文章目錄
- 1. 環境搭建
- 2. 資料集建構
-
- 1. xml檔案生成需要Labelimg軟體
- 2. VOC2007 資料集格式
- 3. 建立*.names
- 4. 更新data/mask.data
- 5. 更新cfg檔案，修改類别相關資訊
- 3. 訓練模型
- 4. 測試圖檔和視訊.
- 5. 測試攝像頭
- 6. 可視化
-
- 完整代碼百度雲連結
1. 環境搭建
首先需要安裝的工具pytorch，這是一個從torch改進過來的架構，進入官網的網站，選擇自己的電腦系統，下載下傳方式，語言，還有對應的CUDA，此時會給出一行pip指令，複制指令，打開cmd指令行，粘貼，同時加上國内源提高下載下傳速度，等待2個鐘頭左右，pytorch安裝完畢。
不同的pytorch有不同的CUDA和CUDNN的版本。CUDA和CUDNN英偉達出品的并行計算架構，簡單通俗的說就是可以讓GPU代替CPU的計算，當然由于是并發，是以處理大量運算的時候速度更快，在下載下傳CUDA的時候一定要注意是否和之前pytorch下載下傳的時候選擇的CUDA版本一緻，下載下傳位址就在英偉達的官方網站首頁。Cudnn是一個加速庫，面向神經網絡，同樣可以在英偉達的官網找到，也要注意支援的CUDA版本。
在全部下載下傳完畢後，輸出代碼torch.cuda.device(0)和torch.cuda.is.available()測試GPU是否能正常使用，如果不行說明CUDA沒有安裝好，或者顯示卡不支援。
pytorch安裝連接配接 https://pytorch.org/
2. 資料集建構
1. xml檔案生成需要Labelimg軟體
在Windows下使用LabelImg軟體進行标注，快捷鍵：
Ctrl + u 加載目錄中的所有圖像，滑鼠點選Open dir同功能
Ctrl + r 更改預設注釋目标目錄(xml檔案儲存的位址)
Ctrl + s 儲存
Ctrl + d 複制目前标簽和矩形框 space 将目前圖像标記為已驗證 w
建立一個矩形框
d 下一張圖檔
a 上一張圖檔
del 删除標明的矩形框
Ctrl++ 放大
Ctrl-- 縮小
↑→↓← 鍵盤箭頭移動標明的矩形框
2. VOC2007 資料集格式
-data
---- VOCdevkit2007
---- VOC2007
---- Annotations (标簽XML檔案，用對應的圖檔處理工具人工生成的)
---- ImageSets (生成的方法是用sh或者MATLAB語言生成)
---- ---- test.txt
---- ---- train.txt
---- ---- trainval.txt
---- ---- val.txt
- JPEGImages(原始檔案)
- labels (xml檔案對應的txt檔案)
imageSets下面的檔案可以由makeTxt.py生成
import os
import random
trainval_percent = 0.1
train_percent = 0.9
xmlfilepath = 'data/Annotations'
txtsavepath = 'data/ImageSets'
total_xml = os.listdir(xmlfilepath)
num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
ftrainval = open('data/ImageSets/trainval.txt', 'w')
ftest = open('data/ImageSets/test.txt', 'w')
ftrain = open('data/ImageSets/train.txt', 'w')
fval = open('data/ImageSets/val.txt', 'w')
for i in list:
name = total_xml[i][:-4] + '\n'
if i in trainval:
ftrainval.write(name)
if i in train:
ftest.write(name)
else:
fval.write(name)
else:
ftrain.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()
xml對應的txt檔案由voc_label.py生成 代碼如下
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets = ['train', 'test', 'val']
classes = ["face","face_mask"] # 我們隻是檢測細胞，是以隻有一個類别
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = (box[0] + box[1]) / 2.0
y = (box[2] + box[3]) / 2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x * dw
w = w * dw
y = y * dh
h = h * dh
return (x, y, w, h)
def convert_annotation(image_id):
in_file = open('data/Annotations/%s.xml' % (image_id))
out_file = open('data/labels/%s.txt' % (image_id), 'w')
tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
height=root.find('height')
w=1024
h=1024
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text))
bb = convert((w, h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
# out_file.write(str(cls_id) + " " + " ".join([str(a) for a in b]) + '\n')
wd = getcwd()
print(wd)
for image_set in sets:
if not os.path.exists('data/labels/'):
os.makedirs('data/labels/')
image_ids = open('data/ImageSets/%s.txt' % (image_set)).read().strip().split()
list_file = open('data/%s.txt' % (image_set), 'w')
for image_id in image_ids:
list_file.write('data/images/%s.jpg\n' % (image_id))
convert_annotation(image_id)
list_file.close()
此時為了避免錯誤檢查一下xml檔案，是否有size=0的情況，可以通關python進行批量删除，代碼如下：
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
def convert_annotation(image_id):
in_file = open('data/Annotations/%s.xml' % (image_id))
path='data/Annotations/%s.xml' % (image_id)
path2='data/Annotations/%s.xml' % (image_id)
tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
if(size is None):
print(image_id)
in_file.close()
os.remove(path)
else:
w=int(size.find('width').text)
h = int(size.find('height').text)
if(w==0 or h==0):
print(image_id)
in_file.close()
