文章目录
概述
本文档主要描述python平台，使用openvino模块推理YOLOv5导出IR模型的方法。
文档主要包含以下内容：
openvino模块的安装- 模型格式的说明
- openvino的基础API接口，包括
初始化，模型加载，模型参数获取，模型推理等 - 图片数据的预处理
- 推理结果后处理，包括
NMS,cxcywh坐标转换为xyxy坐标等 - 关键方法的调用与参数说明
- 完整的示例代码
1.环境部署
预安装环境
(Windows) Visual Studio 2019Anaconda3或MiniConda3
注: 使用
openvino需确认CPU型号是Intel的，否则无法使用。
openvino安装
pytorch 1.7.1+cu110onnxruntime-gpu 1.7.0
conda create -n openvino python=3.8 -y
conda activate ort
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
pip install onnxruntime-gpu==1.7.0
ONNX模型转换
可通过官方链接下载YOLOv5的官方预训练模型，模型格式为pt.下载链接
YOLOv5官方项目提供了pt格式模型转换为ONNX格式模型的脚本，项目链接
模型导出指令：
python export --weights yolov5s.pt --include openvino
注：导出文件执行指令所需环境安装配置参考官方项目
README文档即可，不在赘述。
指令执行完成后，会在yolov5s.pt同级目录下生成名为yolov5s_openvino_model的文件夹，文件夹内包含IR模型文件。文件结构如下：
yolov5s_openvino_model
├── yolov5s.bin
├── yolov5s.mapping
└── yolov5s.xml
2.OpenVINO基础API
2.1 初始化
from openvino.runtime import Core
core = Core()
2.2 获取设备信息
devices = core.available_devices
for device in devices:
device_name = core.get_property(device, "FULL_DEVICE_NAME")
print(f"{
device}: {
device_name}")
代码输出示例:
device_name: 12th Gen Intel(R) Core(TM) i5-12400F
device_name: NVIDIA GeForce RTX 2060 SUPER (dGPU)
2.3 加载模型
加载OpenVINO 中间表示(IR)模型文件，返回ExecutableNetwork对象。
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# load the openvino IR model
yolo_model_path = "weights/yolov5s_openvino_model/yolov5s.xml"
model = core.read_model(model=yolo_model_path)
compiled_model = core.compile_model(model=model, device_name="AUTO")
2.4 获取模型输入输出信息
# 获取输入层信息
input_layer = model.inputs[0]
print(f"input_layer: {
input_layer}")
# 获取输出层信息
output_layer = model.outputs
print(f"output_layer: {
output_layer}")
代码输出示例:
input_layer: <Output: names[images] shape[1,3,640,640] type: f32>
output_layer: [<Output: names[output] shape[1,25200,85] type: f32>, <Output: names[345] shape[1,3,80,80,85] type: f32>, <Output: names[403] shape[1,3,40,40,85] type: f32>, <Output: names[461] shape[1,3,20,20,85] type: f32>]
可以看出，模型仅有一个输入层，但是有4个输出层，符合YOLOv5模型的多层输出特性。
在推理过程中，我们只需要关注output这个输出层就好了。
更进一步，为了后续的模型推理的预处理和后处理，我们需要获取模型的输入输出层的相关信息，包括：
- 输入层的name、shape、type
- 输出层对应的name、shape、type
# 如果输入层只有一层，直接调用any_name获取输入层名称
input_name = input_layer.any_name
print(f"input_name: {
input_name}")
N, C, H, W = input_layer.shape
print(f"N: {
N}, C: {
C}, H: {
H}, W: {
W}")
input_dtype = input_layer.element_type
print(f"input_dtype: {
input_dtype}")
output_name = output_layer[0].any_name
print(f"output_name: {
output_name}")
output_shape = output_layer[0].shape
print(f"output_shape: {
output_shape}")
output_dtype = output_layer[0].element_type
print(f"output_dtype: {
output_dtype}")
输出示例:
input_name: images
N: 1, C: 3, H: 640, W: 640
input_dtype: <Type: 'float32'>
output_name: output
output_shape: [1,25200,85]
output_dtype: <Type: 'float32'>
2.5 模型推理
image = cv2.imread(str(image_filename))
