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.idea/.gitignore 0 → 100644
# Default ignored files
/shelf/
/workspace.xml
.idea/inspectionProfiles/Project_Default.xml 0 → 100644
<component name="InspectionProjectProfileManager">
<profile version="1.0">
<option name="myName" value="Project Default" />
<inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ignoredPackages">
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<item index="0" class="java.lang.String" itemvalue="psutil" />
<item index="1" class="java.lang.String" itemvalue="thop" />
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</value>
</option>
</inspection_tool>
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<component name="InspectionProjectProfileManager">
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<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (workenv)" project-jdk-type="Python SDK" />
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/tamper_det.iml" filepath="$PROJECT_DIR$/.idea/tamper_det.iml" />
</modules>
</component>
</project>
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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="jdk" jdkName="Python 3.6 (workenv)" jdkType="Python SDK" />
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<mapping directory="$PROJECT_DIR$" vcs="Git" />
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classify/val.py deleted 100644 → 0
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Validate a trained YOLOv5 classification model on a classification dataset
Usage:
$ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
$ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet
Usage - formats:
$ python classify/val.py --weights yolov5s-cls.pt # PyTorch
yolov5s-cls.torchscript # TorchScript
yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s-cls.xml # OpenVINO
yolov5s-cls.engine # TensorRT
yolov5s-cls.mlmodel # CoreML (macOS-only)
yolov5s-cls_saved_model # TensorFlow SavedModel
yolov5s-cls.pb # TensorFlow GraphDef
yolov5s-cls.tflite # TensorFlow Lite
yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
yolov5s-cls_paddle_model # PaddlePaddle
"""
import argparse
import os
import sys
from pathlib import Path
import torch
from tqdm import tqdm
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.common import DetectMultiBackend
from utils.dataloaders import create_classification_dataloader
from utils.general import LOGGER, Profile, check_img_size, check_requirements, colorstr, increment_path, print_args
from utils.torch_utils import select_device, smart_inference_mode
@smart_inference_mode()
def run(
data=ROOT / '../datasets/mnist', # dataset dir
weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s)
batch_size=128, # batch size
imgsz=224, # inference size (pixels)
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
workers=8, # max dataloader workers (per RANK in DDP mode)
verbose=False, # verbose output
project=ROOT / 'runs/val-cls', # save to project/name
name='exp', # save to project/name
exist_ok=False, # existing project/name ok, do not increment
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
model=None,
dataloader=None,
criterion=None,
pbar=None,
):
# Initialize/load model and set device
training = model is not None
if training: # called by train.py
device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
half &= device.type != 'cpu' # half precision only supported on CUDA
model.half() if half else model.float()
else: # called directly
device = select_device(device, batch_size=batch_size)
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
save_dir.mkdir(parents=True, exist_ok=True) # make dir
# Load model
model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
half = model.fp16 # FP16 supported on limited backends with CUDA
if engine:
batch_size = model.batch_size
else:
device = model.device
if not (pt or jit):
batch_size = 1 # export.py models default to batch-size 1
LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
# Dataloader
data = Path(data)
test_dir = data / 'test' if (data / 'test').exists() else data / 'val' # data/test or data/val
dataloader = create_classification_dataloader(path=test_dir,
imgsz=imgsz,
batch_size=batch_size,
augment=False,
rank=-1,
workers=workers)
model.eval()
pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())
n = len(dataloader) # number of batches
action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
bar = tqdm(dataloader, desc, n, not training, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}', position=0)
with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
for images, labels in bar:
with dt[0]:
images, labels = images.to(device, non_blocking=True), labels.to(device)
with dt[1]:
y = model(images)
with dt[2]:
pred.append(y.argsort(1, descending=True)[:, :5])
targets.append(labels)
if criterion:
loss += criterion(y, labels)
loss /= n
pred, targets = torch.cat(pred), torch.cat(targets)
correct = (targets[:, None] == pred).float()
acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy
top1, top5 = acc.mean(0).tolist()
if pbar:
pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
if verbose: # all classes
LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
for i, c in model.names.items():
aci = acc[targets == i]
top1i, top5i = aci.mean(0).tolist()
LOGGER.info(f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
# Print results
t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt) # speeds per image
shape = (1, 3, imgsz, imgsz)
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
return top1, top5, loss
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path')
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)')
parser.add_argument('--batch-size', type=int, default=128, help='batch size')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
parser.add_argument('--verbose', nargs='?', const=True, default=True, help='verbose output')
parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name')
parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
opt = parser.parse_args()
print_args(vars(opt))
return opt
def main(opt):
check_requirements(exclude=('tensorboard', 'thop'))
run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)
detect.py
......@@ -215,11 +215,11 @@ def run(
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL')
parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs/train/exp/weights/best.pt', help='model path or triton URL')
parser.add_argument('--source', type=str, default=ROOT / 'data/images/crop_img', help='file/dir/URL/glob/screen/0(webcam)')
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
parser.add_argument('--conf-thres', type=float, default=0.3, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
......