os.remove(path)
image_ids =[x for x in range(100,6366)]
for image_id in image_ids:
convert_annotation(image_id)
若有size=0的情況，可重新執行上面兩段代碼
3. 建立*.names
其中儲存的是你的所有的類别，每行一個類别，如data/mask.names：
face face_mask
4. 更新data/mask.data
classes = 2 # 改成你的資料集的類别個數
train = data/train.txt # 通過voc_label.py檔案生成的txt檔案
valid = data/test.txt # 通過voc_label.py檔案生成的txt檔案
names = data/mask.names # 記錄類别
backup = backup/ # 記錄checkpoint存放位置
eval = coco # 選擇map計算方式
5. 更新cfg檔案，修改類别相關資訊
[net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=2
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
###########
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=21
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=2
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 8
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=21
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=2
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
簡單來說tiny中就是把classes改為需要測的分類，此處為2 classes上一層[convolutional]中的filters改為3*（5+classes）
3. 訓練模型
首先打開cmd指令,進入項目的路徑。使用python train.py運作訓練代碼,效果如圖所示:
4. 測試圖檔和視訊.
python detect.py --weights weights/best.pt
import argparse
import time
from sys import platform
from models import *
from project.datasets import *
from project.utils import *
def detect(
cfg,
data_cfg,
weights,
images='data/samples', # input folder
output='output', # output folder
fourcc='mp4v',
img_size=416,
conf_thres=0.5,
nms_thres=0.5,
save_txt=False,
save_images=True,
webcam=False
):
device = torch_utils.select_device()
if os.path.exists(output):
shutil.rmtree(output) # delete output folder
os.makedirs(output) # make new output folder
# Initialize model
if ONNX_EXPORT:
s = (320, 192) # onnx model image size (height, width)
model = Darknet(cfg, s)
else:
model = Darknet(cfg, img_size)
# Load weights
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format
_ = load_darknet_weights(model, weights)
# Fuse Conv2d + BatchNorm2d layers
model.fuse()
# Eval mode
model.to(device).eval()
if ONNX_EXPORT:
img = torch.zeros((1, 3, s[0], s[1]))
torch.onnx.export(model, img, 'weights/export.onnx', verbose=True)
return
# Set Dataloader
vid_path, vid_writer =None,None
if webcam:
save_images = True
dataloader = LoadWebcam(img_size=img_size)
else:
dataloader = LoadImages(images, img_size=img_size)
# Get classes and colors
classes = load_classes(parse_data_cfg(data_cfg)['names'])
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))]
for i, (path, img, im0, vid_cap) in enumerate(dataloader):
t = time.time()
save_path = str(Path(output) / Path(path).name)
# Get detections
img = torch.from_numpy(img).unsqueeze(0).to(device)
pred, _ = model(img)
det = non_max_suppression(pred, conf_thres, nms_thres)[0]
if det is not None and len(det) > 0:
# Rescale boxes from 416 to true image size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results to screen
print('%gx%g ' % img.shape[2:], end='') # print image size
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum()
print('%g %ss' % (n, classes[int(c)]), end=', ')
# Draw bounding boxes and labels of detections
for *xyxy, conf, cls_conf, cls in det:
if save_txt: # Write to file
with open(save_path + '.txt', 'a') as file:
file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf))
# Add bbox to the image
label = '%s %.2f' % (classes[int(cls)], conf)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
print('Done. (%.3fs)' % (time.time() - t))
if webcam: # Show live webcam
cv2.imshow(weights, im0)
if save_images: # Save image with detections
if dataloader.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