# image.shape = (height, width, channels)
# N,C,H,W = batch size, number of channels, height, width.
N, C, H, W = input_layer.shape
# OpenCV resize expects the destination size as (width, height).
resized_image = cv2.resize(src=image, dsize=(W, H))
# resized_image.shape = (height, width, channels)
input_data = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0).astype(np.float32)
# input_data.shape = (N, C, H, W)
# for single input models only
result = compiled_model(input_data)[output_layer]
# for multiple inputs in a list
result = compiled_model([input_data])[output_layer]
# or using a dictionary, where the key is input tensor name or index
result = compiled_model({
input_layer.any_name: input_data})[output_layer]
3.关键代码
2.1 图片数据预处理
数据预处理步骤包括resize，归一化，颜色通道转换，NCWH维度转换等。
resize之前，有一个非常常用的trick来处理非方形的图片，即计算图形的最长边，以此最长边为基础，创建一个正方形，并将原图形放置到左上角，剩余部分用黑色填充，这样做的好处是，不会改变原图形的长宽比，同时也不会改变原图形的内容。
# image preprocessing, the trick is to make the frame to be a square but not twist the image
row, col, _ = frame.shape # get the row and column of the origin frame array
_max = max(row, col) # get the max value of row and column
input_image = np.zeros((_max, _max, 3), dtype=np.uint8) # create a new array with the max value
input_image[:row, :col, :] = frame # paste the original frame to make the input_image to be a square
完成图片的填充后,继续执行resize，归一化，颜色通道转换等操作。
blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255.0, size=(640,640), swapRB=True, crop=False)
image: 输入图片数据,numpy.ndarray格式,shape为(H,W,C),Channel顺序为BGR。scalefactor: 图片数据归一化系数，一般为1/255.0。size: 图片resize尺寸，以模型的输入要求为准，这里是(640,640)。swapRB: 是否交换颜色通道，即转换BGR为RGBTrue表示交换，False表示不交换，由于opencv读取图片数据的颜色通道顺序为BGR，而YOLOv5模型的输入要求为RGB，所以这里需要交换颜色通道。crop: 是否裁剪图片，False表示不裁剪。
blobFromImage函数返回四维Mat对象(NCHW dimensions order),数据的shape为(1,3,640,640)
2.4 推理结果后处理
由于推理结果存在大量重叠的bbox，需要进行NMS处理，后续根据每个bbox的置信度和用户设定的置信度阈值进行过滤，最终得到最终的bbox，和对应的类别、置信度。
2.4.1 NMS
opencv-python模块提供了NMSBoxes方法，用于进行NMS处理。
cv2.dnn.NMSBoxes(bboxes, scores, score_threshold, nms_threshold, eta=None, top_k=None)
bboxes:bbox列表，shape为(N,4)，N为bbox数量，4为bbox的x,y,w,h。scores:bbox对应的置信度列表，shape为(N,1)，N为bbox数量。score_threshold: 置信度阈值，小于该阈值的bbox将被过滤。nms_threshold:NMS阈值
NMSBoxes函数返回值为bbox索引列表，shape为(M,)，M为bbox数量.
