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.idea/.gitignore 0 → 100644
1 # Default ignored files
2 /shelf/
3 /workspace.xml
.idea/inspectionProfiles/Project_Default.xml 0 → 100644
1 <component name="InspectionProjectProfileManager">
2 <profile version="1.0">
3 <option name="myName" value="Project Default" />
4 <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
5 <option name="ignoredPackages">
6 <value>
7 <list size="2">
8 <item index="0" class="java.lang.String" itemvalue="psutil" />
9 <item index="1" class="java.lang.String" itemvalue="thop" />
10 </list>
11 </value>
12 </option>
13 </inspection_tool>
14 </profile>
15 </component>
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1 <component name="InspectionProjectProfileManager">
2 <settings>
3 <option name="USE_PROJECT_PROFILE" value="false" />
4 <version value="1.0" />
5 </settings>
6 </component>
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1 <?xml version="1.0" encoding="UTF-8"?>
2 <project version="4">
3 <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (workenv)" project-jdk-type="Python SDK" />
4 </project>
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1 <?xml version="1.0" encoding="UTF-8"?>
2 <project version="4">
3 <component name="ProjectModuleManager">
4 <modules>
5 <module fileurl="file://$PROJECT_DIR$/.idea/tamper_det.iml" filepath="$PROJECT_DIR$/.idea/tamper_det.iml" />
6 </modules>
7 </component>
8 </project>
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.idea/tamper_det.iml 0 → 100644
1 <?xml version="1.0" encoding="UTF-8"?>
2 <module type="PYTHON_MODULE" version="4">
3 <component name="NewModuleRootManager">
4 <content url="file://$MODULE_DIR$" />
5 <orderEntry type="jdk" jdkName="Python 3.6 (workenv)" jdkType="Python SDK" />
6 <orderEntry type="sourceFolder" forTests="false" />
7 </component>
8 <component name="PyDocumentationSettings">
9 <option name="format" value="PLAIN" />
10 <option name="myDocStringFormat" value="Plain" />
11 </component>
12 </module>
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1 <?xml version="1.0" encoding="UTF-8"?>
2 <project version="4">
3 <component name="VcsDirectoryMappings">
4 <mapping directory="$PROJECT_DIR$" vcs="Git" />
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classify/val.py deleted 100644 → 0
1 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
2 """
3 Validate a trained YOLOv5 classification model on a classification dataset
4
5 Usage:
6 $ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
7 $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet
8
9 Usage - formats:
10 $ python classify/val.py --weights yolov5s-cls.pt # PyTorch
11 yolov5s-cls.torchscript # TorchScript
12 yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
13 yolov5s-cls.xml # OpenVINO
14 yolov5s-cls.engine # TensorRT
15 yolov5s-cls.mlmodel # CoreML (macOS-only)
16 yolov5s-cls_saved_model # TensorFlow SavedModel
17 yolov5s-cls.pb # TensorFlow GraphDef
18 yolov5s-cls.tflite # TensorFlow Lite
19 yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
20 yolov5s-cls_paddle_model # PaddlePaddle
21 """
22
23 import argparse
24 import os
25 import sys
26 from pathlib import Path
27
28 import torch
29 from tqdm import tqdm
30
31 FILE = Path(__file__).resolve()
32 ROOT = FILE.parents[1] # YOLOv5 root directory
33 if str(ROOT) not in sys.path:
34 sys.path.append(str(ROOT)) # add ROOT to PATH
35 ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
36
37 from models.common import DetectMultiBackend
38 from utils.dataloaders import create_classification_dataloader
39 from utils.general import LOGGER, Profile, check_img_size, check_requirements, colorstr, increment_path, print_args
40 from utils.torch_utils import select_device, smart_inference_mode
41
