chanelisa/objectdetectionhw

1# Plotting utils2 3import glob4import math5import os6import random7from copy import copy8from pathlib import Path9 10import cv211import matplotlib12import matplotlib.pyplot as plt13import numpy as np14import pandas as pd15import seaborn as sns16import torch17import yaml18from PIL import Image, ImageDraw, ImageFont19from scipy.signal import butter, filtfilt20 21from utils.general import xywh2xyxy, xyxy2xywh22from utils.metrics import fitness23 24# Settings25matplotlib.rc('font', **{'size': 11})26matplotlib.use('Agg')  # for writing to files only27 28 29def color_list():30    # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb31    def hex2rgb(h):32        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))33 34    return [hex2rgb(h) for h in matplotlib.colors.TABLEAU_COLORS.values()]  # or BASE_ (8), CSS4_ (148), XKCD_ (949)35 36 37def hist2d(x, y, n=100):38    # 2d histogram used in labels.png and evolve.png39    xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)40    hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))41    xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)42    yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)43    return np.log(hist[xidx, yidx])44 45 46def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):47    # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy48    def butter_lowpass(cutoff, fs, order):49        nyq = 0.5 * fs50        normal_cutoff = cutoff / nyq51        return butter(order, normal_cutoff, btype='low', analog=False)52 53    b, a = butter_lowpass(cutoff, fs, order=order)54    return filtfilt(b, a, data)  # forward-backward filter55 56 57def plot_one_box(x, img, color=None, label=None, line_thickness=3):58    # Plots one bounding box on image img59    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness60    color = color or [random.randint(0, 255) for _ in range(3)]61    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))62    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)63    if label:64        tf = max(tl - 1, 1)  # font thickness65        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]66        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 367        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled68        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)69 70 71def plot_one_box_PIL(box, img, color=None, label=None, line_thickness=None):72    img = Image.fromarray(img)73    draw = ImageDraw.Draw(img)74    line_thickness = line_thickness or max(int(min(img.size) / 200), 2)75    draw.rectangle(box, width=line_thickness, outline=tuple(color))  # plot76    if label:77        fontsize = max(round(max(img.size) / 40), 12)78        font = ImageFont.truetype("Arial.ttf", fontsize)79        txt_width, txt_height = font.getsize(label)80        draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=tuple(color))81        draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font)82    return np.asarray(img)83 84 85def plot_wh_methods():  # from utils.plots import *; plot_wh_methods()86    # Compares the two methods for width-height anchor multiplication87    # https://github.com/ultralytics/yolov3/issues/16888    x = np.arange(-4.0, 4.0, .1)89    ya = np.exp(x)90    yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 291 92    fig = plt.figure(figsize=(6, 3), tight_layout=True)93    plt.plot(x, ya, '.-', label='YOLOv3')94    plt.plot(x, yb ** 2, '.-', label='YOLOR ^2')95    plt.plot(x, yb ** 1.6, '.-', label='YOLOR ^1.6')96    plt.xlim(left=-4, right=4)97    plt.ylim(bottom=0, top=6)98    plt.xlabel('input')99    plt.ylabel('output')100    plt.grid()101    plt.legend()102    fig.savefig('comparison.png', dpi=200)103 104 105def output_to_target(output):106    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]107    targets = []108    for i, o in enumerate(output):109        for *box, conf, cls in o.cpu().numpy():110            targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])111    return np.array(targets)112 113 114def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):115    # Plot image grid with labels116 117    if isinstance(images, torch.Tensor):118        images = images.cpu().float().numpy()119    if isinstance(targets, torch.Tensor):120        targets = targets.cpu().numpy()121 122    # un-normalise123    if np.max(images[0]) <= 1:124        images *= 255125 126    tl = 3  # line thickness127    tf = max(tl - 1, 1)  # font thickness128    bs, _, h, w = images.shape  # batch size, _, height, width129    bs = min(bs, max_subplots)  # limit plot images130    ns = np.ceil(bs ** 0.5)  # number of subplots (square)131 132    # Check if we should resize133    scale_factor = max_size / max(h, w)134    if scale_factor < 1:135        h = math.ceil(scale_factor * h)136        w = math.ceil(scale_factor * w)137 138    colors = color_list()  # list of colors139    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init140    for i, img in enumerate(images):141        if i == max_subplots:  # if last batch