get_pr.py
......@@ -17,7 +17,8 @@ def iou(box, boxes):
inner = np.maximum(0, (xx2 - xx1) * (yy2 - yy1))
return inner / (area1 + areas - inner)
def get_evaluate_score(true_image_path,true_label_path,predict_label_path,threshold):
def get_evaluate_score(true_image_path, true_label_path, predict_label_path, threshold):
true_labels = os.listdir(true_label_path)
predict_labels = os.listdir(predict_label_path)
targets, predicts = [], []
......@@ -38,7 +39,7 @@ def get_evaluate_score(true_image_path,true_label_path,predict_label_path,thresh
predicts.append(0)
else:
tmp = 0
predict_label = open(os.path.join(predict_label_path,label)).readlines()
predict_label = open(os.path.join(predict_label_path, label)).readlines()
boxes = []
for pl in predict_label:
cls, x1, y1, w1, h1 = [float(i) for i in pl.strip().split(' ')]
......@@ -50,23 +51,28 @@ def get_evaluate_score(true_image_path,true_label_path,predict_label_path,thresh
x1, y1, w1, h1 = int(x1 * w), int(y1 * h), int(w1 * w), int(h1 * h)
xx1, yy1, xx2, yy2 = x1 - w1 // 2, y1 - h1 // 2, x1 + w1 // 2, y1 + h1 // 2
box1 = [xx1, yy1, xx2, yy2]
inner_score = iou(np.array(box1),np.array(boxes))
if max(inner_score)>threshold:
tmp=1
inner_score = iou(np.array(box1), np.array(boxes))
if max(inner_score) > threshold:
tmp = 1
predicts.append(1)
break
if tmp==0:
if tmp == 0:
predicts.append(0)
p = precision_score(targets,predicts)
r = recall_score(targets,predicts)
conf = confusion_matrix(targets,predicts)
print('precison:',p)
print('recall:',r)
p = precision_score(targets, predicts)
r = recall_score(targets, predicts)
conf = confusion_matrix(targets, predicts)
print('precison:', p)
print('recall:', r)
print(conf)
print(f' 预 测 ')
print(f' authentic tampered ')
print(f'真 authentic \t\t{conf[0, 0]} \t\t{conf[0,1]}')
print(f'实 tempered \t\t{conf[1, 0]} \t\t\t{conf[1,1]}')
print(f'authentic precision:{conf[0,0]/(conf[0,0]+conf[1,0])}\trecall:{conf[0, 0]/(conf[0, 0]+conf[0, 1])}')
print(f'tampered precision:{conf[1, 1]/(conf[0, 1]+conf[1, 1])}\trecall:{conf[1, 1]/(conf[1, 0]+conf[1, 1])}')
if __name__ == '__main__':
true_image_path = '/home/qfs/WorkSpace/ps_tamper/yolov5_ps/val_data/minsheng/images'
true_label_path = '/home/qfs/WorkSpace/ps_tamper/yolov5_ps/val_data/minsheng/labels'
predict_label_path = '/home/qfs/WorkSpace/ps_tamper/yolov5/runs/detect/exp/labels'
true_image_path = '/data/situ_invoice_bill_data/qfs_train_val_data/gongshang/images/val'
true_label_path = '/data/situ_invoice_bill_data/qfs_train_val_data/gongshang/labels/val'
predict_label_path = '/home/situ/qfs/invoice_tamper/09_project/project/tamper_det/runs/detect/exp4/labels'
threshold = 0.1
get_evaluate_score(true_image_path,true_label_path,predict_label_path,threshold)
\ No newline at end of file
get_evaluate_score(true_image_path, true_label_path, predict_label_path, threshold)
......