42
43 @smart_inference_mode()
44 def run(
45 data=ROOT / '../datasets/mnist', # dataset dir
46 weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s)
47 batch_size=128, # batch size
48 imgsz=224, # inference size (pixels)
49 device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
50 workers=8, # max dataloader workers (per RANK in DDP mode)
51 verbose=False, # verbose output
52 project=ROOT / 'runs/val-cls', # save to project/name
53 name='exp', # save to project/name
54 exist_ok=False, # existing project/name ok, do not increment
55 half=False, # use FP16 half-precision inference
56 dnn=False, # use OpenCV DNN for ONNX inference
57 model=None,
58 dataloader=None,
59 criterion=None,
60 pbar=None,
61 ):
62 # Initialize/load model and set device
63 training = model is not None
64 if training: # called by train.py
65 device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
66 half &= device.type != 'cpu' # half precision only supported on CUDA
67 model.half() if half else model.float()
68 else: # called directly
69 device = select_device(device, batch_size=batch_size)
70
71 # Directories
72 save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
73 save_dir.mkdir(parents=True, exist_ok=True) # make dir
74
75 # Load model
76 model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
77 stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
78 imgsz = check_img_size(imgsz, s=stride) # check image size
79 half = model.fp16 # FP16 supported on limited backends with CUDA
80 if engine:
81 batch_size = model.batch_size
82 else:
83 device = model.device
84 if not (pt or jit):
85 batch_size = 1 # export.py models default to batch-size 1
86 LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
87
88 # Dataloader
89 data = Path(data)
90 test_dir = data / 'test' if (data / 'test').exists() else data / 'val' # data/test or data/val
91 dataloader = create_classification_dataloader(path=test_dir,
92 imgsz=imgsz,
93 batch_size=batch_size,
94 augment=False,
95 rank=-1,
96 workers=workers)
97
98 model.eval()
99 pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())
100 n = len(dataloader) # number of batches
101 action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
102 desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
103 bar = tqdm(dataloader, desc, n, not training, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}', position=0)
104 with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
105 for images, labels in bar:
106 with dt[0]:
107 images, labels = images.to(device, non_blocking=True), labels.to(device)
108
109 with dt[1]:
110 y = model(images)
111
112 with dt[2]:
113 pred.append(y.argsort(1, descending=True)[:, :5])
114 targets.append(labels)
115 if criterion:
116 loss += criterion(y, labels)
117
118 loss /= n
119 pred, targets = torch.cat(pred), torch.cat(targets)
120 correct = (targets[:, None] == pred).float()
121 acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy
122 top1, top5 = acc.mean(0).tolist()
123
124 if pbar:
125 pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
126 if verbose: # all classes
127 LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
128 LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
129 for i, c in model.names.items():
130 aci = acc[targets == i]
131 top1i, top5i = aci.mean(0).tolist()
132 LOGGER.info(f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
133
134 # Print results
135 t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt) # speeds per image
136 shape = (1, 3, imgsz, imgsz)
137 LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)
138 LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
139
140 return top1, top5, loss
141
142
143 def parse_opt():
144 parser = argparse.ArgumentParser()