has fewer images than we expect142            break143 144        block_x = int(w * (i // ns))145        block_y = int(h * (i % ns))146 147        img = img.transpose(1, 2, 0)148        if scale_factor < 1:149            img = cv2.resize(img, (w, h))150 151        mosaic[block_y:block_y + h, block_x:block_x + w, :] = img152        if len(targets) > 0:153            image_targets = targets[targets[:, 0] == i]154            boxes = xywh2xyxy(image_targets[:, 2:6]).T155            classes = image_targets[:, 1].astype('int')156            labels = image_targets.shape[1] == 6  # labels if no conf column157            conf = None if labels else image_targets[:, 6]  # check for confidence presence (label vs pred)158 159            if boxes.shape[1]:160                if boxes.max() <= 1.01:  # if normalized with tolerance 0.01161                    boxes[[0, 2]] *= w  # scale to pixels162                    boxes[[1, 3]] *= h163                elif scale_factor < 1:  # absolute coords need scale if image scales164                    boxes *= scale_factor165            boxes[[0, 2]] += block_x166            boxes[[1, 3]] += block_y167            for j, box in enumerate(boxes.T):168                cls = int(classes[j])169                color = colors[cls % len(colors)]170                cls = names[cls] if names else cls171                if labels or conf[j] > 0.25:  # 0.25 conf thresh172                    label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j])173                    plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl)174 175        # Draw image filename labels176        if paths:177            label = Path(paths[i]).name[:40]  # trim to 40 char178            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]179            cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,180                        lineType=cv2.LINE_AA)181 182        # Image border183        cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3)184 185    if fname:186        r = min(1280. / max(h, w) / ns, 1.0)  # ratio to limit image size187        mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA)188        # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))  # cv2 save189        Image.fromarray(mosaic).save(fname)  # PIL save190    return mosaic191 192 193def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):194    # Plot LR simulating training for full epochs195    optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals196    y = []197    for _ in range(epochs):198        scheduler.step()199        y.append(optimizer.param_groups[0]['lr'])200    plt.plot(y, '.-', label='LR')201    plt.xlabel('epoch')202    plt.ylabel('LR')203    plt.grid()204    plt.xlim(0, epochs)205    plt.ylim(0)206    plt.savefig(Path(save_dir) / 'LR.png', dpi=200)207    plt.close()208 209 210def plot_test_txt():  # from utils.plots import *; plot_test()211    # Plot test.txt histograms212    x = np.loadtxt('test.txt', dtype=np.float32)213    box = xyxy2xywh(x[:, :4])214    cx, cy = box[:, 0], box[:, 1]215 216    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)217    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)218    ax.set_aspect('equal')219    plt.savefig('hist2d.png', dpi=300)220 221    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)222    ax[0].hist(cx, bins=600)223    ax[1].hist(cy, bins=600)224    plt.savefig('hist1d.png', dpi=200)225 226 227def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()228    # Plot targets.txt histograms229    x = np.loadtxt('targets.txt', dtype=np.float32).T230    s = ['x targets', 'y targets', 'width targets', 'height targets']231    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)232    ax = ax.ravel()233    for i in range(4):234        ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))235        ax[i].legend()236        ax[i].set_title(s[i])237    plt.savefig('targets.jpg', dpi=200)238 239 240def plot_study_txt(path='', x=None):  # from utils.plots import *; plot_study_txt()241    # Plot study.txt generated by test.py242    fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)243    # ax = ax.ravel()244 245    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)246    # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolor-p6', 'yolor-w6', 'yolor-e6', 'yolor-d6']]:247    for f in sorted(Path(path).glob('study*.txt')):248        y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T249        x = np.arange(y.shape[1]) if x is None else np.array(x)250        s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']251        # for i in range(7):252        #     ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)253        #     ax[i].set_title(s[i])254 255        j = y[3].argmax() + 1256        ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,257                 label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))258 259    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],260             'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')261 262    ax2.grid(alpha=0.2)263    