fps = vid_cap.get(cv2.CAP_PROP_FPS)
width = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (width, height))
vid_writer.write(im0)
if save_images:
print('Results saved to %s' % os.getcwd() + os.sep + output)
if platform == 'darwin': # macos
os.system('open ' + output + ' ' + save_path)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='cfg/yolov3-tiny.cfg', help='cfg file path')
parser.add_argument('--data-cfg', type=str, default='data/mask.data', help='coco.data file path')
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
parser.add_argument('--images', type=str, default='data/samples', help='path to images')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
parser.add_argument('--fourcc', type=str, default='mp4v', help='specifies the fourcc code for output video encoding (make sure ffmpeg supports specified fourcc codec)')
parser.add_argument('--output', type=str, default='output',help='specifies the output path for images and videos')
opt = parser.parse_args()
print(opt)
with torch.no_grad():
detect(
opt.cfg,
opt.data_cfg,
opt.weights,
images=opt.images,
img_size=opt.img_size,
conf_thres=opt.conf_thres,
nms_thres=opt.nms_thres,
fourcc=opt.fourcc,
output=opt.output
)
5. 測試攝像頭
把detect.py中的webcam改為False。此時會報錯NoneType，打開datasets.py檔案
import glob
import math
import os
import random
import shutil
from pathlib import Path
import cv2
import numpy as np
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
from project.utils import xyxy2xywh
class LoadImages: # for inference
def __init__(self, path, img_size=416):
self.height = img_size
img_formats = ['.jpg', '.jpeg', '.png', '.tif']
vid_formats = ['.mov', '.avi', '.mp4']
files = []
if os.path.isdir(path):
files = sorted(glob.glob('%s/*.*' % path))
elif os.path.isfile(path):
files = [path]
images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
nI, nV = len(images), len(videos)
self.files = images + videos
self.nF = nI + nV # number of files
self.video_flag = [False] * nI + [True] * nV
self.mode = 'images'
if any(videos):
self.new_video(videos[0]) # new video
else:
self.cap = None
assert self.nF > 0, 'No images or videos found in ' + path
def __iter__(self):
self.count = 0
return self
def __next__(self):
if self.count == self.nF:
raise StopIteration
path = self.files[self.count]
if self.video_flag[self.count]:
# Read video
self.mode = 'video'
ret_val, img0 = self.cap.read()
if not ret_val:
self.count += 1
self.cap.release()
if self.count == self.nF: # last video
raise StopIteration
else:
path = self.files[self.count]
self.new_video(path)
ret_val, img0 = self.cap.read()
self.frame += 1
print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nF, self.frame, self.nframes, path), end='')
else:
# Read image
self.count += 1
img0 = cv2.imread(path) # BGR
assert img0 is not None, 'File Not Found ' + path
print('image %g/%g %s: ' % (self.count, self.nF, path), end='')
# Padded resize
img, _, _, _ = letterbox(img0, new_shape=self.height)
# Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
img = np.ascontiguousarray(img, dtype=np.float32) # uint8 to float32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
# cv2.imwrite(path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
return path, img, img0, self.cap
def new_video(self, path):
self.frame = 0
self.cap = cv2.VideoCapture(path)#path
self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
def __len__(self):
return self.nF # number of files
class LoadWebcam: # for inference
def __init__(self, img_size=416):
vid_formats = ['.mov', '.avi', '.mp4']#64
files = []#64
self.cam = cv2.VideoCapture(0)
self.height = img_size
videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]#64
self.mode = 'images'#64
if any(videos):#64
self.new_video(videos[0]) # new video#64
else:#64
self.cap = None#64
def __iter__(self):
self.count = -1
return self
def __next__(self):
self.count += 1
if cv2.waitKey(1) == 27: # esc to quit
cv2.destroyAllWindows()
raise StopIteration
# Read image
ret_val, img0 = self.cam.read()
assert ret_val, 'Webcam Error'
img_path = 'webcam_%g.jpg' % self.count
img0 = cv2.flip(img0, 1) # flip left-right
print('webcam %g: ' % self.count, end='')
# Padded resize
img, _, _, _ = letterbox(img0, new_shape=self.height)
# Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
img = np.ascontiguousarray(img, dtype=np.float32) # uint8 to float32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
return img_path, img, img0, None
def __len__(self):
return 0
class LoadImagesAndLabels(Dataset): # for training/testing
def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True, image_weights=False):
with open(path, 'r') as f:
img_files = f.read().splitlines()
self.img_files = list(filter(lambda x: len(x) > 0, img_files))
n = len(self.img_files)
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
nb = bi[-1] + 1 # number of batches
assert n > 0, 'No images found in %s' % path
self.n = n
self.batch = bi # batch index of image
self.img_size = img_size
self.augment = augment
self.image_weights = image_weights
self.rect = False if image_weights else rect
self.label_files = [x.replace('images', 'labels').
replace('.jpeg', '.txt').
replace('.jpg', '.txt').
replace('.bmp', '.txt').
replace('.png', '.txt') for x in self.img_files]
# Rectangular Training https://github.com/ultralytics/yolov3/issues/232
if self.rect:
from PIL import Image
# Read image shapes
#sp = 'data' + os.sep + path.replace('.txt', '.shapes').split(os.sep)[-1] # shapefile path
#sp = os.sep + path.replace('.txt', '.shapes').split(os.sep)[-1] # shapefile path
sp=path. replace('.txt','shapes'). split(os.sep)[-1]
if os.path.exists(sp): # read existing shapefile
with open(sp, 'r') as f:
s = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
assert len(s) == n, 'Shapefile out of sync, please delete %s and rerun' % sp
else: # no shapefile, so read shape using PIL and write shapefile for next time (faster)
s = np.array([Image.open(f).size for f in tqdm(self.img_files, desc='Reading image shapes')])
np.savetxt(sp, s, fmt='%g')
# Sort by aspect ratio
ar = s[:, 1] / s[:, 0] # aspect ratio
i = ar.argsort()
ar = ar[i]
self.img_files = [self.img_files[i] for i in i]
self.label_files = [self.label_files[i] for i in i]
# Set training image shapes
shapes = [[1, 1]] * nb
for i in range(nb):
ari = ar[bi == i]
mini, maxi = ari.min(), ari.max()
if maxi < 1:
shapes[i] = [maxi, 1]
elif mini > 1:
shapes[i] = [1, 1 / mini]
self.batch_shapes = np.ceil(np.array(shapes) * img_size / 32.).astype(np.int) * 32
# Preload labels (required for weighted CE training)
self.imgs = [None] * n
self.labels = [np.zeros((0, 5))] * n
iter = tqdm(self.label_files, desc='Reading labels') if n > 1000 else self.label_files
for i, file in enumerate(iter):
try:
with open(file, 'r') as f:
l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
if l.shape[0]:
assert l.shape[1] == 5, '> 5 label columns: %s' % file
assert (l >= 0).all(), 'negative labels: %s' % file
assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
self.labels[i] = l
except:
pass # print('Warning: missing labels for %s' % self.img_files[i]) # missing label file
assert len(np.concatenate(self.labels, 0)) > 0, 'No labels found. Incorrect label paths provided.'
