import contextlib 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 import TryExcept, threaded from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path, is_ascii, xywh2xyxy, xyxy2xywh) from utils.metrics import fitness from utils.segment.general import scale_image # 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() hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') self.palette = [self.hex2rgb(f'#{c}') for c in hexs] 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_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: # 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.' non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic self.pil = pil or non_ascii 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 non_ascii 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 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 masks(self, masks, colors, im_gpu=None, alpha=0.5): """Plot masks at once. Args: masks (tensor): predicted masks on cuda, shape: [n, h, w] colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n] im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1] alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque """ if self.pil: # convert to numpy first self.im = np.asarray(self.im).copy() if im_gpu is None: # Add multiple masks of shape(h,w,n) with colors list([r,g,b], [r,g,b], ...) if len(masks) == 0: return if isinstance(masks, torch.Tensor): masks = torch.as_tensor(masks, dtype=torch.uint8) masks = masks.permute(1, 2, 0).contiguous() masks = masks.cpu().numpy() # masks = np.ascontiguousarray(masks.transpose(1, 2, 0)) masks = scale_image(masks.shape[:2], masks, self.im.shape) masks = np.asarray(masks, dtype=np.float32) colors = np.asarray(colors, dtype=np.float32) # shape(n,3) s = masks.sum(2, keepdims=True).clip(0, 1) # add all masks together masks = (masks @ colors).clip(0, 255) # (h,w,n) @ (n,3) = (h,w,3) self.im[:] = masks * alpha + self.im * (1 - s * alpha) else: if len(masks) == 0: self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255 colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0 colors = colors[:, None, None] # shape(n,1,1,3) masks = masks.unsqueeze(3) # shape(n,h,w,1) masks_color = masks * (colors * alpha) # shape(n,h,w,3) inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1) mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3) im_gpu = im_gpu.flip(dims=[0]) # flip channel im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3) im_gpu = im_gpu * inv_alph_masks[-1] + mcs im_mask = (im_gpu * 255).byte().cpu().numpy() self.im[:] = scale_image(im_gpu.shape, im_mask, self.im.shape) if self.pil: # convert im back to PIL and update draw self.fromarray(self.im) 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), anchor='top'): # Add text to image (PIL-only) if anchor == 'bottom': # start y from font bottom w, h = self.font.getsize(text) # text width, height xy[1] += 1 - h self.draw.text(xy, text, fill=txt_color, font=self.font) def fromarray(self, im): # Update self.im from a numpy array self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) self.draw = ImageDraw.Draw(self.im) 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, max_det=300): # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting targets = [] for i, o in enumerate(output): box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1) j = torch.full((conf.shape[0], 1), i) targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1)) return torch.cat(targets, 0).numpy() @threaded def plot_images(images, targets, paths=None, fname='images.jpg', names=None): # Plot image grid with labels if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() if isinstance(targets, torch.Tensor): targets = targets.cpu().numpy() max_size = 1920 # max image size max_subplots = 16 # max image subplots, i.e. 4x4 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) if np.max(images[0]) <= 1: images *= 255 # de-normalise (optional) # 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), 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) @TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395 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) with contextlib.suppress(Exception): # 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 ax[0].set_ylabel('instances') if 0 < len(names) < 30: ax[0].set_xticks(range(len(names))) ax[0].set_xticklabels(list(names.values()), 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 imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')): # Show classification image grid with labels (optional) and predictions (optional) from utils.augmentations import denormalize names = names or [f'class{i}' for i in range(1000)] blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im), dim=0) # select batch index 0, block by channels n = min(len(blocks), nmax) # number of plots m = min(8, round(n ** 0.5)) # 8 x 8 default fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols ax = ax.ravel() if m > 1 else [ax] # plt.subplots_adjust(wspace=0.05, hspace=0.05) for i in range(n): ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0)) ax[i].axis('off') if labels is not None: s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '') ax[i].set_title(s, fontsize=8, verticalalignment='top') plt.savefig(f, dpi=300, bbox_inches='tight') plt.close() if verbose: LOGGER.info(f"Saving {f}") if labels is not None: LOGGER.info('True: ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax])) if pred is not None: LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax])) return f 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 f in 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].astype('float') # 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_boxes(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) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB return crop