145 parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path')
146 parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)')
147 parser.add_argument('--batch-size', type=int, default=128, help='batch size')
148 parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)')
149 parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
150 parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
151 parser.add_argument('--verbose', nargs='?', const=True, default=True, help='verbose output')
152 parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name')
153 parser.add_argument('--name', default='exp', help='save to project/name')
154 parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
155 parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
156 parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
157 opt = parser.parse_args()
158 print_args(vars(opt))
159 return opt
160
161
162 def main(opt):
163 check_requirements(exclude=('tensorboard', 'thop'))
164 run(**vars(opt))
165
166
167 if __name__ == "__main__":
168 opt = parse_opt()
169 main(opt)
detect.py
...@@ -215,11 +215,11 @@ def run( ...@@ -215,11 +215,11 @@ def run(
215 215
216 def parse_opt(): 216 def parse_opt():
217 parser = argparse.ArgumentParser() 217 parser = argparse.ArgumentParser()
218 parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL') 218 parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs/train/exp/weights/best.pt', help='model path or triton URL')
219 parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') 219 parser.add_argument('--source', type=str, default=ROOT / 'data/images/crop_img', help='file/dir/URL/glob/screen/0(webcam)')
220 parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') 220 parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
221 parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') 221 parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
222 parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') 222 parser.add_argument('--conf-thres', type=float, default=0.3, help='confidence threshold')
223 parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') 223 parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
224 parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') 224 parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
225 parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') 225 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): ...@@ -17,7 +17,8 @@ def iou(box, boxes):
17 inner = np.maximum(0, (xx2 - xx1) * (yy2 - yy1)) 17 inner = np.maximum(0, (xx2 - xx1) * (yy2 - yy1))
18 return inner / (area1 + areas - inner) 18 return inner / (area1 + areas - inner)
19 19
20 def get_evaluate_score(true_image_path,true_label_path,predict_label_path,threshold): 20
21 def get_evaluate_score(true_image_path, true_label_path, predict_label_path, threshold):
21 true_labels = os.listdir(true_label_path) 22 true_labels = os.listdir(true_label_path)
22 predict_labels = os.listdir(predict_label_path) 23 predict_labels = os.listdir(predict_label_path)
23 targets, predicts = [], [] 24 targets, predicts = [], []
...@@ -38,7 +39,7 @@ def get_evaluate_score(true_image_path,true_label_path,predict_label_path,thresh ...@@ -38,7 +39,7 @@ def get_evaluate_score(true_image_path,true_label_path,predict_label_path,thresh
38 predicts.append(0) 39 predicts.append(0)
39 else: 40 else:
40 tmp = 0 41 tmp = 0
41 predict_label = open(os.path.join(predict_label_path,label)).readlines() 42 predict_label = open(os.path.join(predict_label_path, label)).readlines()
42 boxes = [] 43 boxes = []
43 for pl in predict_label: 44 for pl in predict_label:
44 cls, x1, y1, w1, h1 = [float(i) for i in pl.strip().split(' ')] 45 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 ...@@ -50,23 +51,28 @@ def get_evaluate_score(true_image_path,true_label_path,predict_label_path,thresh