2.4.2 score_threshold过滤
根据NMS处理后的bbox索引列表，过滤置信度小于score_threshold的bbox。
2.4.3 bbox坐标转换与还原
YOLOv5模型输出的bbox坐标为cxcywh格式，需要转换为xyxy格式，此外，由于之前对图片进行了resize操作，所以需要将bbox坐标还原到原始图片的尺寸。
转换方法如下：
# 获取原始图片的尺寸(填充后)
image_width, image_height, _ = input_image.shape
# 计算缩放比
x_factor = image_width / INPUT_WIDTH # 640
y_factor = image_height / INPUT_HEIGHT # 640
# 将cxcywh坐标转换为xyxy坐标
x1 = int((x - w / 2) * x_factor)
y1 = int((y - h / 2) * y_factor)
w = int(w * x_factor)
h = int(h * y_factor)
x2 = x1 + w
y2 = y1 + h
x1,y1,x2,y2即为bbox的xyxy坐标。
4.示例代码
源代码一共有两份，其中一份是函数的拼接与调用，比较方便调试，另一份是封装成类，方便集成到其他项目中。
3.1 未封装
"""
this file is to demonstrate how to use openvino to do inference with yolov5 model exported from onnx to openvino format
"""
from typing import List
import cv2
import numpy as np
import time
from pathlib import Path
from openvino.runtime import Core
def build_model(model_path: str) -> cv2.dnn_Net:
"""
build the model with opencv dnn module
Args:
model_path: the path of the model, the model should be in onnx format
Returns:
the model object
"""
# load the model
core = Core()
model = core.read_model(model_path)
for device in core.available_devices:
print(device)
compiled_model = core.compile_model(model=model, device_name="AUTO")
# output_layer = compiled_model.output(0)
return compiled_model
def inference(image: np.ndarray, model: cv2.dnn_Net) -> np.ndarray:
"""
inference the model with the input image
Args:
image: the input image in numpy array format, the shape should be (height, width, channel),
the color channels should be in GBR order, like the original opencv image format
model: the model object
Returns:
the output data of the model, the shape should be (1, 25200, nc+5), nc is the number of classes
"""
# image preprocessing, include resize, normalization, channel swap like BGR to RGB, and convert to blob format
# get a 4-dimensional Mat with NCHW dimensions order.
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (INPUT_WIDTH, INPUT_HEIGHT), swapRB=True, crop=False)
output_layer = model.output(0)
outs = model([blob])[output_layer]
start = time.perf_counter()
# inference
# outs = model.forward()
end = time.perf_counter()
# print("inference time: ", end - start)
# the shape of the output data is (1, 25200, nc+5), nc is the number of classes
return outs
def xywh_to_xyxy(bbox_xywh, image_width, image_height):
"""
Convert bounding box coordinates from (center_x, center_y, width, height) to (x_min, y_min, x_max, y_max) format.
Parameters:
bbox_xywh (list or tuple): Bounding box coordinates in (center_x, center_y, width, height) format.
image_width (int): Width of the image.
image_height (int): Height of the image.
Returns:
tuple: Bounding box coordinates in (x_min, y_min, x_max, y_max) format.
"""
center_x, center_y, width, height = bbox_xywh
x_min = max(0, int(center_x - width / 2))
y_min = max(0, int(center_y - height / 2))
x_max = min(image_width - 1, int(center_x + width / 2))
y_max = min(image_height - 1, int(center_y + height / 2))
return x_min, y_min, x_max, y_max
def wrap_detection(
input_image: np.ndarray,
output_data: np.ndarray,
labels: List[str],
confidence_threshold: float = 0.6
) -> (List[int], List[float], List[List[int]]):
# the shape of the output_data is (25200,5+nc),
# the first 5 elements are [x, y, w, h, confidence], the rest are prediction scores of each class
image_width, image_height, _ = input_image.shape
x_factor = image_width / INPUT_WIDTH
y_factor = image_height / INPUT_HEIGHT
# transform the output_data[:, 0:4] from (x, y, w, h) to (x_min, y_min, x_max, y_max)
# output_data[:, 0:4] = np.apply_along_axis(xywh_to_xyxy, 1, output_data[:, 0:4], image_width, image_height)
indices = cv2.dnn.NMSBoxes(output_data[:, 0:4].tolist(), output_data[:, 4].tolist(), 0.6, 0.4)
# print(indices)
raw_boxes = output_data[:, 0:4][indices]
raw_confidences = output_data[:, 4][indices]
raw_class_prediction_probabilities = output_data[:, 5:][indices]
criteria = raw_confidences > confidence_threshold
raw_class_prediction_probabilities = raw_class_prediction_probabilities[criteria]
raw_boxes = raw_boxes[criteria]
raw_confidences = raw_confidences[criteria]