def __len__(self):
return len(self.img_files)
# def __iter__(self):
# self.count = -1
# print('ran dataset iter')
# self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
# return self
def __getitem__(self, index):
if self.image_weights:
index = self.indices[index]
img_path = self.img_files[index]
label_path = self.label_files[index]
# Load image
img = self.imgs[index]
if img is None:
img = cv2.imread(img_path) # BGR
assert img is not None, 'File Not Found ' + img_path
if self.n < 1001:
self.imgs[index] = img # cache image into memory
# Augment colorspace
augment_hsv = True
if self.augment and augment_hsv:
# SV augmentation by 50%
fraction = 0.50 # must be < 1.0
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # hue, sat, val
S = img_hsv[:, :, 1].astype(np.float32) # saturation
V = img_hsv[:, :, 2].astype(np.float32) # value
a = (random.random() * 2 - 1) * fraction + 1
b = (random.random() * 2 - 1) * fraction + 1
S *= a
V *= b
img_hsv[:, :, 1] = S if a < 1 else S.clip(None, 255)
img_hsv[:, :, 2] = V if b < 1 else V.clip(None, 255)
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
# Letterbox
h, w, _ = img.shape
if self.rect:
shape = self.batch_shapes[self.batch[index]]
img, ratio, padw, padh = letterbox(img, new_shape=shape, mode='rect')
else:
shape = self.img_size
img, ratio, padw, padh = letterbox(img, new_shape=shape, mode='square')
# Load labels
labels = []
if os.path.isfile(label_path):
# with open(label_path, 'r') as f:
# x = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
x = self.labels[index]
if x.size > 0:
# Normalized xywh to pixel xyxy format
labels = x.copy()
labels[:, 1] = ratio * w * (x[:, 1] - x[:, 3] / 2) + padw
labels[:, 2] = ratio * h * (x[:, 2] - x[:, 4] / 2) + padh
labels[:, 3] = ratio * w * (x[:, 1] + x[:, 3] / 2) + padw
labels[:, 4] = ratio * h * (x[:, 2] + x[:, 4] / 2) + padh
# Augment image and labels
if self.augment:
img, labels = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.90, 1.10))
nL = len(labels) # number of labels
if nL:
# convert xyxy to xywh
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])
# Normalize coordinates 0 - 1
labels[:, [2, 4]] /= img.shape[0] # height
labels[:, [1, 3]] /= img.shape[1] # width
if self.augment:
# random left-right flip
lr_flip = True
if lr_flip and random.random() > 0.5:
img = np.fliplr(img)
if nL:
labels[:, 1] = 1 - labels[:, 1]
# random up-down flip
ud_flip = False
if ud_flip and random.random() > 0.5:
img = np.flipud(img)
if nL:
labels[:, 2] = 1 - labels[:, 2]
labels_out = torch.zeros((nL, 6))
if nL:
labels_out[:, 1:] = torch.from_numpy(labels)
# Normalize
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img, dtype=np.float32) # uint8 to float32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
return torch.from_numpy(img), labels_out, img_path, (h, w)
@staticmethod
def collate_fn(batch):
img, label, path, hw = list(zip(*batch)) # transposed
for i, l in enumerate(label):
l[:, 0] = i # add target image index for build_targets()
return torch.stack(img, 0), torch.cat(label, 0), path, hw
def letterbox(img, new_shape=416, color=(127.5, 127.5, 127.5), mode='auto'):
# Resize a rectangular image to a 32 pixel multiple rectangle
# https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
ratio = float(new_shape) / max(shape)
else:
ratio = max(new_shape) / max(shape) # ratio = new / old
new_unpad = (int(round(shape[1] * ratio)), int(round(shape[0] * ratio)))
# Compute padding https://github.com/ultralytics/yolov3/issues/232
if mode is 'auto': # minimum rectangle
dw = np.mod(new_shape - new_unpad[0], 32) / 2 # width padding
dh = np.mod(new_shape - new_unpad[1], 32) / 2 # height padding
elif mode is 'square': # square
dw = (new_shape - new_unpad[0]) / 2 # width padding