50 x1, y1, w1, h1 = int(x1 * w), int(y1 * h), int(w1 * w), int(h1 * h) 51 x1, y1, w1, h1 = int(x1 * w), int(y1 * h), int(w1 * w), int(h1 * h)
51 xx1, yy1, xx2, yy2 = x1 - w1 // 2, y1 - h1 // 2, x1 + w1 // 2, y1 + h1 // 2 52 xx1, yy1, xx2, yy2 = x1 - w1 // 2, y1 - h1 // 2, x1 + w1 // 2, y1 + h1 // 2
52 box1 = [xx1, yy1, xx2, yy2] 53 box1 = [xx1, yy1, xx2, yy2]
53 inner_score = iou(np.array(box1),np.array(boxes)) 54 inner_score = iou(np.array(box1), np.array(boxes))
54 if max(inner_score)>threshold: 55 if max(inner_score) > threshold:
55 tmp=1 56 tmp = 1
56 predicts.append(1) 57 predicts.append(1)
57 break 58 break
58 if tmp==0: 59 if tmp == 0:
59 predicts.append(0) 60 predicts.append(0)
60 p = precision_score(targets,predicts) 61 p = precision_score(targets, predicts)
61 r = recall_score(targets,predicts) 62 r = recall_score(targets, predicts)
62 conf = confusion_matrix(targets,predicts) 63 conf = confusion_matrix(targets, predicts)
63 print('precison:',p) 64 print('precison:', p)
64 print('recall:',r) 65 print('recall:', r)
65 print(conf) 66 print(conf)
66 67 print(f' 预 测 ')
68 print(f' authentic tampered ')
69 print(f'真 authentic \t\t{conf[0, 0]} \t\t{conf[0,1]}')
70 print(f'实 tempered \t\t{conf[1, 0]} \t\t\t{conf[1,1]}')
71 print(f'authentic precision:{conf[0,0]/(conf[0,0]+conf[1,0])}\trecall:{conf[0, 0]/(conf[0, 0]+conf[0, 1])}')
72 print(f'tampered precision:{conf[1, 1]/(conf[0, 1]+conf[1, 1])}\trecall:{conf[1, 1]/(conf[1, 0]+conf[1, 1])}')
67 if __name__ == '__main__': 73 if __name__ == '__main__':
68 true_image_path = '/home/qfs/WorkSpace/ps_tamper/yolov5_ps/val_data/minsheng/images' 74 true_image_path = '/data/situ_invoice_bill_data/qfs_train_val_data/gongshang/images/val'
69 true_label_path = '/home/qfs/WorkSpace/ps_tamper/yolov5_ps/val_data/minsheng/labels' 75 true_label_path = '/data/situ_invoice_bill_data/qfs_train_val_data/gongshang/labels/val'
70 predict_label_path = '/home/qfs/WorkSpace/ps_tamper/yolov5/runs/detect/exp/labels' 76 predict_label_path = '/home/situ/qfs/invoice_tamper/09_project/project/tamper_det/runs/detect/exp4/labels'
71 threshold = 0.1 77 threshold = 0.1
72 get_evaluate_score(true_image_path,true_label_path,predict_label_path,threshold)
...\ No newline at end of file ...\ No newline at end of file
78 get_evaluate_score(true_image_path, true_label_path, predict_label_path, threshold)
......
inference.py 0 → 100644
1 import copy
2 import os
3 import sys
4 from pathlib import Path
5 import numpy as np
6 import torch
7
8 from utils.augmentations import letterbox
9
10 FILE = Path(__file__).resolve()
11 ROOT = FILE.parents[0] # YOLOv5 root directory
12 if str(ROOT) not in sys.path:
13 sys.path.append(str(ROOT)) # add ROOT to PATH
14 ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
15 from models.common import DetectMultiBackend
16 from utils.general import (check_img_size, cv2, non_max_suppression, scale_boxes)
17 from utils.torch_utils import select_device, smart_inference_mode
18 from models.yolov5_config import config
19
20 classes = ['tampered']
21
22
23 def gen_result_dict(boxes, label_list=[], std=False):
24 result = {
25 "error_code": 1,
26 "result": []
27 }
28 rs_box = {
29 "class": '',
30 "score": 0,
31 "left": 0,
32 "top": 0,
33 "width": 0,
34 "height": 0
35 }
36
37 if not label_list:
38 label_list = classes
39
40 for box in boxes:
41 result['error_code'] = 0
42 box_dict = copy.deepcopy(rs_box)
43 if std:
44 box_dict['class'] = str(int(box[-1]))
45 else:
46 box_dict['class'] = label_list[int(box[-1])]
47
48 box_dict['left'] = int(round(box[0], 0))
49 box_dict['top'] = int(round(box[1], 0))
50 box_dict['width'] = int(round(box[2], 0) - round(box[0], 0))
