plots.py

# 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 (LOGGER, Timeout, check_requirements, clip_coords, increment_path, is_ascii, is_chinese,
                           try_except, user_config_dir, xywh2xyxy, xyxy2xywh)
from utils.metrics import fitness
from utils.rboxs_utils import poly2hbb, poly2rbox, rbox2poly

# 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)
        try:
            return ImageFont.truetype(str(font), size)
        except TypeError:
            check_requirements('Pillow>=8.4.0')  # known issue https://github.com/ultralytics/yolov5/issues/5374


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=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.im_cv2 = im
            self.draw = ImageDraw.Draw(self.im)
            self.font = check_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.im_cv2 = 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 poly_label(self, poly, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
        # if self.pil or not is_ascii(label):
        #     self.draw.polygon(xy=poly, outline=color)
        #     if label:
        #         xmax, xmin, ymax, ymin = max(poly[0::2]), min(poly[0::2]), max(poly[1::2]), min(poly[1::2])
        #         x_label, y_label = (xmax + xmin)/2, (ymax + ymin)/2
        #         w, h = self.font.getsize(label)  # text width, height
        #         outside = ymin - h >= 0  # label fits outside box
        #         self.draw.rectangle([x_label,
        #                              y_label - h if outside else y_label,
        #                              x_label + w + 1,
        #                              y_label + 1 if outside else y_label + h + 1], fill=color)
        #         self.draw.text((x_label, y_label - h if outside else y_label), label, fill=txt_color, font=self.font)
        # else:
            if isinstance(poly, torch.Tensor):
                poly = poly.cpu().numpy()
            if isinstance(poly[0], torch.Tensor):
                poly = [x.cpu().numpy() for x in poly]
            polygon_list = np.array([(poly[0], poly[1]), (poly[2], poly[3]), \
                    (poly[4], poly[5]), (poly[6], poly[7])], np.int32)
            cv2.drawContours(image=self.im_cv2, contours=[polygon_list], contourIdx=-1, color=color, thickness=self.lw)
            if label:
                tf = max(self.lw - 1, 1)  # font thicknes
                xmax, xmin, ymax, ymin = max(poly[0::2]), min(poly[0::2]), max(poly[1::2]), min(poly[1::2])
                x_label, y_label = int((xmax + xmin)/2), int((ymax + ymin)/2)
                w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
                cv2.rectangle(
                                self.im_cv2,
                                (x_label, y_label),
                                (x_label + w + 1, y_label + int(1.5*h)),
                                color, -1, cv2.LINE_AA
                            )
                cv2.putText(self.im_cv2, label, (x_label, y_label + h), 0, self.lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
            self.im = self.im_cv2 if isinstance(self.im_cv2, Image.Image) else Image.fromarray(self.im_cv2)

    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')

            print(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): #list*(n, [xylsθ, conf, cls]) θ ∈ [-pi/2, pi/2)
    # Convert model output to target format [batch_id, class_id, x, y, l, s, theta, conf]
    targets = []
    for i, o in enumerate(output):
        for *rbox, conf, cls in o.cpu().numpy():
            targets.append([i, cls, *list(*(np.array(rbox)[None])), conf])
    return np.array(targets)


def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=2048, max_subplots=4):
    """
    Args:
        imgs (tensor): (b, 3, height, width)
        targets_train (tensor): (n_targets, [batch_id clsid cx cy l s theta gaussian_θ_labels]) θ∈[-pi/2, pi/2)
        targets_pred (array): (n, [batch_id, class_id, cx, cy, l, s, theta, conf]) θ∈[-pi/2, pi/2)
        paths (list[str,...]): (b)
        fname (str): (1) 
        names :

    """
    # 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)
    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, (n, [img_index clsid cx cy l s theta gaussian_θ_labels])
            # boxes = xywh2xyxy(ti[:, 2:6]).T
            rboxes = ti[:, 2:7]
            classes = ti[:, 1].astype('int')
            # labels = ti.shape[1] == 6  # labels if no conf column
            labels = ti.shape[1] == 187  # labels if no conf column
            # conf = None if labels else ti[:, 6]  # check for confidence presence (label vs pred)
            conf = None if labels else ti[:, 7]  # 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
            polys = rbox2poly(rboxes)
            if scale < 1:
                polys *= scale
            # boxes[[0, 2]] += x
            # boxes[[1, 3]] += y
            polys[:, [0, 2, 4, 6]] += x
            polys[:, [1, 3, 5, 7]] += 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)
            for j, poly in enumerate(polys.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.poly_label(poly, 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', '[email protected]', '[email protected]:.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)


@try_except  # known issue https://github.com/ultralytics/yolov5/issues/5395
@Timeout(30)  # known issue https://github.com/ultralytics/yolov5/issues/5611
def plot_labels(labels, names=(), save_dir=Path(''), img_size=1024):
    rboxes = poly2rbox(labels[:, 1:])
    labels = np.concatenate((labels[:, :1], rboxes[:, :-1]), axis=1) # [cls xyls]

    # plot dataset labels
    LOGGER.info(f"Plotting labels to {save_dir / 'labels_xyls.jpg'}... ")
    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, hboxes(xyls)
    nc = int(c.max() + 1)  # number of classes
    x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'long_edge', 'short_edge'])

    # 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='long_edge', y='short_edge', ax=ax[3], bins=50, pmax=0.9)

    # rectangles
    # labels[:, 1:3] = 0.5 # center
    labels[:, 1:3] = 0.5 * img_size # center
    # labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
    labels[:, 1:] = xywh2xyxy(labels[:, 1:]) 
    # img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
    img = Image.fromarray(np.ones((img_size, img_size, 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_xyls.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})
    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)
    fig, ax = plt.subplots(2, 6, figsize=(18, 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]):
            for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 7, 8]):
                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 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='image.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
        cv2.imwrite(str(increment_path(file).with_suffix('.jpg')), crop)
    return crop