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|
- # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
- """
- Plotting utils
- """
- import math
- import os
- from copy import copy
- from pathlib import Path
- import cv2
- import matplotlib
- import matplotlib.pyplot as plt
- import numpy as np
- import pandas as pd
- import seaborn as sn
- import torch
- from PIL import Image, ImageDraw, ImageFont
- from utils.general import user_config_dir, is_ascii, xywh2xyxy, xyxy2xywh
- from utils.metrics import fitness
- # Settings
- CONFIG_DIR = user_config_dir() # Ultralytics settings dir
- RANK = int(os.getenv('RANK', -1))
- matplotlib.rc('font', **{'size': 11})
- matplotlib.use('Agg') # for writing to files only
- class Colors:
- # Ultralytics color palette https://ultralytics.com/
- def __init__(self):
- # hex = matplotlib.colors.TABLEAU_COLORS.values()
- hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
- '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
- self.palette = [self.hex2rgb('#' + c) for c in hex]
- self.n = len(self.palette)
- def __call__(self, i, bgr=False):
- c = self.palette[int(i) % self.n]
- return (c[2], c[1], c[0]) if bgr else c
- @staticmethod
- def hex2rgb(h): # rgb order (PIL)
- return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
- colors = Colors() # create instance for 'from utils.plots import colors'
- def check_font(font='Arial.ttf', size=10):
- # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
- font = Path(font)
- font = font if font.exists() else (CONFIG_DIR / font.name)
- try:
- return ImageFont.truetype(str(font) if font.exists() else font.name, size)
- except Exception as e: # download if missing
- url = "https://ultralytics.com/assets/" + font.name
- print(f'Downloading {url} to {font}...')
- torch.hub.download_url_to_file(url, str(font), progress=False)
- return ImageFont.truetype(str(font), size)
- class Annotator:
- if RANK in (-1, 0):
- check_font() # download TTF if necessary
- # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
- def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=True):
- assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
- self.pil = pil
- if self.pil: # use PIL
- self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
- self.draw = ImageDraw.Draw(self.im)
- self.font = check_font(font, size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
- else: # use cv2
- self.im = im
- self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
- def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
- # Add one xyxy box to image with label
- if self.pil or not is_ascii(label):
- self.draw.rectangle(box, width=self.lw, outline=color) # box
- if label:
- w, h = self.font.getsize(label) # text width, height
- outside = box[1] - h >= 0 # label fits outside box
- self.draw.rectangle([box[0],
- box[1] - h if outside else box[1],
- box[0] + w + 1,
- box[1] + 1 if outside else box[1] + h + 1], fill=color)
- # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
- self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
- else: # cv2
- p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
- cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
- if label:
- tf = max(self.lw - 1, 1) # font thickness
- w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
- outside = p1[1] - h - 3 >= 0 # label fits outside box
- p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
- cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
- cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color,
- thickness=tf, lineType=cv2.LINE_AA)
- def rectangle(self, xy, fill=None, outline=None, width=1):
- # Add rectangle to image (PIL-only)
- self.draw.rectangle(xy, fill, outline, width)
- def text(self, xy, text, txt_color=(255, 255, 255)):
- # Add text to image (PIL-only)
- w, h = self.font.getsize(text) # text width, height
- self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font)
- def result(self):
- # Return annotated image as array
- return np.asarray(self.im)
- def hist2d(x, y, n=100):
- # 2d histogram used in labels.png and evolve.png
- xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
- hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
- xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
- yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
- return np.log(hist[xidx, yidx])
- def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
- from scipy.signal import butter, filtfilt
- # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
- def butter_lowpass(cutoff, fs, order):
- nyq = 0.5 * fs
- normal_cutoff = cutoff / nyq
- return butter(order, normal_cutoff, btype='low', analog=False)
- b, a = butter_lowpass(cutoff, fs, order=order)
- return filtfilt(b, a, data) # forward-backward filter
- def output_to_target(output):
- # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
- targets = []
- for i, o in enumerate(output):
- for *box, conf, cls in o.cpu().numpy():
- targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])
- return np.array(targets)
- def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=1920, max_subplots=16):
- # Plot image grid with labels
- if isinstance(images, torch.Tensor):
- images = images.cpu().float().numpy()
- if isinstance(targets, torch.Tensor):
- targets = targets.cpu().numpy()
- if np.max(images[0]) <= 1:
- images *= 255.0 # de-normalise (optional)
- bs, _, h, w = images.shape # batch size, _, height, width
- bs = min(bs, max_subplots) # limit plot images
- ns = np.ceil(bs ** 0.5) # number of subplots (square)
- # Build Image
- mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
- for i, im in enumerate(images):
- if i == max_subplots: # if last batch has fewer images than we expect
- break
- x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
- im = im.transpose(1, 2, 0)
- mosaic[y:y + h, x:x + w, :] = im
- # Resize (optional)
- scale = max_size / ns / max(h, w)
- if scale < 1:
- h = math.ceil(scale * h)
- w = math.ceil(scale * w)
- mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
- # Annotate
- fs = int((h + w) * ns * 0.01) # font size
- annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs)
- for i in range(i + 1):
- x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
- annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
- if paths:
- annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
- if len(targets) > 0:
- ti = targets[targets[:, 0] == i] # image targets
- boxes = xywh2xyxy(ti[:, 2:6]).T
- classes = ti[:, 1].astype('int')
- labels = ti.shape[1] == 6 # labels if no conf column
- conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)
- if boxes.shape[1]:
- if boxes.max() <= 1.01: # if normalized with tolerance 0.01
- boxes[[0, 2]] *= w # scale to pixels
- boxes[[1, 3]] *= h
- elif scale < 1: # absolute coords need scale if image scales
- boxes *= scale
- boxes[[0, 2]] += x
- boxes[[1, 3]] += y
- for j, box in enumerate(boxes.T.tolist()):
- cls = classes[j]
- color = colors(cls)
- cls = names[cls] if names else cls
- if labels or conf[j] > 0.25: # 0.25 conf thresh
- label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
- annotator.box_label(box, label, color=color)
- annotator.im.save(fname) # save
- def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
- # Plot LR simulating training for full epochs
- optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals
- y = []
- for _ in range(epochs):
- scheduler.step()
- y.append(optimizer.param_groups[0]['lr'])
- plt.plot(y, '.-', label='LR')
- plt.xlabel('epoch')
- plt.ylabel('LR')
- plt.grid()
- plt.xlim(0, epochs)
- plt.ylim(0)
- plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
- plt.close()
- def plot_val_txt(): # from utils.plots import *; plot_val()
- # Plot val.txt histograms
- x = np.loadtxt('val.txt', dtype=np.float32)
- box = xyxy2xywh(x[:, :4])
- cx, cy = box[:, 0], box[:, 1]
- fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
- ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
- ax.set_aspect('equal')
- plt.savefig('hist2d.png', dpi=300)
- fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
- ax[0].hist(cx, bins=600)
- ax[1].hist(cy, bins=600)
- plt.savefig('hist1d.png', dpi=200)
- def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
- # Plot targets.txt histograms
- x = np.loadtxt('targets.txt', dtype=np.float32).T
- s = ['x targets', 'y targets', 'width targets', 'height targets']
- fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
- ax = ax.ravel()
- for i in range(4):
- ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
- ax[i].legend()
- ax[i].set_title(s[i])
- plt.savefig('targets.jpg', dpi=200)
- def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study()
- # Plot file=study.txt generated by val.py (or plot all study*.txt in dir)
- save_dir = Path(file).parent if file else Path(dir)
- plot2 = False # plot additional results
- if plot2:
- ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
- fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
- # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
- for f in sorted(save_dir.glob('study*.txt')):
- y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
- x = np.arange(y.shape[1]) if x is None else np.array(x)
- if plot2:
- s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']
- for i in range(7):
- ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
- ax[i].set_title(s[i])
- j = y[3].argmax() + 1
- ax2.plot(y[5, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,
- label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
- ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
- 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
- ax2.grid(alpha=0.2)
- ax2.set_yticks(np.arange(20, 60, 5))
- ax2.set_xlim(0, 57)
- ax2.set_ylim(30, 55)
- ax2.set_xlabel('GPU Speed (ms/img)')
- ax2.set_ylabel('COCO AP val')
- ax2.legend(loc='lower right')
- f = save_dir / 'study.png'
- print(f'Saving {f}...')