51 box_dict['height'] = int(round(box[3], 0) - (round(box[1], 0)))
52 box_dict['score'] = box[-2]
53 result['result'].append(box_dict)
54 return result
55
56
57 class Yolov5:
58 def __init__(self, cfg=None):
59 self.cfg = cfg
60 self.device = select_device(self.cfg.device)
61 self.model = DetectMultiBackend(self.cfg.weights, device=self.device, dnn=False, data=self.cfg.data, fp16=False)
62
63 def detect(self, image):
64 image0 = image.copy()
65 stride, names, pt = self.model.stride, self.model.names, self.model.pt
66 imgsz = check_img_size(self.cfg.imgsz, s=stride) # check image size
67 # Dataloader
68 bs = 1 # batch_size
69 im = letterbox(image, imgsz, stride=stride, auto=True)[0] # padded resize
70 im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
71 im = np.ascontiguousarray(im) # contiguous
72 # Run inference
73 self.model.warmup(imgsz=(1 if pt or self.model.triton else bs, 3, *imgsz)) # warmup
74 im = torch.from_numpy(im).to(self.model.device)
75 im = im.half() if self.model.fp16 else im.float() # uint8 to fp16/32
76 im /= 255 # 0 - 255 to 0.0 - 1.0
77 if len(im.shape) == 3:
78 im = im[None] # expand for batch dim
79 # Inference
80 pred = self.model(im, augment=False, visualize=False)
81 # NMS
82 pred = non_max_suppression(pred, self.cfg.conf_thres, self.cfg.iou_thres, None, False, max_det=self.cfg.max_det)
83
84 det = pred[0]
85 # if len(det):
86 det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], image0.shape).round()
87 result = gen_result_dict(det.cpu().numpy().tolist())
88 return result
89
90 def plot(self, image, boxes):
91 for box in boxes:
92 cv2.rectangle(image, (box[0], box[1], box[2], box[3]), (0, 0, 255), 2)
93 return image
94
95
96 if __name__ == "__main__":
97 img = cv2.imread(
98 '/home/situ/qfs/invoice_tamper/09_project/project/yolov5_inference/data/images/crop_img/_1594890230.8032346page_10_img_0_hname.jpg')
99 detector = Yolov5(config)
100 result = detector.detect(img)
101 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
1 from easydict import EasyDict as edict
2
3 config = edict(
4 weights='runs/train/exp/weights/best.pt', # model path or triton URL
5 data='data/VOC.yaml', # dataset.yaml path
6 imgsz=(640, 640), # inference size (height, width)
7 conf_thres=0.5, # confidence threshold
8 iou_thres=0.45, # NMS IOU threshold
9 max_det=1000, # maximum detections per image
10 device='' # cuda device, i.e. 0 or 0,1,2,3 or cpu
11 )
pipeline.py 0 → 100644
File mode changed
plot_sourece_labels.py 0 → 100644
1 import os
2
3 import cv2
4 import numpy as np
5 import pandas as pd
6 import tqdm
7
8
9 def get_source_image_det(crop_position, predict_positions):
10 result = []
11 x1, y1, x2, y2 = crop_position
12 for p in predict_positions:
13 px1, py1, px2, py2,score = p
14 w, h = px2 - px1, py2 - py1
15 result.append([x1 + px1, y1 + py1, x1 + px1 + w, y1 + py1 + h,score])
16 return result
17
18
19 def decode_label(image, label_path):
20 data = open(label_path).readlines()
21 h, w, c = image.shape
22 result = []
23 for d in data:
24 d = [float(i) for i in d.strip().split(' ')]
25 cls, cx, cy, cw, ch,score = d
26 cx, cy, cw, ch = cx * w, cy * h, cw * w, ch * h
27 result.append([int(cx - cw // 2), int(cy - ch // 2), int(cx + cw // 2), int(cy + ch // 2),score])
28 return result
29
30
31 if __name__ == '__main__':
32 source_image_path = '/data/situ_invoice_bill_data/new_data/qfs_bank_bill_data/gongshang/authentic/images/val'
33 val_image_path = '/home/situ/qfs/invoice_tamper/09_project/project/tamper_det/data/images/crop_img'
34 predict_label_path = '/home/situ/qfs/invoice_tamper/09_project/project/tamper_det/runs/detect/exp/labels'
35 crop_csv_path = '/data/situ_invoice_bill_data/new_data/qfs_bank_bill_data/gongshang/croped_merge.csv'
36 predict_labels = os.listdir(predict_label_path)