inference.py 0 → 100644
import copy
import os
import sys
from pathlib import Path
import numpy as np
import torch
from utils.augmentations import letterbox
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.common import DetectMultiBackend
from utils.general import (check_img_size, cv2, non_max_suppression, scale_boxes)
from utils.torch_utils import select_device, smart_inference_mode
from models.yolov5_config import config
classes = ['tampered']
def gen_result_dict(boxes, label_list=[], std=False):
result = {
"error_code": 1,
"result": []
}
rs_box = {
"class": '',
"score": 0,
"left": 0,
"top": 0,
"width": 0,
"height": 0
}
if not label_list:
label_list = classes
for box in boxes:
result['error_code'] = 0
box_dict = copy.deepcopy(rs_box)
if std:
box_dict['class'] = str(int(box[-1]))
else:
box_dict['class'] = label_list[int(box[-1])]
box_dict['left'] = int(round(box[0], 0))
box_dict['top'] = int(round(box[1], 0))
box_dict['width'] = int(round(box[2], 0) - round(box[0], 0))
box_dict['height'] = int(round(box[3], 0) - (round(box[1], 0)))
box_dict['score'] = box[-2]
result['result'].append(box_dict)
return result
class Yolov5:
def __init__(self, cfg=None):
self.cfg = cfg
self.device = select_device(self.cfg.device)
self.model = DetectMultiBackend(self.cfg.weights, device=self.device, dnn=False, data=self.cfg.data, fp16=False)
def detect(self, image):
image0 = image.copy()
stride, names, pt = self.model.stride, self.model.names, self.model.pt
imgsz = check_img_size(self.cfg.imgsz, s=stride) # check image size
# Dataloader
bs = 1 # batch_size
im = letterbox(image, imgsz, stride=stride, auto=True)[0] # padded resize
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
# Run inference
self.model.warmup(imgsz=(1 if pt or self.model.triton else bs, 3, *imgsz)) # warmup
im = torch.from_numpy(im).to(self.model.device)
im = im.half() if self.model.fp16 else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
pred = self.model(im, augment=False, visualize=False)
# NMS
pred = non_max_suppression(pred, self.cfg.conf_thres, self.cfg.iou_thres, None, False, max_det=self.cfg.max_det)
det = pred[0]
# if len(det):
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], image0.shape).round()
result = gen_result_dict(det.cpu().numpy().tolist())
return result
def plot(self, image, boxes):
for box in boxes:
cv2.rectangle(image, (box[0], box[1], box[2], box[3]), (0, 0, 255), 2)
return image
if __name__ == "__main__":
img = cv2.imread(
'/home/situ/qfs/invoice_tamper/09_project/project/yolov5_inference/data/images/crop_img/_1594890230.8032346page_10_img_0_hname.jpg')
detector = Yolov5(config)
result = detector.detect(img)
print(result)
models/__pycache__/__init__.cpython-36.pyc 0 → 100644
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models/__pycache__/common.cpython-36.pyc 0 → 100644
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models/__pycache__/experimental.cpython-36.pyc 0 → 100644
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models/__pycache__/yolo.cpython-36.pyc 0 → 100644
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models/yolov5_config.py 0 → 100644
from easydict import EasyDict as edict
config = edict(
weights='runs/train/exp/weights/best.pt', # model path or triton URL
data='data/VOC.yaml', # dataset.yaml path
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.5, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
device='' # cuda device, i.e. 0 or 0,1,2,3 or cpu
)
pipeline.py 0 → 100644
File mode changed
plot_sourece_labels.py 0 → 100644
import os
import cv2
import numpy as np
import pandas as pd
import tqdm
def get_source_image_det(crop_position, predict_positions):
result = []
x1, y1, x2, y2 = crop_position
for p in predict_positions:
px1, py1, px2, py2,score = p
w, h = px2 - px1, py2 - py1
result.append([x1 + px1, y1 + py1, x1 + px1 + w, y1 + py1 + h,score])
return result
def decode_label(image, label_path):
data = open(label_path).readlines()
h, w, c = image.shape
result = []
for d in data:
d = [float(i) for i in d.strip().split(' ')]
cls, cx, cy, cw, ch,score = d