ax2.set_yticks(np.arange(20, 60, 5))264    ax2.set_xlim(0, 57)265    ax2.set_ylim(30, 55)266    ax2.set_xlabel('GPU Speed (ms/img)')267    ax2.set_ylabel('COCO AP val')268    ax2.legend(loc='lower right')269    plt.savefig(str(Path(path).name) + '.png', dpi=300)270 271 272def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):273    # plot dataset labels274    print('Plotting labels... ')275    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes276    nc = int(c.max() + 1)  # number of classes277    colors = color_list()278    x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])279 280    # seaborn correlogram281    sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))282    plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)283    plt.close()284 285    # matplotlib labels286    matplotlib.use('svg')  # faster287    ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()288    ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)289    ax[0].set_ylabel('instances')290    if 0 < len(names) < 30:291        ax[0].set_xticks(range(len(names)))292        ax[0].set_xticklabels(names, rotation=90, fontsize=10)293    else:294        ax[0].set_xlabel('classes')295    sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)296    sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)297 298    # rectangles299    labels[:, 1:3] = 0.5  # center300    labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000301    img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)302    for cls, *box in labels[:1000]:303        ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls) % 10])  # plot304    ax[1].imshow(img)305    ax[1].axis('off')306 307    for a in [0, 1, 2, 3]:308        for s in ['top', 'right', 'left', 'bottom']:309            ax[a].spines[s].set_visible(False)310 311    plt.savefig(save_dir / 'labels.jpg', dpi=200)312    matplotlib.use('Agg')313    plt.close()314 315    # loggers316    for k, v in loggers.items() or {}:317        if k == 'wandb' and v:318            v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False)319 320 321def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.plots import *; plot_evolution()322    # Plot hyperparameter evolution results in evolve.txt323    with open(yaml_file) as f:324        hyp = yaml.load(f, Loader=yaml.SafeLoader)325    x = np.loadtxt('evolve.txt', ndmin=2)326    f = fitness(x)327    # weights = (f - f.min()) ** 2  # for weighted results328    plt.figure(figsize=(10, 12), tight_layout=True)329    matplotlib.rc('font', **{'size': 8})330    for i, (k, v) in enumerate(hyp.items()):331        y = x[:, i + 7]332        # mu = (y * weights).sum() / weights.sum()  # best weighted result333        mu = y[f.argmax()]  # best single result334        plt.subplot(6, 5, i + 1)335        plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none')336        plt.plot(mu, f.max(), 'k+', markersize=15)337        plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters338        if i % 5 != 0:339            plt.yticks([])340        print('%15s: %.3g' % (k, mu))341    plt.savefig('evolve.png', dpi=200)342    print('\nPlot saved as evolve.png')343 344 345def profile_idetection(start=0, stop=0, labels=(), save_dir=''):346    # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()347    ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()348    s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']349    files = list(Path(save_dir).glob('frames*.txt'))350    for fi, f in enumerate(files):351        try:352            results = np.loadtxt(f, ndmin=2).T[:, 90:-30]  # clip first and last rows353            n = results.shape[1]  # number of rows354            x = np.arange(start, min(stop, n) if stop else n)355            results = results[:, x]356            t = (results[0] - results[0].min())  # set t0=0s357            results[0] = x358            for i, a in enumerate(ax):359                if i < len(results):360                    label = labels[fi] if len(labels) else f.stem.replace('frames_', '')361                    a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)362                    a.set_title(s[i])363                    a.set_xlabel('time (s)')364                    # if fi == len(files) - 1:365                    #     a.set_ylim(bottom=0)366                    for side in ['top', 'right']:367                        a.spines[side].set_visible(False)368                else:369                    a.remove()370        except Exception as e:371            print('Warning: Plotting error for %s; %s' % (f, e))372 373    ax[1].legend()374    plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)375 376 377def plot_results_overlay(start=0, stop=0):  # from utils.plots import *; plot_results_overlay()378    # Plot training 'results*.txt', overlaying train and val losses379    s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends380    t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles381    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):382        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T383        