# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
import math
import warnings
from pathlib import Path
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
from PIL import __version__ as pil_version
from ultralytics.utils import LOGGER, TryExcept, plt_settings, threaded
from .checks import check_font, check_version, is_ascii
from .files import increment_path
from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
class Colors:
"""
Ultralytics default color palette https://ultralytics.com/.
This class provides methods to work with the Ultralytics color palette, including converting hex color codes to
RGB values.
Attributes:
palette (list of tuple): List of RGB color values.
n (int): The number of colors in the palette.
pose_palette (np.array): A specific color palette array with dtype np.uint8.
"""
def __init__(self):
"""Initialize colors as 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)
self.pose_palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255],
[153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255],
[255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102],
[51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]],
dtype=np.uint8)
def __call__(self, i, bgr=False):
"""Converts hex color codes to RGB values."""
c = self.palette[int(i) % self.n]
return (c[2], c[1], c[0]) if bgr else c
@staticmethod
def hex2rgb(h):
"""Converts hex color codes to RGB values (i.e. default PIL order)."""
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'
class Annotator:
"""
Ultralytics Annotator for train/val mosaics and JPGs and predictions annotations.
Attributes:
im (Image.Image or numpy array): The image to annotate.
pil (bool): Whether to use PIL or cv2 for drawing annotations.
font (ImageFont.truetype or ImageFont.load_default): Font used for text annotations.
lw (float): Line width for drawing.
skeleton (List[List[int]]): Skeleton structure for keypoints.
limb_color (List[int]): Color palette for limbs.
kpt_color (List[int]): Color palette for keypoints.
"""
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
"""Initialize the Annotator class with image and line width along with color palette for keypoints and limbs."""
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)
try:
font = check_font('Arial.Unicode.ttf' if non_ascii else font)
size = font_size or max(round(sum(self.im.size) / 2 * 0.035), 12)
self.font = ImageFont.truetype(str(font), size)
except Exception:
self.font = ImageFont.load_default()
# Deprecation fix for w, h = getsize(string) -> _, _, w, h = getbox(string)
if check_version(pil_version, '9.2.0'):
self.font.getsize = lambda x: self.font.getbbox(x)[2:4] # text width, height
else: # use cv2
self.im = im
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
# Pose
self.skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8], [7, 9],
[8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
self.limb_color = colors.pose_palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
self.kpt_color = colors.pose_palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
"""Add one xyxy box to image with label."""
if isinstance(box, torch.Tensor):
box = box.tolist()
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, alpha=0.5, retina_masks=False):
"""
Plot masks on image.
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): Image is in cuda, shape: [3, h, w], range: [0, 1]
alpha (float): Mask transparency: 0.0 fully transparent, 1.0 opaque
retina_masks (bool): Whether to use high resolution masks or not. Defaults to False.
"""
if self.pil:
# Convert to numpy first
self.im = np.asarray(self.im).copy()
if len(masks) == 0:
self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
if im_gpu.device != masks.device:
im_gpu = im_gpu.to(masks.device)
colors = torch.tensor(colors, device=masks.device, dtype=torch.float32) / 255.0 # shape(n,3)
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.max(dim=0).values # 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)
im_mask_np = im_mask.byte().cpu().numpy()
self.im[:] = im_mask_np if retina_masks else scale_image(im_mask_np, self.im.shape)
if self.pil:
# Convert im back to PIL and update draw
self.fromarray(self.im)
def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True):
"""
Plot keypoints on the image.
Args:
kpts (tensor): Predicted keypoints with shape [17, 3]. Each keypoint has (x, y, confidence).
shape (tuple): Image shape as a tuple (h, w), where h is the height and w is the width.
radius (int, optional): Radius of the drawn keypoints. Default is 5.
kpt_line (bool, optional): If True, the function will draw lines connecting keypoints
for human pose. Default is True.
Note: `kpt_line=True` currently only supports human pose plotting.
"""
if self.pil:
# Convert to numpy first
self.im = np.asarray(self.im).copy()
nkpt, ndim = kpts.shape
is_pose = nkpt == 17 and ndim == 3
kpt_line &= is_pose # `kpt_line=True` for now only supports human pose plotting
for i, k in enumerate(kpts):
color_k = [int(x) for x in self.kpt_color[i]] if is_pose else colors(i)
x_coord, y_coord = k[0], k[1]
if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
if len(k) == 3:
conf = k[2]
if conf < 0.5:
continue
cv2.circle(self.im, (int(x_coord), int(y_coord)), radius, color_k, -1, lineType=cv2.LINE_AA)
if kpt_line:
ndim = kpts.shape[-1]
for i, sk in enumerate(self.skeleton):
pos1 = (int(kpts[(sk[0] - 1), 0]), int(kpts[(sk[0] - 1), 1]))
pos2 = (int(kpts[(sk[1] - 1), 0]), int(kpts[(sk[1] - 1), 1]))
if ndim == 3:
conf1 = kpts[(sk[0] - 1), 2]
conf2 = kpts[(sk[1] - 1), 2]
if conf1 < 0.5 or conf2 < 0.5:
continue
if pos1[0] % shape[1] == 0 or pos1[1] % shape[0] == 0 or pos1[0] < 0 or pos1[1] < 0:
continue
if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
continue
cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
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', box_style=False):
"""Adds text to an image using PIL or cv2."""
