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318 lines
14 KiB
318 lines
14 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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import contextlib
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import math
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from pathlib import Path
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import torch
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from PIL import Image, ImageDraw, ImageFont
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from PIL import __version__ as pil_version
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from ultralytics.yolo.utils import threaded
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from .checks import check_font, check_version, is_ascii
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from .files import increment_path
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from .ops import clip_coords, scale_image, xywh2xyxy, xyxy2xywh
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class Colors:
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# Ultralytics color palette https://ultralytics.com/
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def __init__(self):
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# hex = matplotlib.colors.TABLEAU_COLORS.values()
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hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
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'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
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self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
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self.n = len(self.palette)
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def __call__(self, i, bgr=False):
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c = self.palette[int(i) % self.n]
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return (c[2], c[1], c[0]) if bgr else c
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@staticmethod
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def hex2rgb(h): # rgb order (PIL)
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return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
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colors = Colors() # create instance for 'from utils.plots import colors'
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class Annotator:
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# YOLOv8 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
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def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
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assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
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non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
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self.pil = pil or non_ascii
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if self.pil: # use PIL
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self.pil_9_2_0_check = check_version(pil_version, '9.2.0') # deprecation check
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self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
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self.draw = ImageDraw.Draw(self.im)
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try:
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font = check_font('Arial.Unicode.ttf' if non_ascii else font)
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size = font_size or max(round(sum(self.im.size) / 2 * 0.035), 12)
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self.font = ImageFont.truetype(str(font), size)
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except Exception:
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self.font = ImageFont.load_default()
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else: # use cv2
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self.im = im
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self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
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def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
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# Add one xyxy box to image with label
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if isinstance(box, torch.Tensor):
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box = box.tolist()
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if self.pil or not is_ascii(label):
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self.draw.rectangle(box, width=self.lw, outline=color) # box
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if label:
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if self.pil_9_2_0_check:
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_, _, w, h = self.font.getbbox(label) # text width, height (New)
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else:
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w, h = self.font.getsize(label) # text width, height (Old, deprecated in 9.2.0)
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outside = box[1] - h >= 0 # label fits outside box
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self.draw.rectangle(
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(box[0], box[1] - h if outside else box[1], box[0] + w + 1,
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box[1] + 1 if outside else box[1] + h + 1),
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fill=color,
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)
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# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
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self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
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else: # cv2
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p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
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cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
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if label:
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tf = max(self.lw - 1, 1) # font thickness
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w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
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outside = p1[1] - h >= 3
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p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
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cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
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cv2.putText(self.im,
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label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
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0,
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self.lw / 3,
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txt_color,
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thickness=tf,
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lineType=cv2.LINE_AA)
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def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
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"""Plot masks at once.
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Args:
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masks (tensor): predicted masks on cuda, shape: [n, h, w]
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colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
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im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
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alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
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"""
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if self.pil:
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# convert to numpy first
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self.im = np.asarray(self.im).copy()
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if len(masks) == 0:
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self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
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colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
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colors = colors[:, None, None] # shape(n,1,1,3)
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masks = masks.unsqueeze(3) # shape(n,h,w,1)
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masks_color = masks * (colors * alpha) # shape(n,h,w,3)
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inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1)
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mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3)
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im_gpu = im_gpu.flip(dims=[0]) # flip channel
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im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3)
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im_gpu = im_gpu * inv_alph_masks[-1] + mcs
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im_mask = (im_gpu * 255)
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im_mask_np = im_mask.byte().cpu().numpy()
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self.im[:] = im_mask_np if retina_masks else scale_image(im_gpu.shape, im_mask_np, self.im.shape)
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if self.pil:
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# convert im back to PIL and update draw
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self.fromarray(self.im)
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def rectangle(self, xy, fill=None, outline=None, width=1):
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# Add rectangle to image (PIL-only)
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self.draw.rectangle(xy, fill, outline, width)
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def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
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# Add text to image (PIL-only)
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if anchor == 'bottom': # start y from font bottom
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w, h = self.font.getsize(text) # text width, height
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xy[1] += 1 - h
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if self.pil:
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self.draw.text(xy, text, fill=txt_color, font=self.font)
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else:
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tf = max(self.lw - 1, 1) # font thickness
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cv2.putText(self.im, text, xy, 0, self.lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
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def fromarray(self, im):
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# Update self.im from a numpy array
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self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
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self.draw = ImageDraw.Draw(self.im)
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def result(self):
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# Return annotated image as array
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return np.asarray(self.im)
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def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
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# Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
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xyxy = torch.tensor(xyxy).view(-1, 4)
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b = xyxy2xywh(xyxy) # boxes
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if square:
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b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
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b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