bounding_boxes, confidences, class_ids = [], [], []
for class_prediction_probability, box, confidence in zip(raw_class_prediction_probabilities, raw_boxes,
raw_confidences):
# find the least and most probable classes' indices and their probabilities
# min_val, max_val, min_loc, mac_loc = cv2.minMaxLoc(class_prediction_probability)
most_probable_class_index = np.argmax(class_prediction_probability)
label = labels[most_probable_class_index]
confidence = float(confidence)
x, y, w, h = box
left = int((x - 0.5 * w) * x_factor)
top = int((y - 0.5 * h) * y_factor)
width = int(w * x_factor)
height = int(h * y_factor)
bounding_box = [left, top, width, height]
bounding_boxes.append(bounding_box)
confidences.append(confidence)
class_ids.append(most_probable_class_index)
return class_ids, confidences, bounding_boxes
coco_class_names = ["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"]
# generate different colors for coco classes
colors = np.random.uniform(0, 255, size=(len(coco_class_names), 3))
INPUT_WIDTH = 640
INPUT_HEIGHT = 640
CONFIDENCE_THRESHOLD = 0.7
NMS_THRESHOLD = 0.45
def video_detector(video_src):
cap = cv2.VideoCapture(video_src)
# 3. inference and show the result in a loop
while cap.isOpened():
success, frame = cap.read()
start = time.perf_counter()
if not success:
break
# image preprocessing, the trick is to make the frame to be a square but not twist the image
row, col, _ = frame.shape # get the row and column of the origin frame array
_max = max(row, col) # get the max value of row and column
input_image = np.zeros((_max, _max, 3), dtype=np.uint8) # create a new array with the max value
input_image[:row, :col, :] = frame # paste the original frame to make the input_image to be a square
# inference
output_data = inference(input_image, net) # the shape of output_data is (1, 25200, 85)
# define coco dataset class names dictionary
# 4. wrap the detection result
class_ids, confidences, boxes = wrap_detection(input_image, output_data[0], coco_class_names)
# wrap_detection(input_image, output_data[0], coco_class_names) ##
# 5. draw the detection result on the frame
for (class_id, confidence, box) in zip(class_ids, confidences, boxes):
color = colors[int(class_id) % len(colors)]
label = coco_class_names[int(class_id)]
# cv2.rectangle(frame, box, color, 2)
# print(type(box), box[0], box[1], box[2], box[3], box)
xmin, ymin, width, height = box
cv2.rectangle(frame, (xmin, ymin), (xmin + width, ymin + height), color, 2)
# cv2.rectangle(frame, box, color, 2)
# cv2.rectangle(frame, [box[0], box[1], box[2], box[3]], color, thickness=2)
# cv2.rectangle(frame, (box[0], box[1] - 20), (box[0] + 100, box[1]), color, -1)
cv2.putText(frame, str(label), (box[0], box[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
finish = time.perf_counter()
FPS = round(1.0 / (finish - start), 2)
cv2.putText(frame, str(FPS), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
# 6. show the frame
cv2.imshow("frame", frame)
# 7. press 'q' to exit
if cv2.waitKey(1) == ord('q'):
break
# 8. release the capture and destroy all windows
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
# there are 4 steps to use opencv dnn module to inference onnx model exported by yolov5 and show the result
# 1. load the model
model_path = Path("weights/yolov5s_openvino_model/yolov5s.xml")
# model_path = Path("weights/POT_INT8_openvino_model/yolov5s_int8.xml")
net = build_model(str(model_path))
# 2. load the video capture
video_source = 0
# video_source = 'rtsp://admin:[email protected]:554/h264/ch1/main/av_stream'
video_detector(video_source)
exit(0)
3.2 封装成类调用
import cv2
import numpy as np
from pathlib import Path
import time
from typing import List
from glob import glob
from openvino.runtime import Core
class YoloV5OpenvinoInference:
def __init__(self, model_path: str,
imgsize: int = 640,
labels: List[str] = None,
score_threshold: float = 0.6,
nms_threshold: float = 0.45):
self.load_model(model_path)
self.imgsize = imgsize
self.score_threshold = score_threshold
self.nms_threshold = nms_threshold
self.coco_class_names = ["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"]
