diff --git a/docs/reference/nn/autoshape.md b/docs/reference/nn/autoshape.md
deleted file mode 100644
index b009e09..0000000
--- a/docs/reference/nn/autoshape.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-description: Detect 80+ object categories with bounding box coordinates and class probabilities using AutoShape in Ultralytics YOLO. Explore Detections now.
-keywords: Ultralytics, YOLO, docs, autoshape, detections, object detection, customized shapes, bounding boxes, computer vision
----
-
-## AutoShape
----
-### ::: ultralytics.nn.autoshape.AutoShape
-
-
-## Detections
----
-### ::: ultralytics.nn.autoshape.Detections
-
diff --git a/mkdocs.yml b/mkdocs.yml
index 75f8a74..219a059 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -257,7 +257,6 @@ nav:
- utils: reference/hub/utils.md
- nn:
- autobackend: reference/nn/autobackend.md
- - autoshape: reference/nn/autoshape.md
- modules:
- block: reference/nn/modules/block.md
- conv: reference/nn/modules/conv.md
diff --git a/ultralytics/nn/autoshape.py b/ultralytics/nn/autoshape.py
deleted file mode 100644
index d557f78..0000000
--- a/ultralytics/nn/autoshape.py
+++ /dev/null
@@ -1,244 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-"""
-Common modules
-"""
-
-from copy import copy
-from pathlib import Path
-
-import cv2
-import numpy as np
-import requests
-import torch
-import torch.nn as nn
-from PIL import Image, ImageOps
-from torch.cuda import amp
-
-from ultralytics.nn.autobackend import AutoBackend
-from ultralytics.yolo.data.augment import LetterBox
-from ultralytics.yolo.utils import LOGGER, colorstr
-from ultralytics.yolo.utils.files import increment_path
-from ultralytics.yolo.utils.ops import Profile, make_divisible, non_max_suppression, scale_boxes, xyxy2xywh
-from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
-from ultralytics.yolo.utils.torch_utils import copy_attr, smart_inference_mode
-
-
-class AutoShape(nn.Module):
- """YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS."""
- conf = 0.25 # NMS confidence threshold
- iou = 0.45 # NMS IoU threshold
- agnostic = False # NMS class-agnostic
- multi_label = False # NMS multiple labels per box
- classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
- max_det = 1000 # maximum number of detections per image
- amp = False # Automatic Mixed Precision (AMP) inference
-
- def __init__(self, model, verbose=True):
- """Initializes object and copies attributes from model object."""
- super().__init__()
- if verbose:
- LOGGER.info('Adding AutoShape... ')
- copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes
- self.dmb = isinstance(model, AutoBackend) # DetectMultiBackend() instance
- self.pt = not self.dmb or model.pt # PyTorch model
- self.model = model.eval()
- if self.pt:
- m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
- m.inplace = False # Detect.inplace=False for safe multithread inference
- m.export = True # do not output loss values
-
- def _apply(self, fn):
- """Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers."""
- self = super()._apply(fn)
- if self.pt:
- m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
- m.stride = fn(m.stride)
- m.grid = list(map(fn, m.grid))
- if isinstance(m.anchor_grid, list):
- m.anchor_grid = list(map(fn, m.anchor_grid))
- return self
-
- @smart_inference_mode()
- def forward(self, ims, size=640, augment=False, profile=False):
- """Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:."""