cx, cy, cw, ch = cx * w, cy * h, cw * w, ch * h
result.append([int(cx - cw // 2), int(cy - ch // 2), int(cx + cw // 2), int(cy + ch // 2),score])
return result
if __name__ == '__main__':
source_image_path = '/data/situ_invoice_bill_data/new_data/qfs_bank_bill_data/gongshang/authentic/images/val'
val_image_path = '/home/situ/qfs/invoice_tamper/09_project/project/tamper_det/data/images/crop_img'
predict_label_path = '/home/situ/qfs/invoice_tamper/09_project/project/tamper_det/runs/detect/exp/labels'
crop_csv_path = '/data/situ_invoice_bill_data/new_data/qfs_bank_bill_data/gongshang/croped_merge.csv'
predict_labels = os.listdir(predict_label_path)
source_images = os.listdir(source_image_path)
data = pd.read_csv(crop_csv_path)
img_name = data.loc[:, 'img_name'].tolist()
crop_position1 = data.loc[:, 'name_crop_coord'].tolist()
crop_position2 = data.loc[:,'number_crop_coord'].tolist()
cc='/data/situ_invoice_bill_data/new_data/qfs_bank_bill_data/gongshang/tampered/images/val/ps3'
for im in os.listdir(cc):
print(im)
img = cv2.imread(os.path.join(cc,im))
img_=img.copy()
id = img_name.index(im)
name_crop_position=[int(i) for i in crop_position1[id].split(',')]
number_crop_position=[int(i) for i in crop_position2[id].split(',')]
nx1,ny1,nx2,ny2=name_crop_position
nux1,nuy1,nux2,nuy2=number_crop_position
if im[:-4]+'_hname.txt' in predict_labels:
h, w, c = img[ny1:ny2, nx1:nx2, :].shape
data = open(os.path.join(predict_label_path,im[:-4]+'_hname.txt')).readlines()
for d in data:
cls,cx,cy,cw,ch,score = [float(i) for i in d.strip().split(' ')]
cx,cy,cw,ch=int(cx*w),int(cy*h),int(cw*w),int(ch*h)
cx1,cy1=cx-cw//2,cy-ch//2
x1,y1,x2,y2=nx1+cx1,ny1+cy1,nx1+cx1+cw,ny1+cy1+ch
cv2.rectangle(img,(x1,y1),(x2,y2),(0,0,255),2)
cv2.putText(img,f'tampered:{score}',(x1,y1-5),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,255),1)
if im[:-4] + '_hnumber.txt' in predict_labels:
h, w, c = img[nuy1:nuy2, nux1:nux2, :].shape
data = open(os.path.join(predict_label_path, im[:-4] + '_hname.txt')).readlines()
for d in data:
cls, cx, cy, cw, ch, score = [float(i) for i in d.strip().split(' ')]
cx, cy, cw, ch = int(cx * w), int(cy * h), int(cw * w), int(ch * h)
cx1, cy1 = cx - cw // 2, cy - ch // 2
x1, y1, x2, y2 = nux1 + cx1, nuy1 + cy1, nux1 + cx1 + cw, nuy1 + cy1 + ch
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
cv2.putText(img, f'tampered:{score}', (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
result = np.vstack((img_,img))
cv2.imwrite(f'z/{im}',result)
setup.cfg deleted 100644 → 0
# Project-wide configuration file, can be used for package metadata and other toll configurations
# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments
# Local usage: pip install pre-commit, pre-commit run --all-files
[metadata]
license_file = LICENSE
description_file = README.md
[tool:pytest]
norecursedirs =
.git
dist
build
addopts =
--doctest-modules
--durations=25
--color=yes
[flake8]
max-line-length = 120
exclude = .tox,*.egg,build,temp
select = E,W,F
doctests = True
verbose = 2
# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
format = pylint
# see: https://www.flake8rules.com/
ignore =
E731 # Do not assign a lambda expression, use a def
F405 # name may be undefined, or defined from star imports: module
E402 # module level import not at top of file
F401 # module imported but unused
W504 # line break after binary operator
E127 # continuation line over-indented for visual indent
E231 # missing whitespace after ‘,’, ‘;’, or ‘:’
E501 # line too long
F403 # ‘from module import *’ used; unable to detect undefined names
[isort]
# https://pycqa.github.io/isort/docs/configuration/options.html
line_length = 120
# see: https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html
multi_line_output = 0
[yapf]
based_on_style = pep8
spaces_before_comment = 2
COLUMN_LIMIT = 120
COALESCE_BRACKETS = True
SPACES_AROUND_POWER_OPERATOR = True
SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False
SPLIT_BEFORE_CLOSING_BRACKET = False
SPLIT_BEFORE_FIRST_ARGUMENT = False
# EACH_DICT_ENTRY_ON_SEPARATE_LINE = False
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