如何形容网站开发公司技术经验,wordpress文章首页不显示怎么办,浙江网站建设多少钱,株洲最新消息YOLOv5 分类模型 预处理 OpenCV实现
flyfish
YOLOv5 分类模型 预处理 PIL 实现 YOLOv5 分类模型 OpenCV和PIL两者实现预处理的差异
YOLOv5 分类模型 数据集加载 1 样本处理 YOLOv5 分类模型 数据集加载 2 切片处理 YOLOv5 分类模型 数据集加载 3 自定义类别
YOLOv5 分类模型…YOLOv5 分类模型 预处理 OpenCV实现
flyfish
YOLOv5 分类模型 预处理 PIL 实现 YOLOv5 分类模型 OpenCV和PIL两者实现预处理的差异
YOLOv5 分类模型 数据集加载 1 样本处理 YOLOv5 分类模型 数据集加载 2 切片处理 YOLOv5 分类模型 数据集加载 3 自定义类别
YOLOv5 分类模型的预处理1 Resize 和 CenterCrop YOLOv5 分类模型的预处理2ToTensor 和 Normalize
YOLOv5 分类模型 Top 1和Top 5 指标说明 YOLOv5 分类模型 Top 1和Top 5 指标实现
判断图像是否是np.ndarray类型和维度
OpenCV读取一张图像时类型类型就是class numpy.ndarray这里判断图像是否是np.ndarray类型 dim是dimension维度的缩写,shape属性的长度也是它的ndim 灰度图的shape为HW二个维度 RGB图的shape为HWC三个维度
def _is_numpy_image(img):return isinstance(img, np.ndarray) and (img.ndim in {2, 3})实现ToTensor和Normalize
def totensor_normalize(img):print(preprocess:,img.shape)images (img/255-mean)/stdimages images.transpose((2, 0, 1))# HWC to CHWimages np.ascontiguousarray(images)return images实现Resize
插值可以是以下参数
# nearest: cv2.INTER_NEAREST,
# bilinear: cv2.INTER_LINEAR,
# area: cv2.INTER_AREA,
# bicubic: cv2.INTER_CUBIC,
# lanczos: cv2.INTER_LANCZOS4def resize(img, size, interpolationcv2.INTER_LINEAR):rResize the input numpy ndarray to the given size.Args:img (numpy ndarray): Image to be resized.size: like pytroch about size interpretation flyfish.interpolation (int, optional): Desired interpolation. Default iscv2.INTER_LINEAR Returns:numpy Image: Resized image.like opencvif not _is_numpy_image(img):raise TypeError(img should be numpy image. Got {}.format(type(img)))if not (isinstance(size, int) or (isinstance(size, collections.abc.Iterable) and len(size) 2)):raise TypeError(Got inappropriate size arg: {}.format(size))h, w img.shape[0], img.shape[1]if isinstance(size, int):if (w h and w size) or (h w and h size):return imgif w h:ow sizeoh int(size * h / w)else:oh sizeow int(size * w / h)else:ow, oh size[1], size[0]output cv2.resize(img, dsize(ow, oh), interpolationinterpolation)if img.shape[2] 1:return output[:, :, np.newaxis]else:return output实现CenterCrop
def crop(img, i, j, h, w):Crop the given Image flyfish.Args:img (numpy ndarray): Image to be cropped.i: Upper pixel coordinate.j: Left pixel coordinate.h: Height of the cropped image.w: Width of the cropped image.Returns:numpy ndarray: Cropped image.if not _is_numpy_image(img):raise TypeError(img should be numpy image. Got {}.format(type(img)))return img[i:i h, j:j w, :]def center_crop(img, output_size):if isinstance(output_size, numbers.Number):output_size (int(output_size), int(output_size))h, w img.shape[0:2]th, tw output_sizei int(round((h - th) / 2.))j int(round((w - tw) / 2.))return crop(img, i, j, th, tw)完整
import time
from models.common import DetectMultiBackend
import os
import os.path
from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
import cv2
import numpy as np
import collections
import torch
import numbersclasses_name[n02086240, n02087394, n02088364, n02089973, n02093754, n02096294, n02099601, n02105641, n02111889, n02115641]mean[0.485, 0.456, 0.406]
std[0.229, 0.224, 0.225]def _is_numpy_image(img):return isinstance(img, np.ndarray) and (img.ndim in {2, 3})def totensor_normalize(img):print(preprocess:,img.shape)images (img/255-mean)/stdimages images.transpose((2, 0, 1))# HWC to CHWimages np.ascontiguousarray(images)return imagesdef resize(img, size, interpolationcv2.INTER_LINEAR):rResize the input numpy ndarray to the given size.Args:img (numpy ndarray): Image to be resized.size: like pytroch about size interpretation flyfish.interpolation (int, optional): Desired interpolation. Default iscv2.INTER_LINEAR Returns:numpy Image: Resized image.like opencvif not _is_numpy_image(img):raise TypeError(img should be numpy image. Got {}.format(type(img)))if not (isinstance(size, int) or (isinstance(size, collections.abc.Iterable) and len(size) 2)):raise TypeError(Got inappropriate size arg: {}.format(size))h, w img.shape[0], img.shape[1]if isinstance(size, int):if (w h and w size) or (h w and h size):return imgif w h:ow sizeoh int(size * h / w)else:oh sizeow int(size * w / h)else:ow, oh size[1], size[0]output cv2.resize(img, dsize(ow, oh), interpolationinterpolation)if img.shape[2] 1:return output[:, :, np.newaxis]else:return outputdef crop(img, i, j, h, w):Crop the given Image flyfish.Args:img (numpy ndarray): Image to be cropped.i: Upper pixel coordinate.j: