# 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, ops, plt_settings, threaded
from .checks import check_font, check_version, is_ascii
from .files import increment_path
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
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
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.tf = max(self.lw - 1, 1) # font thickness
self.sf = self.lw / 3 # font scale
# 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:
w, h = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.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.sf,
txt_color,
thickness=self.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_alpha_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_alpha_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 ops.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:
w, h = cv2.getTextSize(text, 0, fontScale=self.sf, thickness=self.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)
cv2.putText(self.im, text, xy, 0, self.sf, txt_color, thickness=self.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)
# Object Counting Annotator
def draw_region(self, reg_pts=None, color=(0, 255, 0)):
# Draw region line
cv2.polylines(self.im, [np.array(reg_pts, dtype=np.int32)], isClosed=True, color=color, thickness=self.tf + 2)
def draw_centroid_and_tracks(self, track, color=(255, 0, 255), track_thickness=2):
# Draw region line
points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(self.im, [points], isClosed=False, color=color, thickness=track_thickness)
cv2.circle(self.im, (int(track[-1][0]), int(track[-1][1])), track_thickness * 2, color, -1)
def count_labels(self, in_count=0, out_count=0, color=(255, 255, 255), txt_color=(0, 0, 0)):
tl = self.tf or round(0.002 * (self.im.shape[0] + self.im.shape[1]) / 2) + 1
tf = max(tl - 1, 1)
gap = int(24 * tl) # Calculate the gap between in_count and out_count based on line_thickness
# Get text size for in_count and out_count
t_size_in = cv2.getTextSize(str(in_count), 0, fontScale=tl / 2, thickness=tf)[0]
t_size_out = cv2.getTextSize(str(out_count), 0, fontScale=tl / 2, thickness=tf)[0]
# Calculate positions for in_count and out_count labels
text_width = max(t_size_in[0], t_size_out[0])
text_x1 = (self.im.shape[1] - text_width - 120 * self.tf) // 2 - gap
text_x2 = (self.im.shape[1] - text_width + 120 * self.tf) // 2 + gap
text_y = max(t_size_in[1], t_size_out[1])
# Create a rounded rectangle for in_count
cv2.rectangle(self.im, (text_x1 - 5, text_y - 5), (text_x1 + text_width + 7, text_y + t_size_in[1] + 7), color,
-1)
cv2.putText(self.im,
str(in_count), (text_x1, text_y + t_size_in[1]),
0,
tl / 2,
txt_color,
self.tf,
lineType=cv2.LINE_AA)
# Create a rounded rectangle for out_count
cv2.rectangle(self.im, (text_x2 - 5, text_y - 5), (text_x2 + text_width + 7, text_y + t_size_out[1] + 7), color,
-1)
cv2.putText(self.im,
str(out_count), (text_x2, text_y + t_size_out[1]),
0,
tl / 2,
txt_color,
thickness=self.tf,
lineType=cv2.LINE_AA)
# AI GYM Annotator
def estimate_pose_angle(self, a, b, c):
"""Calculate the pose angle for object
Args:
a (float) : The value of pose point a
b (float): The value of pose point b
c (float): The value o pose point c
Returns:
angle (degree): Degree value of angle between three points
"""
a, b, c = np.array(a), np.array(b), np.array(c)
radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0])
angle = np.abs(radians * 180.0 / np.pi)
if angle > 180.0:
angle = 360 - angle
return angle
def draw_specific_points(self, keypoints, indices=[2, 5, 7], shape=(640, 640), radius=2):
"""Draw specific keypoints for gym steps counting."""
nkpts, ndim = keypoints.shape
nkpts == 17 and ndim == 3
for i, k in enumerate(keypoints):
if i in indices:
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, (0, 255, 0), -1, lineType=cv2.LINE_AA)
return self.im
def plot_angle_and_count_and_stage(self, angle_text, count_text, stage_text, center_kpt, line_thickness=2):
"""Plot the pose angle, count value and step stage."""
angle_text, count_text, stage_text = f' {angle_text:.2f}', 'Steps : ' + f'{count_text}', f' {stage_text}'
font_scale = 0.6 + (line_thickness / 10.0)
# Draw angle
(angle_text_width, angle_text_height), _ = cv2.getTextSize(angle_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale,
line_thickness)
angle_text_position = (int(center_kpt[0]), int(center_kpt[1]))
angle_background_position = (angle_text_position[0], angle_text_position[1] - angle_text_height - 5)
angle_background_size = (angle_text_width + 2 * 5, angle_text_height + 2 * 5 + (line_thickness * 2))
cv2.rectangle(self.im, angle_background_position, (angle_background_position[0] + angle_background_size[0],
angle_background_position[1] + angle_background_size[1]),
(255, 255, 255), -1)
cv2.putText(self.im, angle_text, angle_text_position, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 0),
line_thickness)
# Draw Counts
(count_text_width, count_text_height), _ = cv2.getTextSize(count_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale,
line_thickness)
count_text_position = (angle_text_position[0], angle_text_position[1] + angle_text_height + 20)
count_background_position = (angle_background_position[0],
angle_background_position[1] + angle_background_size[1] + 5)
count_background_size = (count_text_width + 10, count_text_height + 10 + (line_thickness * 2))
cv2.rectangle(self.im, count_background_position, (count_background_position[0] + count_background_size[0],
count_background_position[1] + count_background_size[1]),
(255, 255, 255), -1)
cv2.putText(self.im, count_text, count_text_position, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 0),
line_thickness)
# Draw Stage
(stage_text_width, stage_text_height), _ = cv2.getTextSize(stage_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale,
line_thickness)
stage_text_position = (int(center_kpt[0]), int(center_kpt[1]) + angle_text_height + count_text_height + 40)
stage_background_position = (stage_text_position[0], stage_text_position[1] - stage_text_height - 5)
stage_background_size = (stage_text_width + 10, stage_text_height + 10)
cv2.rectangle(self.im, stage_background_position, (stage_background_position[0] + stage_background_size[0],
stage_background_position[1] + stage_background_size[1]),
(255, 255, 255), -1)
cv2.putText(self.im, stage_text, stage_text_position, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 0),
line_thickness)
@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 and Seaborn use_inf and is_categorical FutureWarnings
warnings.filterwarnings('ignore', category=UserWarning, message='The figure layout has changed to tight')
warnings.filterwarnings('ignore', category=FutureWarning)
# 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)
for i in range(nc):
y[2].patches[i].set_color([x / 255 for x in colors(i)])
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 = ops.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 = ops.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 = ops.xywh2xyxy(b).long()
xyxy = ops.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 = ops.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 a results CSV file. The function supports various types of data including segmentation,
pose estimation, and classification. Plots are saved as 'results.png' in the directory where the CSV is located.
Args:
file (str, optional): Path to the CSV file containing the training results. Defaults to 'path/to/results.csv'.
dir (str, optional): Directory where the CSV file is located if 'file' is not provided. Defaults to ''.
segment (bool, optional): Flag to indicate if the data is for segmentation. Defaults to False.
pose (bool, optional): Flag to indicate if the data is for pose estimation. Defaults to False.
classify (bool, optional): Flag to indicate if the data is for classification. Defaults to False.
on_plot (callable, optional): Callback function to be executed after plotting. Takes filename as an argument.
Defaults to None.
Example:
```python
from ultralytics.utils.plotting import plot_results
plot_results('path/to/results.csv', segment=True)
```
"""
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(2, 8, figsize=(18, 6), tight_layout=True)
index = [1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]
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 plt_color_scatter(v, f, bins=20, cmap='viridis', alpha=0.8, edgecolors='none'):
"""
Plots a scatter plot with points colored based on a 2D histogram.
Args:
v (array-like): Values for the x-axis.
f (array-like): Values for the y-axis.
bins (int, optional): Number of bins for the histogram. Defaults to 20.
cmap (str, optional): Colormap for the scatter plot. Defaults to 'viridis'.
alpha (float, optional): Alpha for the scatter plot. Defaults to 0.8.
edgecolors (str, optional): Edge colors for the scatter plot. Defaults to 'none'.
Examples:
>>> v = np.random.rand(100)
>>> f = np.random.rand(100)
>>> plt_color_scatter(v, f)
"""
# Calculate 2D histogram and corresponding colors
hist, xedges, yedges = np.histogram2d(v, f, bins=bins)
colors = [
hist[min(np.digitize(v[i], xedges, right=True) - 1, hist.shape[0] - 1),
min(np.digitize(f[i], yedges, right=True) - 1, hist.shape[1] - 1)] for i in range(len(v))]
# Scatter plot
plt.scatter(v, f, c=colors, cmap=cmap, alpha=alpha, edgecolors=edgecolors)
def plot_tune_results(csv_file='tune_results.csv'):
"""
Plot the evolution results stored in an 'tune_results.csv' file. The function generates a scatter plot for each key
in the CSV, color-coded based on fitness scores. The best-performing configurations are highlighted on the plots.
Args:
csv_file (str, optional): Path to the CSV file containing the tuning results. Defaults to 'tune_results.csv'.
Examples:
>>> plot_tune_results('path/to/tune_results.csv')
"""
import pandas as pd
from scipy.ndimage import gaussian_filter1d
# Scatter plots for each hyperparameter
csv_file = Path(csv_file)
data = pd.read_csv(csv_file)
num_metrics_columns = 1
keys = [x.strip() for x in data.columns][num_metrics_columns:]
x = data.values
fitness = x[:, 0] # fitness
j = np.argmax(fitness) # max fitness index
n = math.ceil(len(keys) ** 0.5) # columns and rows in plot
plt.figure(figsize=(10, 10), tight_layout=True)
for i, k in enumerate(keys):
v = x[:, i + num_metrics_columns]
mu = v[j] # best single result
plt.subplot(n, n, i + 1)
plt_color_scatter(v, fitness, cmap='viridis', alpha=.8, edgecolors='none')
plt.plot(mu, fitness.max(), 'k+', markersize=15)
plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters
plt.tick_params(axis='both', labelsize=8) # Set axis label size to 8
if i % n != 0:
plt.yticks([])
file = csv_file.with_name('tune_scatter_plots.png') # filename
plt.savefig(file, dpi=200)
plt.close()
LOGGER.info(f'Saved {file}')
# Fitness vs iteration
x = range(1, len(fitness) + 1)
plt.figure(figsize=(10, 6), tight_layout=True)
plt.plot(x, fitness, marker='o', linestyle='none', label='fitness')
plt.plot(x, gaussian_filter1d(fitness, sigma=3), ':', label='smoothed', linewidth=2) # smoothing line
plt.title('Fitness vs Iteration')
plt.xlabel('Iteration')
plt.ylabel('Fitness')
plt.grid(True)
plt.legend()
file = csv_file.with_name('tune_fitness.png') # filename
plt.savefig(file, dpi=200)
plt.close()
LOGGER.info(f'Saved {file}')
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, ops.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