n = results.shape[1]  # number of rows384        x = range(start, min(stop, n) if stop else n)385        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)386        ax = ax.ravel()387        for i in range(5):388            for j in [i, i + 5]:389                y = results[j, x]390                ax[i].plot(x, y, marker='.', label=s[j])391                # y_smooth = butter_lowpass_filtfilt(y)392                # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])393 394            ax[i].set_title(t[i])395            ax[i].legend()396            ax[i].set_ylabel(f) if i == 0 else None  # add filename397        fig.savefig(f.replace('.txt', '.png'), dpi=200)398 399 400def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):401    # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp')402    fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)403    ax = ax.ravel()404    s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',405         'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']406    if bucket:407        # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]408        files = ['results%g.txt' % x for x in id]409        c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id)410        os.system(c)411    else:412        files = list(Path(save_dir).glob('results*.txt'))413    assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)414    for fi, f in enumerate(files):415        try:416            results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T417            n = results.shape[1]  # number of rows418            x = range(start, min(stop, n) if stop else n)419            for i in range(10):420                y = results[i, x]421                if i in [0, 1, 2, 5, 6, 7]:422                    y[y == 0] = np.nan  # don't show zero loss values423                    # y /= y[0]  # normalize424                label = labels[fi] if len(labels) else f.stem425                ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8)426                ax[i].set_title(s[i])427                # if i in [5, 6, 7]:  # share train and val loss y axes428                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])429        except Exception as e:430            print('Warning: Plotting error for %s; %s' % (f, e))431 432    ax[1].legend()433    fig.savefig(Path(save_dir) / 'results.png', dpi=200)434    435    436def output_to_keypoint(output):437    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]438    targets = []439    for i, o in enumerate(output):440        kpts = o[:,6:]441        o = o[:,:6]442        for index, (*box, conf, cls) in enumerate(o.detach().cpu().numpy()):443            targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf, *list(kpts.detach().cpu().numpy()[index])])444    return np.array(targets)445 446 447def plot_skeleton_kpts(im, kpts, steps, orig_shape=None):448    #Plot the skeleton and keypointsfor coco datatset449    palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102],450                        [230, 230, 0], [255, 153, 255], [153, 204, 255],451                        [255, 102, 255], [255, 51, 255], [102, 178, 255],452                        [51, 153, 255], [255, 153, 153], [255, 102, 102],453                        [255, 51, 51], [153, 255, 153], [102, 255, 102],454                        [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0],455                        [255, 255, 255]])456 457    skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12],458                [7, 13], [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3],459                [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]460 461    pose_limb_color = palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]462    pose_kpt_color = palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]463    radius = 5464    num_kpts = len(kpts) // steps465 466    for kid in range(num_kpts):467        r, g, b = pose_kpt_color[kid]468        x_coord, y_coord = kpts[steps * kid], kpts[steps * kid + 1]469        if not (x_coord % 640 == 0 or y_coord % 640 == 0):470            if steps == 3:471                conf = kpts[steps * kid + 2]472                if conf < 0.5:473                    continue474            cv2.circle(im, (int(x_coord), int(y_coord)), radius, (int(r), int(g), int(b)), -1)475 476    for sk_id, sk in enumerate(skeleton):477        r, g, b = pose_limb_color[sk_id]478        pos1 = (int(kpts[(sk[0]-1)*steps]), int(kpts[(sk[0]-1)*steps+1]))479        pos2 = (int(kpts[(sk[1]-1)*steps]), int(kpts[(sk[1]-1)*steps+1]))480        if steps == 3:481            conf1 = kpts[(sk[0]-1)*steps+2]482            conf2 = kpts[(sk[1]-1)*steps+2]483            if conf1<0.5 or conf2<0.5:484                continue485        if pos1[0]%640 == 0 or pos1[1]%640==0 or pos1[0]<0 or pos1[1]<0:486            continue487        if pos2[0] % 640 == 0 or pos2[1] % 640 == 0 or pos2[0]<0 or pos2[1]<0:488            continue489        cv2.line(im, pos1, pos2, (int(r), int(g), int(b)), thickness=2)490