修改截图+FOV

SetUI.py → FirstUI.py

@@ -1,13 +1,3 @@
-# -*- coding: utf-8 -*-
-
-# Form implementation generated from reading ui file 'Apex1.5.ui'
-#
-# Created by: PyQt5 UI code generator 5.15.9
-#
-# WARNING: Any manual changes made to this file will be lost when pyuic5 is
-# run again.  Do not edit this file unless you know what you are doing.
-
-
 from PyQt5 import QtCore, QtGui, QtWidgets
 
 

export.py

@@ -1,3 +1,44 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
+Format                      | `export.py --include`         | Model
+---                         | ---                           | ---
+PyTorch                     | -                             | yolov5s.pt
+TorchScript                 | `torchscript`                 | yolov5s.torchscript
+ONNX                        | `onnx`                        | yolov5s.onnx
+OpenVINO                    | `openvino`                    | yolov5s_openvino_model/
+TensorRT                    | `engine`                      | yolov5s.engine
+CoreML                      | `coreml`                      | yolov5s.mlmodel
+TensorFlow SavedModel       | `saved_model`                 | yolov5s_saved_model/
+TensorFlow GraphDef         | `pb`                          | yolov5s.pb
+TensorFlow Lite             | `tflite`                      | yolov5s.tflite
+TensorFlow Edge TPU         | `edgetpu`                     | yolov5s_edgetpu.tflite
+TensorFlow.js               | `tfjs`                        | yolov5s_web_model/
+PaddlePaddle                | `paddle`                      | yolov5s_paddle_model/
+Requirements:
+    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU
+    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow  # GPU
+Usage:
+    $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
+Inference:
+    $ python detect.py --weights yolov5s.pt                 # PyTorch
+                                 yolov5s.torchscript        # TorchScript
+                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                 yolov5s_openvino_model     # OpenVINO
+                                 yolov5s.engine             # TensorRT
+                                 yolov5s.mlmodel            # CoreML (macOS-only)
+                                 yolov5s_saved_model        # TensorFlow SavedModel
+                                 yolov5s.pb                 # TensorFlow GraphDef
+                                 yolov5s.tflite             # TensorFlow Lite
+                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
+                                 yolov5s_paddle_model       # PaddlePaddle
+TensorFlow.js:
+    $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
+    $ npm install
+    $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
+    $ npm start
+"""
+
 import argparse
 import contextlib
 import json
@@ -12,14 +53,6 @@
 
 import pandas as pd
 import torch
-from torch.utils.mobile_optimizer import optimize_for_mobile
-
-from models.experimental import attempt_load
-from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel
-from utils.dataloaders import LoadImages
-from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
-                           check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save)
-from utils.torch_utils import select_device, smart_inference_mode
 
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[0]  # YOLOv5 root directory
@@ -28,6 +61,13 @@
 if platform.system() != 'Windows':
     ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
+from models.experimental import attempt_load
+from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel
+from utils.dataloaders import LoadImages
+from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
+                           check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save)
+from utils.torch_utils import select_device, smart_inference_mode
+
 MACOS = platform.system() == 'Darwin'  # macOS environment
 
 
@@ -74,7 +114,7 @@ def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:'
     f = file.with_suffix('.torchscript')
 
     ts = torch.jit.trace(model, im, strict=False)
-    d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
+    d = {'shape': im.shape, 'stride': int(max(model.stride)), 'names': model.names}
     extra_files = {'config.txt': json.dumps(d)}  # torch._C.ExtraFilesMap()
     if optimize:  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
         optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
@@ -86,7 +126,7 @@ def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:'
 @try_export
 def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')):
     # YOLOv5 ONNX export
-    check_requirements('onnx')
+    check_requirements('onnx>=1.12.0')
     import onnx
 
     LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
@@ -107,7 +147,7 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
         f,
         verbose=False,
         opset_version=opset,
-        do_constant_folding=True,
+        do_constant_folding=True,  # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
         input_names=['images'],
         output_names=output_names,
         dynamic_axes=dynamic or None)
@@ -148,8 +188,15 @@ def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
     LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
     f = str(file).replace('.pt', f'_openvino_model{os.sep}')
 
