batch_size, bindings=bindings, stream_handle=stream.handle)
# Transfer predictions back from the GPU.
cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream)
# Synchronize the stream
stream.synchronize()
end = time.time()
# Remove any context from the top of the context stack, deactivating it.
self.ctx.pop()
# Here we use the first row of output in that batch_size = 1
output = host_outputs[0]
# Do postprocess
for i in range(self.batch_size):
result_boxes, result_scores, result_classid = self.post_process_new(
output[i * LEN_ALL_RESULT: (i + 1) * LEN_ALL_RESULT], batch_origin_h[i], batch_origin_w[i],
batch_input_image[i]
)
if result_boxes is None:
continue
# Draw rectangles and labels on the original image
for j in range(len(result_boxes)):
box = result_boxes[j]
plot_one_box(
box,
batch_image_raw[i],
label="{}:{:.2f}".format(
categories[int(result_classid[j])], result_scores[j]
),
)
return batch_image_raw, end - start
def destroy(self):
# Remove any context from the top of the context stack, deactivating it.
self.ctx.pop()
def get_raw_image(self, image_path_batch):
"""
description: Read an image from image path
"""
for img_path in image_path_batch:
yield cv2.imread(img_path)
def get_raw_image_zeros(self, image_path_batch=None):
"""
description: Ready data for warmup
"""
for _ in range(self.batch_size):
yield np.zeros([self.input_h, self.input_w, 3], dtype=np.uint8)
def preprocess_image(self, raw_bgr_image):
"""
description: Convert BGR image to RGB,
resize and pad it to target size, normalize to [0,1],
transform to NCHW format.
param:
input_image_path: str, image path
return:
image: the processed image
image_raw: the original image
h: original height
w: original width
"""
image_raw = raw_bgr_image
h, w, c = image_raw.shape
image = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB)
# Calculate widht and height and paddings
r_w = self.input_w / w
r_h = self.input_h / h
if r_h > r_w:
tw = self.input_w
th = int(r_w * h)
tx1 = tx2 = 0
ty1 = int((self.input_h - th) / 2)
ty2 = self.input_h - th - ty1
else:
tw = int(r_h * w)
th = self.input_h
tx1 = int((self.input_w - tw) / 2)
tx2 = self.input_w - tw - tx1
ty1 = ty2 = 0
# Resize the image with long side while maintaining ratio
image = cv2.resize(image, (tw, th))
# Pad the short side with (128,128,128)
image = cv2.copyMakeBorder(
image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, None, (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])
# CHW to NCHW format
image = np.expand_dims(image, axis=0)
# Convert the image to row-major order, also known as "C order":
image = np.ascontiguousarray(image)
return image, image_raw, h, w
def xywh2xyxy(self, origin_h, origin_w, x):
"""
description: Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
param:
origin_h: height of original image
origin_w: width of original image
x: 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 = self.input_w / origin_w
r_h = self.input_h / origin_h
if r_h > r_w:
y[:, 0] = x[:, 0] - x[:, 2] / 2
y[:, 2] = x[:, 0] + x[:, 2] / 2
y[:, 1] = x[:, 1] - x[:, 3] / 2 - (self.input_h - r_w * origin_h) / 2
y[:, 3] = x[:, 1] + x[:, 3] / 2 - (self.input_h - r_w * origin_h) / 2
y /= r_w
else:
y[:, 0] = x[:, 0] - x[:, 2] / 2 - (self.input_w - r_h * origin_w) / 2
y[:, 2] = x[:, 0] + x[:, 2] / 2 - (self.input_w - r_h * origin_w) / 2
y[:, 1] = x[:, 1] - x[:, 3] / 2
y[:, 3] = x[:, 1] + x[:, 3] / 2
y /= r_h
return y
def post_process_new(self, output, origin_h, origin_w, img_pad):
# Reshape to a two dimentional ndarray
c, h, w = img_pad.shape
ratio_w = w / origin_w
ratio_h = h / origin_h
num_anchors = int(((h / 32) * (w / 32) + (h / 16) * (w / 16) + (h / 8) * (w / 8)))
pred = np.reshape(output, (num_anchors, 4 + NUM_CLASSES))
results = []
for detection in pred:
score = detection[4:]
classid = np.argmax(score)
confidence = score[classid]
if confidence > CONF_THRESH:
if ratio_h > ratio_w:
center_x = int(detection[0] / ratio_w)
center_y = int((detection[1] - (h - ratio_w * origin_h) / 2) / ratio_w)
width = int(detection[2] / ratio_w)
height = int(detection[3] / ratio_w)
x1 = int(center_x - width / 2)
y1 = int(center_y - height / 2)
x2 = int(center_x + width / 2)
y2 = int(center_y + height / 2)
else:
center_x = int((detection[0] - (w - ratio_h * origin_w) / 2) / ratio_h)
center_y = int(detection[1] / ratio_h)
width = int(detection[2] / ratio_h)
height = int(detection[3] / ratio_h)
x1 = int(center_x - width / 2)
y1 = int(center_y - height / 2)
x2 = int(center_x + width / 2)
y2 = int(center_y + height / 2)
results.append([x1, y1, x2, y2, confidence, classid])
results = np.array(results)
if len(results) <= 0:
return None, None, None
# Do nms
boxes = self.non_max_suppression(results, origin_h, origin_w, conf_thres=CONF_THRESH, nms_thres=IOU_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] if len(boxes) else np.array([])
return result_boxes, result_scores, result_classid
def bbox_iou(self, box1, box2, x1y1x2y2=True):
"""
description: compute the IoU of two bounding boxes
param:
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 format
return:
iou: computed iou
"""
if not x1y1x2y2:
# Transform from center and width to exact coordinates
b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
else:
# Get the coordinates of bounding boxes
b1_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 rectangle
inter_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 area
inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, None) * \
np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, None)
# Union Area
b1_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 iou
def non_max_suppression(self, prediction, origin_h, origin_w, conf_thres=0.5, nms_thres=0.4):
"""
description: Removes detections with lower object confidence score than 'conf_thres' and performs
Non-Maximum Suppression to further filter detections.