Left pixel coordinate.h: Height of the cropped image.w: Width of the cropped image.Returns:numpy ndarray: Cropped image.if not _is_numpy_image(img):raise TypeError(img should be numpy image. Got {}.format(type(img)))return img[i:i h, j:j w, :]def center_crop(img, output_size):if isinstance(output_size, numbers.Number):output_size (int(output_size), int(output_size))h, w img.shape[0:2]th, tw output_sizei int(round((h - th) / 2.))j int(round((w - tw) / 2.))return crop(img, i, j, th, tw)class DatasetFolder:def __init__(self,root: str,) - None:self.root rootif classes_name is None or not classes_name:classes, class_to_idx self.find_classes(self.root)print(not classes_name)else:classes classes_nameclass_to_idx {cls_name: i for i, cls_name in enumerate(classes)}print(is classes_name)print(classes:,classes)print(class_to_idx:,class_to_idx)samples self.make_dataset(self.root, class_to_idx)self.classes classesself.class_to_idx class_to_idxself.samples samplesself.targets [s[1] for s in samples]staticmethoddef make_dataset(directory: str,class_to_idx: Optional[Dict[str, int]] None,) - List[Tuple[str, int]]:directory os.path.expanduser(directory)if class_to_idx is None:_, class_to_idx self.find_classes(directory)elif not class_to_idx:raise ValueError(class_to_index must have at least one entry to collect any samples.)instances []available_classes set()for target_class in sorted(class_to_idx.keys()):class_index class_to_idx[target_class]target_dir os.path.join(directory, target_class)if not os.path.isdir(target_dir):continuefor root, _, fnames in sorted(os.walk(target_dir, followlinksTrue)):for fname in sorted(fnames):path os.path.join(root, fname)if 1: # 验证:item path, class_indexinstances.append(item)if target_class not in available_classes:available_classes.add(target_class)empty_classes set(class_to_idx.keys()) - available_classesif empty_classes:msg fFound no valid file for the classes {, .join(sorted(empty_classes))}. return instancesdef find_classes(self, directory: str) - Tuple[List[str], Dict[str, int]]:classes sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())if not classes:raise FileNotFoundError(fCouldnt find any class folder in {directory}.)class_to_idx {cls_name: i for i, cls_name in enumerate(classes)}return classes, class_to_idxdef __getitem__(self, index: int) - Tuple[Any, Any]:path, target self.samples[index]sample self.loader(path)return sample, targetdef __len__(self) - int:return len(self.samples)def loader(self, path):print(path:, path)img cv2.imread(path) # BGR HWCimgcv2.cvtColor(img,cv2.COLOR_BGR2RGB)#RGBprint(type:,type(img))return imgdef time_sync():return time.time()dataset DatasetFolder(root/media/flyfish/datasets/imagewoof/val)
weights /home/classes.pt
device cpu
model DetectMultiBackend(weights, devicedevice, dnnFalse, fp16False)
model.eval()def classify_transforms(img):imgresize(img,224)imgcenter_crop(img,224)imgtotensor_normalize(img)return img;pred, targets, loss, dt [], [], 0, [0.0, 0.0, 0.0]
# current batch size 1
for i, (images, labels) in enumerate(dataset):print(i:, i)print(images.shape, labels)im classify_transforms(images)imagestorch.from_numpy(im).to(torch.float32) # numpy to tensorimages images.unsqueeze(0).to(cpu)print(images.shape)t1 time_sync()images images.to(device, non_blockingTrue)t2 time_sync()# dt[0] t2 - t1y model(images)yy.numpy()print(y:, y)t3 time_sync()# dt[1] t3 - t2tmp1y.argsort()[:,::-1][:, :5]print(tmp1:, tmp1)pred.append(tmp1)print(labels:, labels)targets.append(labels)print(for pred:, pred) # listprint(for targets:, targets) # list# dt[2] time_sync() - t3pred, targets np.concatenate(pred), np.array(targets)
print(pred:, pred)
print(pred:, pred.shape)
print(targets:, targets)
print(targets:, targets.shape)
correct ((targets[:, None] pred)).astype(np.float32)
print(correct:, correct.shape)
print(correct:, correct)
acc np.stack((correct[:, 0], correct.max(1)), axis1) # (top1, top5) accuracy
print(acc:, acc.shape)
print(acc:, acc)
top acc.mean(0)
print(top1:, top[0])
print(top5:, top[1])结果
pred: [[0 3 6 2 1][0 7 2 9 3][0 5 6 2 9]...[9 8 7 6 1][9 3 6 7 0][9 5 0 2 7]]
pred: (3929, 5)
targets: [0 0 0 ... 9 9 9]
targets: (3929,)
correct: (3929, 5)
correct: [[ 1 0 0 0 0][ 1 0 0 0 0][ 1 0 0 0 0]...[ 1 0 0 0 0][ 1 0 0 0 0][ 1 0 0 0 0]]
acc: (3929, 2)
acc: [[ 1 1][ 1 1][ 1 1]...[ 1 1][ 1 1][ 1 1]]
top1: 0.86230594
top5: 0.98167473