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xyxy = xywh2xyxy(b).long()
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clip_coords(xyxy, im.shape)
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crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
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if save:
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file.parent.mkdir(parents=True, exist_ok=True) # make directory
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f = str(increment_path(file).with_suffix('.jpg'))
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# cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
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Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB
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return crop
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@threaded
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def plot_images(images,
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batch_idx,
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cls,
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bboxes,
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masks=np.zeros(0, dtype=np.uint8),
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paths=None,
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fname='images.jpg',
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names=None):
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# Plot image grid with labels
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if isinstance(images, torch.Tensor):
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images = images.cpu().float().numpy()
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if isinstance(cls, torch.Tensor):
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cls = cls.cpu().numpy()
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if isinstance(bboxes, torch.Tensor):
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bboxes = bboxes.cpu().numpy()
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if isinstance(masks, torch.Tensor):
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masks = masks.cpu().numpy().astype(int)
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if isinstance(batch_idx, torch.Tensor):
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batch_idx = batch_idx.cpu().numpy()
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max_size = 1920 # max image size
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max_subplots = 16 # max image subplots, i.e. 4x4
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bs, _, h, w = images.shape # batch size, _, height, width
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bs = min(bs, max_subplots) # limit plot images
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ns = np.ceil(bs ** 0.5) # number of subplots (square)
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if np.max(images[0]) <= 1:
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images *= 255 # de-normalise (optional)
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# Build Image
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mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
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for i, im in enumerate(images):
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if i == max_subplots: # if last batch has fewer images than we expect
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break
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x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
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im = im.transpose(1, 2, 0)
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mosaic[y:y + h, x:x + w, :] = im
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# Resize (optional)
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scale = max_size / ns / max(h, w)
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if scale < 1:
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h = math.ceil(scale * h)
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w = math.ceil(scale * w)
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mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
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# Annotate
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fs = int((h + w) * ns * 0.01) # font size
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annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
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for i in range(i + 1):
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x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
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annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
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if paths:
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annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
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if len(cls) > 0:
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idx = batch_idx == i
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boxes = xywh2xyxy(bboxes[idx, :4]).T
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classes = cls[idx].astype('int')
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labels = bboxes.shape[1] == 4 # labels if no conf column
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conf = None if labels else bboxes[idx, 4] # check for confidence presence (label vs pred)
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if boxes.shape[1]:
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if boxes.max() <= 1.01: # if normalized with tolerance 0.01
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boxes[[0, 2]] *= w # scale to pixels
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boxes[[1, 3]] *= h
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elif scale < 1: # absolute coords need scale if image scales
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boxes *= scale
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boxes[[0, 2]] += x
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boxes[[1, 3]] += y
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for j, box in enumerate(boxes.T.tolist()):
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c = classes[j]
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color = colors(c)
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c = names[c] if names else c
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if labels or conf[j] > 0.25: # 0.25 conf thresh
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label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
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annotator.box_label(box, label, color=color)
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# Plot masks
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if len(masks):
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if masks.max() > 1.0: # mean that masks are overlap
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image_masks = masks[[i]] # (1, 640, 640)
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nl = idx.sum()
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index = np.arange(nl).reshape(nl, 1, 1) + 1
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image_masks = np.repeat(image_masks, nl, axis=0)
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image_masks = np.where(image_masks == index, 1.0, 0.0)
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else:
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image_masks = masks[idx]
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im = np.asarray(annotator.im).copy()
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for j, box in enumerate(boxes.T.tolist()):
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if labels or conf[j] > 0.25: # 0.25 conf thresh
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color = colors(classes[j])
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mh, mw = image_masks[j].shape
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if mh != h or mw != w:
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mask = image_masks[j].astype(np.uint8)
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mask = cv2.resize(mask, (w, h))
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mask = mask.astype(bool)
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else:
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mask = image_masks[j].astype(bool)
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with contextlib.suppress(Exception):
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im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
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annotator.fromarray(im)
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annotator.im.save(fname) # save
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def plot_results(file='path/to/results.csv', dir='', segment=False):
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# Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
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save_dir = Path(file).parent if file else Path(dir)
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if segment:
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fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
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index = [1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]
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else:
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fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
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index = [1, 2, 3, 4, 5, 8, 9, 10, 6, 7]
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ax = ax.ravel()
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files = list(save_dir.glob('results*.csv'))
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assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
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for f in files:
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try:
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data = pd.read_csv(f)
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s = [x.strip() for x in data.columns]
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x = data.values[:, 0]
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for i, j in enumerate(index):
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y = data.values[:, j].astype('float')
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# y[y == 0] = np.nan # don't show zero values
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ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
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ax[i].set_title(s[j], fontsize=12)
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# if j in [8, 9, 10]: # share train and val loss y axes
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# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
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except Exception as e:
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print(f'Warning: Plotting error for {f}: {e}')
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ax[1].legend()
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fig.savefig(save_dir / 'results.png', dpi=200)
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plt.close()
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def output_to_target(output, max_det=300):
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# Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting
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targets = []
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for i, o in enumerate(output):
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box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
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j = torch.full((conf.shape[0], 1), i)
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targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
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targets = torch.cat(targets, 0).numpy()
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return targets[:, 0], targets[:, 1], targets[:, 2:]
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