dh = (new_shape - new_unpad[1]) / 2 # height padding
elif mode is 'rect': # square
dw = (new_shape[1] - new_unpad[0]) / 2 # width padding
dh = (new_shape[0] - new_unpad[1]) / 2 # height padding
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) # resized, no border
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded square
return img, ratio, dw, dh
def random_affine(img, targets=(), degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
borderValue=(127.5, 127.5, 127.5)):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
if targets is None:
targets = []
border = 0 # width of added border (optional)
height = img.shape[0] + border * 2
width = img.shape[1] + border * 2
# Rotation and Scale
R = np.eye(3)
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
# a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations
s = random.random() * (scale[1] - scale[0]) + scale[0]
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
# Translation
T = np.eye(3)
T[0, 2] = (random.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels)
T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels)
# Shear
S = np.eye(3)
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
imw = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_LINEAR,
borderValue=borderValue) # BGR order borderValue
# Return warped points also
if len(targets) > 0:
n = targets.shape[0]
points = targets[:, 1:5].copy()
area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])
# warp points
xy = np.ones((n * 4, 3))
xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = (xy @ M.T)[:, :2].reshape(n, 8)
# create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
# # apply angle-based reduction of bounding boxes
# radians = a * math.pi / 180
# reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
# x = (xy[:, 2] + xy[:, 0]) / 2
# y = (xy[:, 3] + xy[:, 1]) / 2
# w = (xy[:, 2] - xy[:, 0]) * reduction
# h = (xy[:, 3] - xy[:, 1]) * reduction
# xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
# reject warped points outside of image
xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
w = xy[:, 2] - xy[:, 0]
h = xy[:, 3] - xy[:, 1]
area = w * h
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)
targets = targets[i]
targets[:, 1:5] = xy[i]
return imw, targets
def convert_images2bmp():
# cv2.imread() jpg at 230 img/s, *.bmp at 400 img/s
for path in ['../coco/images/val2014/', '../coco/images/train2014/']:
folder = os.sep + Path(path).name
output = path.replace(folder, folder + 'bmp')
if os.path.exists(output):
shutil.rmtree(output) # delete output folder
os.makedirs(output) # make new output folder
for f in tqdm(glob.glob('%s*.jpg' % path)):
save_name = f.replace('.jpg', '.bmp').replace(folder, folder + 'bmp')
cv2.imwrite(save_name, cv2.imread(f))
for label_path in ['../coco/trainvalno5k.txt', '../coco/5k.txt']:
with open(label_path, 'r') as file:
lines = file.read()
lines = lines.replace('2014/', '2014bmp/').replace('.jpg', '.bmp').replace(
'/Users/glennjocher/PycharmProjects/', '../')
with open(label_path.replace('5k', '5k_bmp'), 'w') as file:
file.write(lines)
增加一段代碼，上面已經添加了。
在class LoadWebcam的init中 ，修改為：
def __init__(self, img_size=416):
vid_formats = ['.mov', '.avi', '.mp4']#64
files = []#64
self.cam = cv2.VideoCapture(0)
self.height = img_size
videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]#64
self.mode = 'images'#64
if any(videos):#64
self.new_video(videos[0]) # new video#64
else:#64
self.cap = None#64
報錯解決，此時攝像頭會把拍到的臉變成圖檔的形式存在output中
6. 可視化
python -c from utils import utils;utils.plot_results()
比如face和mask
準确度precision=tp/(tp+fp) 識别出來的face占識别出來face和識别出來的面具的比率
召回率recall=tp/(tp+fn)識别出來的face占face總數的比率
完整代碼百度雲連結
連結：https://pan.baidu.com/s/1J5lEbPULafJNCTFMuLQ88g
提取碼：74sb