if labels is None or len(labels) == 0:
self.labels = self.coco_class_names
else:
self.labels = labels
self.colors = np.random.uniform(0, 255, size=(len(self.labels), 3))
self.x_factor = 1
self.y_factor = 1
self.xyxy = True
def load_model(self, model_path: str) -> None:
if not Path(model_path).exists():
raise FileNotFoundError(f"model file {
model_path} not found")
self.core = Core()
self.loaded_model = self.core.read_model(model_path)
self.compiled_model = self.core.compile_model(model=self.loaded_model, device_name="AUTO")
self.output_layer = self.loaded_model.output(0)
def preprocess(self, image: np.ndarray) -> np.ndarray:
row, col, _ = image.shape
_max = max(row, col)
input_image = np.zeros((_max, _max, 3), dtype=np.uint8)
input_image[:row, :col, :] = image
image_width, image_height, _ = input_image.shape
self.x_factor = image_width / self.imgsize
self.y_factor = image_height / self.imgsize
return input_image
def inference(self, image: np.ndarray) -> np.ndarray:
image = self.preprocess(image)
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (self.imgsize, self.imgsize), swapRB=True, crop=False)
start = time.perf_counter()
outs = self.compiled_model([blob])
# TODO
# 此处由于model.output(0)返回和推理输出的数据类型可能不一致(openvino.runtime.ConstOutput和openvino.runtime.Output)
# 导致取出输出层数据报错，暂时先这样处理
output_layer = [item for item in outs if item.any_name == "output"][0]
outs = outs[output_layer]
# outs = self.compiled_model([blob])['output']
end = time.perf_counter()
print("inference time: ", end - start)
return outs[0]
def wrap_detection(self, result: np.ndarray, to_xyxy: bool = True) -> (List[int], List[float], List[List[int]]):
# using NMS algorithm to filter out the overlapping bounding boxes
indices = cv2.dnn.NMSBoxes(result[:, 0:4].tolist(), result[:, 4].tolist(), 0.6, 0.4)
# get the real data after filtering
result = result[indices]
# filter the bounding boxes with confidence lower than the threshold
result = result[result[:, 4] > self.score_threshold]
bounding_boxes, confidences, classes = [], [], []
for item in result:
box = item[0:4]
confidence = float(item[4])
class_prediction_probability = item[5:]
most_probable_class_index = np.argmax(class_prediction_probability)
#
x, y, w, h = box
left = int((x - 0.5 * w) * self.x_factor)
top = int((y - 0.5 * h) * self.y_factor)
width = int(w * self.x_factor)
height = int(h * self.y_factor)
if to_xyxy:
self.xyxy = True
bounding_box = [left, top, left + width, top + height]
else:
self.xyxy = False
bounding_box = [left, top, width, height]
bounding_boxes.append(bounding_box)
confidences.append(confidence)
classes.append(most_probable_class_index)
return classes, confidences, bounding_boxes
def detect(self, image: np.ndarray, visualize=True) -> np.ndarray:
img = self.preprocess(image)
result = self.inference(img)
class_ids, confidences, boxes = self.wrap_detection(result, to_xyxy=True)
if visualize:
for (class_id, confidence, box) in zip(class_ids, confidences, boxes):
color = yolo_v5.colors[int(class_id) % len(yolo_v5.colors)]
label = yolo_v5.coco_class_names[int(class_id)]
if self.xyxy:
cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), color, 2)
else:
cv2.rectangle(image, box, color, 2)
cv2.rectangle(image, (box[0], box[1] - 20), (box[0] + 100, box[1]), color, -1)
cv2.putText(image, str(label), (box[0], box[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
return image
def video_detector(video_src):
cap = cv2.VideoCapture(video_src)
while cap.isOpened():
success, frame = cap.read()
if not success:
break
frame = yolo_v5.detect(frame)
cv2.imshow("frame", frame)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def image_detector(image_src):
image = cv2.imread(image_src)
image = yolo_v5.detect(image)
cv2.imshow("image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == '__main__':
model_path = "weights/yolov5s_openvino_model/yolov5s.xml"
yolo_v5 = YoloV5OpenvinoInference(model_path=model_path)
video_source = 0
# video_source = 'rtsp://admin:[email protected]:554/h264/ch1/main/av_stream'
video_detector(video_source)
# image_path = "data/images/bus.jpg"
# image_detector(image_path)
exit(0)