if anchor == 'bottom': # start y from font bottom
w, h = self.font.getsize(text) # text width, height
xy[1] += 1 - h
if self.pil:
if box_style:
w, h = self.font.getsize(text)
self.draw.rectangle((xy[0], xy[1], xy[0] + w + 1, xy[1] + h + 1), fill=txt_color)
# Using `txt_color` for background and draw fg with white color
txt_color = (255, 255, 255)
if '\n' in text:
lines = text.split('\n')
_, h = self.font.getsize(text)
for line in lines:
self.draw.text(xy, line, fill=txt_color, font=self.font)
xy[1] += h
else:
self.draw.text(xy, text, fill=txt_color, font=self.font)
else:
if box_style:
tf = max(self.lw - 1, 1) # font thickness
w, h = cv2.getTextSize(text, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
outside = xy[1] - h >= 3
p2 = xy[0] + w, xy[1] - h - 3 if outside else xy[1] + h + 3
cv2.rectangle(self.im, xy, p2, txt_color, -1, cv2.LINE_AA) # filled
# Using `txt_color` for background and draw fg with white color
txt_color = (255, 255, 255)
tf = max(self.lw - 1, 1) # font thickness
cv2.putText(self.im, text, xy, 0, self.lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
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)
@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
@plt_settings()
def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None):
"""Plot training labels including class histograms and box statistics."""
import pandas as pd
import seaborn as sn
# Filter matplotlib>=3.7.2 warning
warnings.filterwarnings('ignore', category=UserWarning, message='The figure layout has changed to tight')
# Plot dataset labels
LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
nc = int(cls.max() + 1) # number of classes
boxes = boxes[:1000000] # limit to 1M boxes
x = pd.DataFrame(boxes, 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
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
y = ax[0].hist(cls, 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
boxes[:, 0:2] = 0.5 # center
boxes = xywh2xyxy(boxes) * 1000
img = Image.fromarray(np.ones((1000, 1000, 3), dtype=np.uint8) * 255)
for cls, box in zip(cls[:500], boxes[:500]):
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)
fname = save_dir / 'labels.jpg'
plt.savefig(fname, dpi=200)
plt.close()
if on_plot:
on_plot(fname)
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.
This function takes a bounding box and an image, and then saves a cropped portion of the image according
to the bounding box. Optionally, the crop can be squared, and the function allows for gain and padding
adjustments to the bounding box.
Args:
xyxy (torch.Tensor or list): A tensor or list representing the bounding box in xyxy format.
im (numpy.ndarray): The input image.
file (Path, optional): The path where the cropped image will be saved. Defaults to 'im.jpg'.
gain (float, optional): A multiplicative factor to increase the size of the bounding box. Defaults to 1.02.
pad (int, optional): The number of pixels to add to the width and height of the bounding box. Defaults to 10.
square (bool, optional): If True, the bounding box will be transformed into a square. Defaults to False.
BGR (bool, optional): If True, the image will be saved in BGR format, otherwise in RGB. Defaults to False.
save (bool, optional): If True, the cropped image will be saved to disk. Defaults to True.
Returns:
(numpy.ndarray): The cropped image.
Example:
```python
from ultralytics.utils.plotting import save_one_box
xyxy = [50, 50, 150, 150]
im = cv2.imread('image.jpg')
cropped_im = save_one_box(xyxy, im, file='cropped.jpg', square=True)
```
"""
if not isinstance(xyxy, torch.Tensor): # may be list
xyxy = torch.stack(xyxy)
b = xyxy2xywh(xyxy.view(-1, 4)) # 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
@threaded
def plot_images(images,
batch_idx,
cls,
bboxes=np.zeros(0, dtype=np.float32),
masks=np.zeros(0, dtype=np.uint8),
kpts=np.zeros((0, 51), dtype=np.float32),
paths=None,
fname='images.jpg',
names=None,
on_plot=None):
"""Plot image grid with labels."""