-    cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
-    subprocess.run(cmd.split(), check=True, env=os.environ)  # export
+    args = [
+        'mo',
+        '--input_model',
+        str(file.with_suffix('.onnx')),
+        '--output_dir',
+        f,
+        '--data_type',
+        ('FP16' if half else 'FP32'), ]
+    subprocess.run(args, check=True, env=os.environ)  # export
     yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata)  # add metadata.yaml
     return f, None
 
@@ -184,7 +231,7 @@ def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
     if bits < 32:
         if MACOS:  # quantization only supported on macOS
             with warnings.catch_warnings():
-                warnings.filterwarnings("ignore", category=DeprecationWarning)  # suppress numpy==1.20 float warning
+                warnings.filterwarnings('ignore', category=DeprecationWarning)  # suppress numpy==1.20 float warning
                 ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
         else:
             print(f'{prefix} quantization only supported on macOS, skipping...')
@@ -240,7 +287,7 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
 
     if dynamic:
         if im.shape[0] <= 1:
-            LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument")
+            LOGGER.warning(f'{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument')
         profile = builder.create_optimization_profile()
         for inp in inputs:
             profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
@@ -350,7 +397,7 @@ def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=c
         converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
 
     tflite_model = converter.convert()
-    open(f, "wb").write(tflite_model)
+    open(f, 'wb').write(tflite_model)
     return f, None
 
 
@@ -360,7 +407,7 @@ def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
     cmd = 'edgetpu_compiler --version'
     help_url = 'https://coral.ai/docs/edgetpu/compiler/'
     assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
-    if subprocess.run(f'{cmd} >/dev/null', shell=True).returncode != 0:
+    if subprocess.run(f'{cmd} > /dev/null 2>&1', shell=True).returncode != 0:
         LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
         sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0  # sudo installed on system
         for c in (
@@ -374,13 +421,20 @@ def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
     f = str(file).replace('.pt', '-int8_edgetpu.tflite')  # Edge TPU model
     f_tfl = str(file).replace('.pt', '-int8.tflite')  # TFLite model
 
-    cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}"
-    subprocess.run(cmd.split(), check=True)
+    subprocess.run([
+        'edgetpu_compiler',
+        '-s',
+        '-d',
+        '-k',
+        '10',
+        '--out_dir',
+        str(file.parent),
+        f_tfl, ], check=True)
     return f, None
 
 
 @try_export
-def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
+def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')):
     # YOLOv5 TensorFlow.js export
     check_requirements('tensorflowjs')
     import tensorflowjs as tfjs
@@ -390,9 +444,14 @@ def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
     f_pb = file.with_suffix('.pb')  # *.pb path
     f_json = f'{f}/model.json'  # *.json path
 
-    cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
-          f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
-    subprocess.run(cmd.split())
+    args = [
+        'tensorflowjs_converter',
+        '--input_format=tf_frozen_model',
+        '--quantize_uint8' if int8 else '',
+        '--output_node_names=Identity,Identity_1,Identity_2,Identity_3',
+        str(f_pb),
+        str(f), ]
+    subprocess.run([arg for arg in args if arg], check=True)
 
     json = Path(f_json).read_text()
     with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
@@ -447,7 +506,7 @@ def run(
         weights=ROOT / 'yolov5s.pt',  # weights path
         imgsz=(640, 640),  # image (height, width)
         batch_size=1,  # batch size
-        device='0',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
         include=('torchscript', 'onnx'),  # include formats
         half=False,  # FP16 half-precision export
         inplace=False,  # set YOLOv5 Detect() inplace=True
@@ -466,7 +525,6 @@ def run(
         iou_thres=0.45,  # TF.js NMS: IoU threshold
         conf_thres=0.25,  # TF.js NMS: confidence threshold
 ):
-    global y
     t = time.time()
     include = [x.lower() for x in include]  # to lowercase
     fmts = tuple(export_formats()['Argument'][1:])  # --include arguments
@@ -543,28 +601,29 @@ def run(
                 f[8], _ = export_edgetpu(file)
             add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))
         if tfjs:
-            f[9], _ = export_tfjs(file)
+            f[9], _ = export_tfjs(file, int8)
     if paddle:  # PaddlePaddle
         f[10], _ = export_paddle(model, im, file, metadata)
 