- plt.savefig(f, dpi=300)
- def plot_labels(labels, names=(), save_dir=Path('')):
- # plot dataset labels
- print('Plotting labels... ')
- c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes
- nc = int(c.max() + 1) # number of classes
- x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
- # seaborn correlogram
- sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
- plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
- plt.close()
- # matplotlib labels
- matplotlib.use('svg') # faster
- ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
- y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
- # [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # update colors bug #3195
- ax[0].set_ylabel('instances')
- if 0 < len(names) < 30:
- ax[0].set_xticks(range(len(names)))
- ax[0].set_xticklabels(names, rotation=90, fontsize=10)
- else:
- ax[0].set_xlabel('classes')
- sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
- sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
- # rectangles
- labels[:, 1:3] = 0.5 # center
- labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
- img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
- for cls, *box in labels[:1000]:
- ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot
- ax[1].imshow(img)
- ax[1].axis('off')
- for a in [0, 1, 2, 3]:
- for s in ['top', 'right', 'left', 'bottom']:
- ax[a].spines[s].set_visible(False)
- plt.savefig(save_dir / 'labels.jpg', dpi=200)
- matplotlib.use('Agg')
- plt.close()
- def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
- # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
- ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
- s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
- files = list(Path(save_dir).glob('frames*.txt'))
- for fi, f in enumerate(files):
- try:
- results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows
- n = results.shape[1] # number of rows
- x = np.arange(start, min(stop, n) if stop else n)
- results = results[:, x]
- t = (results[0] - results[0].min()) # set t0=0s
- results[0] = x
- for i, a in enumerate(ax):
- if i < len(results):
- label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
- a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
- a.set_title(s[i])
- a.set_xlabel('time (s)')
- # if fi == len(files) - 1:
- # a.set_ylim(bottom=0)
- for side in ['top', 'right']:
- a.spines[side].set_visible(False)
- else:
- a.remove()
- except Exception as e:
- print('Warning: Plotting error for %s; %s' % (f, e))
- ax[1].legend()
- plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
- def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve()
- # Plot evolve.csv hyp evolution results
- evolve_csv = Path(evolve_csv)
- data = pd.read_csv(evolve_csv)
- keys = [x.strip() for x in data.columns]
- x = data.values
- f = fitness(x)
- j = np.argmax(f) # max fitness index
- plt.figure(figsize=(10, 12), tight_layout=True)
- matplotlib.rc('font', **{'size': 8})
- for i, k in enumerate(keys[7:]):
- v = x[:, 7 + i]
- mu = v[j] # best single result
- plt.subplot(6, 5, i + 1)
- plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
- plt.plot(mu, f.max(), 'k+', markersize=15)
- plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters
- if i % 5 != 0:
- plt.yticks([])
- print('%15s: %.3g' % (k, mu))
- f = evolve_csv.with_suffix('.png') # filename
- plt.savefig(f, dpi=200)
- plt.close()
- print(f'Saved {f}')
- def plot_results(file='path/to/results.csv', dir=''):
- # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
- save_dir = Path(file).parent if file else Path(dir)
- fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
- ax = ax.ravel()
- files = list(save_dir.glob('results*.csv'))
- assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
- for fi, f in enumerate(files):
- try:
- data = pd.read_csv(f)
- s = [x.strip() for x in data.columns]
- x = data.values[:, 0]
- for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
- y = data.values[:, j]
- # y[y == 0] = np.nan # don't show zero values
- ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
- ax[i].set_title(s[j], fontsize=12)
- # if j in [8, 9, 10]: # share train and val loss y axes
- # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
- except Exception as e:
- print(f'Warning: Plotting error for {f}: {e}')
- ax[1].legend()
- fig.savefig(save_dir / 'results.png', dpi=200)
- plt.close()
- def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
- """
- x: Features to be visualized
- module_type: Module type
- stage: Module stage within model
- n: Maximum number of feature maps to plot
- save_dir: Directory to save results
- """
- if 'Detect' not in module_type:
- batch, channels, height, width = x.shape # batch, channels, height, width
- if height > 1 and width > 1:
- f = f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
- blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
- n = min(n, channels) # number of plots
- fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols
- ax = ax.ravel()
- plt.subplots_adjust(wspace=0.05, hspace=0.05)
- for i in range(n):
- ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
- ax[i].axis('off')
- print(f'Saving {save_dir / f}... ({n}/{channels})')
- plt.savefig(save_dir / f, dpi=300, bbox_inches='tight')
- plt.close()
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