param:
prediction: detections, (x1, y1,x2, y2, conf, cls_id)
origin_h: original image height
origin_w: original image width
conf_thres: a confidence threshold to filter detections
nms_thres: a iou threshold to filter detections
return:
boxes: output after nms with the shape (x1, y1, x2, y2, conf, cls_id)
"""
# Get the boxes that score > CONF_THRESH
boxes = prediction[prediction[:, 4] >= conf_thres]
# Trandform bbox from [center_x, center_y, w, h] to [x1, y1, x2, y2]
# boxes[:, :4] = self.xywh2xyxy(origin_h, origin_w, boxes[:, :4])
# clip the coordinates
boxes[:, 0] = np.clip(boxes[:, 0], 0, origin_w)
boxes[:, 2] = np.clip(boxes[:, 2], 0, origin_w)
boxes[:, 1] = np.clip(boxes[:, 1], 0, origin_h)
boxes[:, 3] = np.clip(boxes[:, 3], 0, origin_h)
# Object confidence
confs = boxes[:, 4]
# Sort by the confs
boxes = boxes[np.argsort(-confs)]
# Perform non-maximum suppression
keep_boxes = []
while boxes.shape[0]:
large_overlap = self.bbox_iou(np.expand_dims(boxes[0, :4], 0), boxes[:, :4]) > nms_thres
label_match = boxes[0, -1] == boxes[:, -1]
# Indices of boxes with lower confidence scores, large IOUs and matching labels
invalid = large_overlap & label_match
keep_boxes += [boxes[0]]
boxes = boxes[~invalid]
boxes = np.stack(keep_boxes, 0) if len(keep_boxes) else np.array([])
return boxes
def img_infer(yolov5_wrapper, image_path_batch):
batch_image_raw, use_time = yolov5_wrapper.infer(yolov5_wrapper.get_raw_image(image_path_batch))
for i, img_path in enumerate(image_path_batch):
parent, filename = os.path.split(img_path)
save_name = os.path.join('output', filename)
# Save image
cv2.imwrite(save_name, batch_image_raw[i])
print('input->{}, time->{:.2f}ms, saving into output/'.format(image_path_batch, use_time * 1000))
def warmup(yolov5_wrapper):
batch_image_raw, use_time = yolov5_wrapper.infer(yolov5_wrapper.get_raw_image_zeros())
print('warm_up->{}, time->{:.2f}ms'.format(batch_image_raw[0].shape, use_time * 1000))
if __name__ == "__main__":
engine_file_path = r"D:\personal\workplace\python_code\ultralytics-main\yolov8s_p.engine"
# load coco labels
categories = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" ]
# engine_file_path = r'C:\Users\caobin\Desktop\model_version\yolov8\20230602\best.engine'
# categories = ['man']
if os.path.exists('output/'):
shutil.rmtree('output/')
os.makedirs('output/')
# a YoLov5TRT instance
yolov8_wrapper = YoLov8TRT(engine_file_path)
try:
print('batch size is', yolov8_wrapper.batch_size)
image_dir = r"D:\personal\workplace\python_code\yolov5-6.0\data\images"
image_path_batches = get_img_path_batches(yolov8_wrapper.batch_size, image_dir)
for i in range(10):
warmup(yolov8_wrapper)
for batch in image_path_batches:
img_infer(yolov8_wrapper, batch)
finally:
yolov8_wrapper.destroy()
到了这里，关于目标检测笔记(十四): 使用YOLOv8完成对图像的目标检测任务（从数据准备到训练测试部署的完整流程）的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！
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