     # Finish
     f = [str(x) for x in f if x]  # filter out '' and None
     if any(f):
         cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel))  # type
+        det &= not seg  # segmentation models inherit from SegmentationModel(DetectionModel)
         dir = Path('segment' if seg else 'classify' if cls else '')
         h = '--half' if half else ''  # --half FP16 inference arg
-        s = "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" if cls else \
-            "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" if seg else ''
+        s = '# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference' if cls else \
+            '# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference' if seg else ''
         LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
                     f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
                     f"\nDetect:          python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
                     f"\nValidate:        python {dir / 'val.py'} --weights {f[-1]} {h}"
                     f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')  {s}"
-                    f"\nVisualize:       https://netron.app")
+                    f'\nVisualize:       https://netron.app')
     return f  # return list of exported files/dirs
 
 
-def parse_opt():
+def parse_opt(known=False):
     parser = argparse.ArgumentParser()
     parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
     parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
@@ -578,7 +637,7 @@ def parse_opt():
     parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
     parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
     parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
-    parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
+    parser.add_argument('--opset', type=int, default=17, help='ONNX: opset version')
     parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
     parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
     parser.add_argument('--nms', action='store_true', help='TF: add NMS to model')
@@ -592,6 +651,16 @@ def parse_opt():
         nargs='+',
         default=['torchscript'],
         help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle')
-    opt = parser.parse_args()
+    opt = parser.parse_known_args()[0] if known else parser.parse_args()
     print_args(vars(opt))
     return opt
+
+
+def main(opt):
+    for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]):
+        run(**vars(opt))
+
+
+if __name__ == '__main__':
+    opt = parse_opt()
+    main(opt)

function/detect_object.py

@@ -3,39 +3,40 @@
 import sys
 from pathlib import Path
 
+import cv2
 import numpy as np
 import torch
 
 from models.common import DetectMultiBackend
 from utils.augmentations import (letterbox)
-from utils.general import (Profile, cv2,
-                           non_max_suppression, scale_boxes, xyxy2xywh)
+from utils.general import (non_max_suppression, scale_boxes, xyxy2xywh)
 from utils.torch_utils import select_device
 
 
-def load_model():
-    global weights
-    model_trt = TRTModule()
-    model_trt.load_state_dict(torch.load(weights))
-    device = select_device('cuda:0')
-    model_trt = model_trt.to(device)
-    model_trt.half()
-    return model_trt
+def load_model(img_size):
+    device = select_device('')
+    model = DetectMultiBackend(weights, device=device, dnn=False, data=data, fp16=True)
+    model.warmup(imgsz=(1, 3, *[img_size, img_size]))  # warmup
+    return model
 
 
 def interface_img(img, model):
     stride, names = model.stride, model.names
     h, w = img.shape[:2]
     img = cv2.resize(img, (w, h), interpolation=cv2.INTER_LINEAR)
+
     im = letterbox(img, 416, stride=stride, auto=True)[0]
     im = im.transpose((2, 0, 1))[::-1]
     im = np.ascontiguousarray(im)
-    im = torch.tensor(im).to(model.device, dtype=torch.float16)
+
+    im = torch.from_numpy(im).to(model.device)
+    im = im.half() if model.fp16 else im.float()
+    im = im.div(255.0).clamp(0.0, 1.0)  # 使用 .div() 方法进行除法操作，并使用 .clamp() 方法限制数据范围在 [0, 1] 之间
     if len(im.shape) == 3:
-        im = im.unsqueeze(0)
+        im = im[None]
+
     with torch.no_grad():
         pred = model(im, augment=False, visualize=False)
-        pred = pred.float()
         pred = non_max_suppression(pred, conf_thres, iou_thres, max_det=max_det)
 
     box_list = []
@@ -44,7 +45,7 @@ def interface_img(img, model):
         if len(det):
             det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], img.shape).round()
             for *xyxy, conf, cls in reversed(det):
-                xywh = ((torch.tensor(xyxy).float() + 0.5).int() / gn).tolist()
+                xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
                 line = (names[int(cls)], *xywh)
                 if 0.1 < (xywh[2] / xywh[3]) < 0.9 and xywh[3] < 0.8:
                     box_list.append(line)
@@ -57,8 +58,8 @@ def interface_img(img, model):
     sys.path.append(str(ROOT))
 info_dir = os.path.join(ROOT, 'information.csv')
 
-conf_thres = 0.5
-iou_thres = 0.5
+conf_thres = 0.55
+iou_thres = 0.45
 max_det = 1000
 
 with open(info_dir, 'r', encoding='utf-8', newline='') as fr: