General refactoring and improvements (#373)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>pull/381/head
Glenn Jocher
2 years ago
committed by
GitHub
parent
ac628c0d3e
commit
583eac0e80
18 changed files with 305 additions and 310 deletions
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# Ultralytics YOLO 🚀, GPL-3.0 license |
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""" |
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Common modules |
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""" |
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from copy import copy |
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from pathlib import Path |
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import cv2 |
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import numpy as np |
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import pandas as pd |
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import requests |
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import torch |
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import torch.nn as nn |
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from PIL import Image, ImageOps |
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from torch.cuda import amp |
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from ultralytics.nn.autobackend import AutoBackend |
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from ultralytics.yolo.data.augment import LetterBox |
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from ultralytics.yolo.utils import LOGGER, colorstr |
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from ultralytics.yolo.utils.files import increment_path |
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from ultralytics.yolo.utils.ops import Profile, make_divisible, non_max_suppression, scale_boxes, xyxy2xywh |
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from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box |
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from ultralytics.yolo.utils.torch_utils import copy_attr, smart_inference_mode |
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class AutoShape(nn.Module): |
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# YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS |
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conf = 0.25 # NMS confidence threshold |
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iou = 0.45 # NMS IoU threshold |
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agnostic = False # NMS class-agnostic |
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multi_label = False # NMS multiple labels per box |
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classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs |
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max_det = 1000 # maximum number of detections per image |
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amp = False # Automatic Mixed Precision (AMP) inference |
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def __init__(self, model, verbose=True): |
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super().__init__() |
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if verbose: |
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LOGGER.info('Adding AutoShape... ') |
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copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes |
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self.dmb = isinstance(model, AutoBackend) # DetectMultiBackend() instance |
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self.pt = not self.dmb or model.pt # PyTorch model |
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self.model = model.eval() |
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if self.pt: |
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m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() |
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m.inplace = False # Detect.inplace=False for safe multithread inference |
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m.export = True # do not output loss values |
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def _apply(self, fn): |
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# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers |
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self = super()._apply(fn) |
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if self.pt: |
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m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() |
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m.stride = fn(m.stride) |
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m.grid = list(map(fn, m.grid)) |
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if isinstance(m.anchor_grid, list): |
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m.anchor_grid = list(map(fn, m.anchor_grid)) |
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return self |
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@smart_inference_mode() |
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def forward(self, ims, size=640, augment=False, profile=False): |
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# Inference from various sources. For size(height=640, width=1280), RGB images example inputs are: |
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# file: ims = 'data/images/zidane.jpg' # str or PosixPath |
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# URI: = 'https://ultralytics.com/images/zidane.jpg' |
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# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) |
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# PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) |
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# numpy: = np.zeros((640,1280,3)) # HWC |
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# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) |
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# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images |
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dt = (Profile(), Profile(), Profile()) |
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with dt[0]: |
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if isinstance(size, int): # expand |
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size = (size, size) |
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p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param |
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autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference |
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if isinstance(ims, torch.Tensor): # torch |
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with amp.autocast(autocast): |
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return self.model(ims.to(p.device).type_as(p), augment=augment) # inference |
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# Pre-process |
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n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images |
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shape0, shape1, files = [], [], [] # image and inference shapes, filenames |
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for i, im in enumerate(ims): |
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f = f'image{i}' # filename |
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if isinstance(im, (str, Path)): # filename or uri |
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im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im |
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im = np.asarray(ImageOps.exif_transpose(im)) |
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elif isinstance(im, Image.Image): # PIL Image |
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im, f = np.asarray(ImageOps.exif_transpose(im)), getattr(im, 'filename', f) or f |
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files.append(Path(f).with_suffix('.jpg').name) |
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if im.shape[0] < 5: # image in CHW |
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im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) |
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im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input |
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s = im.shape[:2] # HWC |
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shape0.append(s) # image shape |
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g = max(size) / max(s) # gain |
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shape1.append([y * g for y in s]) |
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ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update |
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shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size # inf shape |
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x = [LetterBox(shape1, auto=False)(image=im)["img"] for im in ims] # pad |
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x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW |
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x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 |
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with amp.autocast(autocast): |
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# Inference |
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with dt[1]: |
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y = self.model(x, augment=augment) # forward |
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# Post-process |
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with dt[2]: |
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y = non_max_suppression(y if self.dmb else y[0], |
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self.conf, |
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self.iou, |
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self.classes, |
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self.agnostic, |
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self.multi_label, |
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max_det=self.max_det) # NMS |
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for i in range(n): |
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scale_boxes(shape1, y[i][:, :4], shape0[i]) |
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return Detections(ims, y, files, dt, self.names, x.shape) |
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class Detections: |
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# YOLOv8 detections class for inference results |
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def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): |
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super().__init__() |
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d = pred[0].device # device |
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gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations |
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self.ims = ims # list of images as numpy arrays |
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self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) |
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self.names = names # class names |
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self.files = files # image filenames |
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self.times = times # profiling times |
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self.xyxy = pred # xyxy pixels |
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self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels |
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self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized |
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self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized |
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self.n = len(self.pred) # number of images (batch size) |
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self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms) |
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self.s = tuple(shape) # inference BCHW shape |
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def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')): |
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s, crops = '', [] |
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for i, (im, pred) in enumerate(zip(self.ims, self.pred)): |
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s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string |
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if pred.shape[0]: |
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for c in pred[:, -1].unique(): |
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n = (pred[:, -1] == c).sum() # detections per class |
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s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string |
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s = s.rstrip(', ') |
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if show or save or render or crop: |
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annotator = Annotator(im, example=str(self.names)) |
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for *box, conf, cls in reversed(pred): # xyxy, confidence, class |
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label = f'{self.names[int(cls)]} {conf:.2f}' |
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if crop: |
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file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None |
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crops.append({ |
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'box': box, |
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'conf': conf, |
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'cls': cls, |
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'label': label, |
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'im': save_one_box(box, im, file=file, save=save)}) |
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else: # all others |
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annotator.box_label(box, label if labels else '', color=colors(cls)) |
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im = annotator.im |
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else: |
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s += '(no detections)' |
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im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np |
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if show: |
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im.show(self.files[i]) # show |
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if save: |
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f = self.files[i] |
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im.save(save_dir / f) # save |
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if i == self.n - 1: |
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LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") |
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if render: |
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self.ims[i] = np.asarray(im) |
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if pprint: |
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s = s.lstrip('\n') |
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return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t |
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if crop: |
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if save: |
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LOGGER.info(f'Saved results to {save_dir}\n') |
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return crops |
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def show(self, labels=True): |
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self._run(show=True, labels=labels) # show results |
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def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False): |
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save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir |
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self._run(save=True, labels=labels, save_dir=save_dir) # save results |
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def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False): |
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save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None |
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return self._run(crop=True, save=save, save_dir=save_dir) # crop results |
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def render(self, labels=True): |
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self._run(render=True, labels=labels) # render results |
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return self.ims |
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def pandas(self): |
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# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) |
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new = copy(self) # return copy |
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ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns |
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cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns |
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for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): |
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a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update |
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setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) |
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return new |
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def tolist(self): |
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# return a list of Detections objects, i.e. 'for result in results.tolist():' |
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r = range(self.n) # iterable |
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x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] |
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# for d in x: |
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# for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: |
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# setattr(d, k, getattr(d, k)[0]) # pop out of list |
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return x |
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def print(self): |
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LOGGER.info(self.__str__()) |
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def __len__(self): # override len(results) |
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return self.n |
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def __str__(self): # override print(results) |
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return self._run(pprint=True) # print results |
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def __repr__(self): |
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return f'YOLOv8 {self.__class__} instance\n' + self.__str__() |
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print('works') |
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