37 source_images = os.listdir(source_image_path)
38 data = pd.read_csv(crop_csv_path)
39 img_name = data.loc[:, 'img_name'].tolist()
40 crop_position1 = data.loc[:, 'name_crop_coord'].tolist()
41 crop_position2 = data.loc[:,'number_crop_coord'].tolist()
42 cc='/data/situ_invoice_bill_data/new_data/qfs_bank_bill_data/gongshang/tampered/images/val/ps3'
43 for im in os.listdir(cc):
44 print(im)
45 img = cv2.imread(os.path.join(cc,im))
46 img_=img.copy()
47 id = img_name.index(im)
48 name_crop_position=[int(i) for i in crop_position1[id].split(',')]
49 number_crop_position=[int(i) for i in crop_position2[id].split(',')]
50 nx1,ny1,nx2,ny2=name_crop_position
51 nux1,nuy1,nux2,nuy2=number_crop_position
52 if im[:-4]+'_hname.txt' in predict_labels:
53
54 h, w, c = img[ny1:ny2, nx1:nx2, :].shape
55 data = open(os.path.join(predict_label_path,im[:-4]+'_hname.txt')).readlines()
56 for d in data:
57 cls,cx,cy,cw,ch,score = [float(i) for i in d.strip().split(' ')]
58 cx,cy,cw,ch=int(cx*w),int(cy*h),int(cw*w),int(ch*h)
59 cx1,cy1=cx-cw//2,cy-ch//2
60 x1,y1,x2,y2=nx1+cx1,ny1+cy1,nx1+cx1+cw,ny1+cy1+ch
61 cv2.rectangle(img,(x1,y1),(x2,y2),(0,0,255),2)
62 cv2.putText(img,f'tampered:{score}',(x1,y1-5),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,255),1)
63 if im[:-4] + '_hnumber.txt' in predict_labels:
64 h, w, c = img[nuy1:nuy2, nux1:nux2, :].shape
65 data = open(os.path.join(predict_label_path, im[:-4] + '_hname.txt')).readlines()
66 for d in data:
67 cls, cx, cy, cw, ch, score = [float(i) for i in d.strip().split(' ')]
68 cx, cy, cw, ch = int(cx * w), int(cy * h), int(cw * w), int(ch * h)
69 cx1, cy1 = cx - cw // 2, cy - ch // 2
70 x1, y1, x2, y2 = nux1 + cx1, nuy1 + cy1, nux1 + cx1 + cw, nuy1 + cy1 + ch
71 cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
72 cv2.putText(img, f'tampered:{score}', (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
73 result = np.vstack((img_,img))
74 cv2.imwrite(f'z/{im}',result)
setup.cfg deleted 100644 → 0
1 # Project-wide configuration file, can be used for package metadata and other toll configurations
2 # Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments
3 # Local usage: pip install pre-commit, pre-commit run --all-files
4
5 [metadata]
6 license_file = LICENSE
7 description_file = README.md
8
9
10 [tool:pytest]
11 norecursedirs =
12 .git
13 dist
14 build
15 addopts =
16 --doctest-modules
17 --durations=25
18 --color=yes
19
20
21 [flake8]
22 max-line-length = 120
23 exclude = .tox,*.egg,build,temp
24 select = E,W,F
25 doctests = True
26 verbose = 2
27 # https://pep8.readthedocs.io/en/latest/intro.html#error-codes
28 format = pylint
29 # see: https://www.flake8rules.com/
30 ignore =
31 E731 # Do not assign a lambda expression, use a def
32 F405 # name may be undefined, or defined from star imports: module
33 E402 # module level import not at top of file
34 F401 # module imported but unused
35 W504 # line break after binary operator
36 E127 # continuation line over-indented for visual indent
37 E231 # missing whitespace after ‘,’, ‘;’, or ‘:’
38 E501 # line too long
39 F403 # ‘from module import *’ used; unable to detect undefined names
40
41
42 [isort]
43 # https://pycqa.github.io/isort/docs/configuration/options.html
44 line_length = 120
45 # see: https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html
46 multi_line_output = 0
47
48
49 [yapf]
50 based_on_style = pep8
51 spaces_before_comment = 2
52 COLUMN_LIMIT = 120
53 COALESCE_BRACKETS = True
54 SPACES_AROUND_POWER_OPERATOR = True
55 SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False
56 SPLIT_BEFORE_CLOSING_BRACKET = False
57 SPLIT_BEFORE_FIRST_ARGUMENT = False
58 # EACH_DICT_ENTRY_ON_SEPARATE_LINE = False
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