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# YOLOv5 π by Ultralytics, GPL-3.0 license | |
""" | |
Plotting utils | |
""" | |
import math | |
import os | |
from copy import copy | |
from pathlib import Path | |
from urllib.error import URLError | |
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 (CONFIG_DIR, FONT, LOGGER, Timeout, check_font, check_requirements, clip_coords, | |
increment_path, is_ascii, is_chinese, try_except, xywh2xyxy, xyxy2xywh) | |
from utils.metrics import fitness | |
# Settings | |
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 | |
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_pil_font(font=FONT, 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: # download if missing | |
try: | |
check_font(font) | |
return ImageFont.truetype(str(font), size) | |
except TypeError: | |
check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374 | |
except URLError: # not online | |
return ImageFont.load_default() | |
class Annotator: | |
if RANK in (-1, 0): | |
check_pil_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=False, example='abc'): | |
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.' | |
self.pil = pil or not is_ascii(example) or is_chinese(example) | |
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_pil_font(font='Arial.Unicode.ttf' if is_chinese(example) else 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 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 = save_dir / 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') | |
LOGGER.info(f'Saving {f}... ({n}/{channels})') | |
plt.savefig(f, dpi=300, bbox_inches='tight') | |
plt.close() | |
np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save | |
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 # 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, pil=True, example=names) | |
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=f'{x[i].mean():.3g} +/- {x[i].std():.3g}') | |
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 [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', '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(25, 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) | |
# known issue https://github.com/ultralytics/yolov5/issues/5395 | |
# known issue https://github.com/ultralytics/yolov5/issues/5611 | |
def plot_labels(labels, names=(), save_dir=Path('')): | |
# plot dataset labels | |
LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") | |
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) | |
try: # color histogram bars by class | |
[y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195 | |
except Exception: | |
pass | |
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 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}) | |
print(f'Best results from row {j} of {evolve_csv}:') | |
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(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters | |
if i % 5 != 0: | |
plt.yticks([]) | |
print(f'{k:>15}: {mu:.3g}') | |
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: | |
LOGGER.info(f'Warning: Plotting error for {f}: {e}') | |
ax[1].legend() | |
fig.savefig(save_dir / 'results.png', dpi=200) | |
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(f'Warning: Plotting error for {f}; {e}') | |
ax[1].legend() | |
plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) | |
def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True): | |
# Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop | |
xyxy = torch.tensor(xyxy).view(-1, 4) | |
b = xyxy2xywh(xyxy) # boxes | |
if square: | |
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square | |
b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad | |
xyxy = xywh2xyxy(b).long() | |
clip_coords(xyxy, im.shape) | |
crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)] | |
if save: | |
file.parent.mkdir(parents=True, exist_ok=True) # make directory | |
f = str(increment_path(file).with_suffix('.jpg')) | |
# cv2.imwrite(f, crop) # https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue | |
Image.fromarray(cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)).save(f, quality=95, subsampling=0) | |
return crop | |