if isinstance(images, torch.Tensor):
images = images.cpu().float().numpy()
if isinstance(cls, torch.Tensor):
cls = cls.cpu().numpy()
if isinstance(bboxes, torch.Tensor):
bboxes = bboxes.cpu().numpy()
if isinstance(masks, torch.Tensor):
masks = masks.cpu().numpy().astype(int)
if isinstance(kpts, torch.Tensor):
kpts = kpts.cpu().numpy()
if isinstance(batch_idx, torch.Tensor):
batch_idx = batch_idx.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(cls) > 0:
idx = batch_idx == i
classes = cls[idx].astype('int')
if len(bboxes):
boxes = xywh2xyxy(bboxes[idx, :4]).T
labels = bboxes.shape[1] == 4 # labels if no conf column
conf = None if labels else bboxes[idx, 4] # 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()):
c = classes[j]
color = colors(c)
c = names.get(c, c) if names else c
if labels or conf[j] > 0.25: # 0.25 conf thresh
label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
annotator.box_label(box, label, color=color)
elif len(classes):
for c in classes:
color = colors(c)
c = names.get(c, c) if names else c
annotator.text((x, y), f'{c}', txt_color=color, box_style=True)
# Plot keypoints
if len(kpts):
kpts_ = kpts[idx].copy()
if len(kpts_):
if kpts_[..., 0].max() <= 1.01 or kpts_[..., 1].max() <= 1.01: # if normalized with tolerance .01
kpts_[..., 0] *= w # scale to pixels
kpts_[..., 1] *= h
elif scale < 1: # absolute coords need scale if image scales
kpts_ *= scale
kpts_[..., 0] += x
kpts_[..., 1] += y
for j in range(len(kpts_)):
if labels or conf[j] > 0.25: # 0.25 conf thresh
annotator.kpts(kpts_[j])
# Plot masks
if len(masks):
if idx.shape[0] == masks.shape[0]: # overlap_masks=False
image_masks = masks[idx]
else: # overlap_masks=True
image_masks = masks[[i]] # (1, 640, 640)
nl = idx.sum()
index = np.arange(nl).reshape((nl, 1, 1)) + 1
image_masks = np.repeat(image_masks, nl, axis=0)
image_masks = np.where(image_masks == index, 1.0, 0.0)
im = np.asarray(annotator.im).copy()
for j, box in enumerate(boxes.T.tolist()):
if labels or conf[j] > 0.25: # 0.25 conf thresh
color = colors(classes[j])
mh, mw = image_masks[j].shape
if mh != h or mw != w:
mask = image_masks[j].astype(np.uint8)
mask = cv2.resize(mask, (w, h))
mask = mask.astype(bool)
else:
mask = image_masks[j].astype(bool)
with contextlib.suppress(Exception):
im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
annotator.fromarray(im)
annotator.im.save(fname) # save
if on_plot:
on_plot(fname)
@plt_settings()
def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False, classify=False, on_plot=None):
"""
Plot training results from results CSV file.
Example:
```python
from ultralytics.utils.plotting import plot_results
plot_results('path/to/results.csv')
```
"""
import pandas as pd
from scipy.ndimage import gaussian_filter1d
save_dir = Path(file).parent if file else Path(dir)
if classify:
fig, ax = plt.subplots(2, 2, figsize=(6, 6), tight_layout=True)
index = [1, 4, 2, 3]
elif segment:
fig, ax = plt.subplots(4, 6, figsize=(18, 6), tight_layout=True)
#index is everything from 0 to 21:
index = [0, 1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12, 17, 18, 19, 20, 21]
elif pose:
fig, ax = plt.subplots(2, 9, figsize=(21, 6), tight_layout=True)
index = [1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 15, 16, 17, 18, 8, 9, 12, 13]
else:
fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
index = [1, 2, 3, 4, 5, 8, 9, 10, 6, 7]
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(index):
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) # actual results
ax[i].plot(x, gaussian_filter1d(y, sigma=3), ':', label='smooth', linewidth=2) # smoothing line
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.warning(f'WARNING: Plotting error for {f}: {e}')
ax[1].legend()
fname = save_dir / 'results.png'
fig.savefig(fname, dpi=200)
plt.close()
if on_plot:
on_plot(fname)
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))
targets = torch.cat(targets, 0).numpy()
return targets[:, 0], targets[:, 1], targets[:, 2:]
def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
"""
Visualize feature maps of a given model module during inference.
Args:
x (torch.Tensor): Features to be visualized.
module_type (str): Module type.
stage (int): Module stage within the model.
n (int, optional): Maximum number of feature maps to plot. Defaults to 32.
save_dir (Path, optional): Directory to save results. Defaults to Path('runs/detect/exp').
"""
for m in ['Detect', 'Pose', 'Segment']:
if m in module_type:
return
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