- # file: ims = 'data/images/zidane.jpg' # str or PosixPath
- # URI: = 'https://ultralytics.com/images/zidane.jpg'
- # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
- # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)
- # numpy: = np.zeros((640,1280,3)) # HWC
- # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
- # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
-
- dt = (Profile(), Profile(), Profile())
- with dt[0]:
- if isinstance(size, int): # expand
- size = (size, size)
- p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param
- autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference
- if isinstance(ims, torch.Tensor): # torch
- with amp.autocast(autocast):
- return self.model(ims.to(p.device).type_as(p), augment=augment) # inference
-
- # Preprocess
- n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images
- shape0, shape1, files = [], [], [] # image and inference shapes, filenames
- for i, im in enumerate(ims):
- f = f'image{i}' # filename
- if isinstance(im, (str, Path)): # filename or uri
- im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
- im = np.asarray(ImageOps.exif_transpose(im))
- elif isinstance(im, Image.Image): # PIL Image
- im, f = np.asarray(ImageOps.exif_transpose(im)), getattr(im, 'filename', f) or f
- files.append(Path(f).with_suffix('.jpg').name)
- if im.shape[0] < 5: # image in CHW
- im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
- im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input
- s = im.shape[:2] # HWC
- shape0.append(s) # image shape
- g = max(size) / max(s) # gain
- shape1.append([y * g for y in s])
- ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
- shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size # inf shape
- x = [LetterBox(shape1, auto=False)(image=im)['img'] for im in ims] # pad
- x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW
- x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
-
- with amp.autocast(autocast):
- # Inference
- with dt[1]:
- y = self.model(x, augment=augment) # forward
-
- # Postprocess
- with dt[2]:
- y = non_max_suppression(y if self.dmb else y[0],
- self.conf,
- self.iou,
- self.classes,
- self.agnostic,
- self.multi_label,
- max_det=self.max_det) # NMS
- for i in range(n):
- scale_boxes(shape1, y[i][:, :4], shape0[i])
-
- return Detections(ims, y, files, dt, self.names, x.shape)
-
-
-class Detections:
- """ YOLOv8 detections class for inference results"""
-
- def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
- """Initialize object attributes for YOLO detection results."""
- super().__init__()
- d = pred[0].device # device
- gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations
- self.ims = ims # list of images as numpy arrays
- self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
- self.names = names # class names
- self.files = files # image filenames
- self.times = times # profiling times
- self.xyxy = pred # xyxy pixels
- self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
- self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
- self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
- self.n = len(self.pred) # number of images (batch size)
- self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms)
- self.s = tuple(shape) # inference BCHW shape
-
- def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
- """Return performance metrics and optionally cropped/save images or results."""
- s, crops = '', []
- for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
- s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
- if pred.shape[0]:
- for c in pred[:, -1].unique():
- n = (pred[:, -1] == c).sum() # detections per class
- s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
- s = s.rstrip(', ')
- if show or save or render or crop:
- annotator = Annotator(im, example=str(self.names))
- for *box, conf, cls in reversed(pred): # xyxy, confidence, class
- label = f'{self.names[int(cls)]} {conf:.2f}'
- if crop:
- file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
- crops.append({
- 'box': box,
- 'conf': conf,
- 'cls': cls,
- 'label': label,
- 'im': save_one_box(box, im, file=file, save=save)})
- else: # all others
- annotator.box_label(box, label if labels else '', color=colors(cls))
- im = annotator.im
- else:
- s += '(no detections)'
-
- im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
- if show:
- im.show(self.files[i]) # show
- if save:
- f = self.files[i]
- im.save(save_dir / f) # save
- if i == self.n - 1:
- LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
- if render:
- self.ims[i] = np.asarray(im)
- if pprint:
- s = s.lstrip('\n')
- return f'{s}\nSpeed: %.1fms preprocess, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
- if crop:
- if save:
- LOGGER.info(f'Saved results to {save_dir}\n')
- return crops
-
- def show(self, labels=True):
- """Displays YOLO results with detected bounding boxes."""
- self._run(show=True, labels=labels) # show results
-
- def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
- """Save detection results with optional labels to specified directory."""
- save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir
- self._run(save=True, labels=labels, save_dir=save_dir) # save results
-
- def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
- """Crops images into detections and saves them if 'save' is True."""
- save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
- return self._run(crop=True, save=save, save_dir=save_dir) # crop results
-
- def render(self, labels=True):
- """Renders detected objects and returns images."""
- self._run(render=True, labels=labels) # render results
- return self.ims
-
- def pandas(self):
- """Return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])."""
- import pandas
- new = copy(self) # return copy
- ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
- cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
- for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
- a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
- setattr(new, k, [pandas.DataFrame(x, columns=c) for x in a])
- return new
-
- def tolist(self):
- """Return a list of Detections objects, i.e. 'for result in results.tolist():'."""
- r = range(self.n) # iterable
- x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
- # for d in x:
- # for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
- # setattr(d, k, getattr(d, k)[0]) # pop out of list
- return x
-
- def print(self):
- """Print the results of the `self._run()` function."""
- LOGGER.info(self.__str__())
-
- def __len__(self): # override len(results)
- return self.n
-
- def __str__(self): # override print(results)
- return self._run(pprint=True) # print results
-
- def __repr__(self):
- """Returns a printable representation of the object."""
- return f'YOLOv8 {self.__class__} instance\n' + self.__str__()