结果：（前提是对应类别的yolov8模型已经训练好）
具体实现：
在ultralytics\utils\plotting.py里面
Ctrl+F搜索box_label
再次照片的最后一行输入：
# 左上角
cv2.putText(self.im, f"({p1[0]}, {p1[1]})", (p1[0], p1[1]+20), 0, self.lw / 3, (0, 0, 225), thickness=2, lineType=cv2.LINE_AA)
# 右上角
cv2.putText(self.im, f"({p2[0]}, {p1[1]})", (p2[0], p1[1]+20), 0, self.lw / 3, (0, 0, 225), thickness=2, lineType=cv2.LINE_AA)
# 左下角
cv2.putText(self.im, f"({p1[0]}, {p2[1]})", (p1[0]-20, p2[1]+20), 0, self.lw / 3, (0, 0, 225), thickness=2, lineType=cv2.LINE_AA)
# 右下角
cv2.putText(self.im, f"({p2[0]}, {p2[1]})", (p2[0]-20, p2[1]+20), 0, self.lw / 3, (0, 0, 225), thickness=2, lineType=cv2.LINE_AA)复制到相应位置测试文件及命令即可达到想要的效果
plotting.py完整代码如下：
# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
import math
import warnings
from pathlib import Path
from typing import Callable, Dict, List, Optional, Union
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 ultralytics.utils.checks import check_font, check_version, is_ascii
from ultralytics.utils.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.ndarray): A specific color palette array with dtype np.uint8.
"""
def __init__(self):
"""Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
hexs = (
"042AFF",
"0BDBEB",
"F3F3F3",
"00DFB7",
"111F68",
"FF6FDD",
"FF444F",
"CCED00",
"00F344",
"BD00FF",
"00B4FF",
"DD00BA",
"00FFFF",
"26C000",
"01FFB3",
"7D24FF",
"7B0068",
"FF1B6C",
"FC6D2F",
"A2FF0B",
)
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."""
non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
input_is_pil = isinstance(im, Image.Image)
self.pil = pil or non_ascii or input_is_pil
self.lw = line_width or max(round(sum(im.size if input_is_pil else im.shape) / 2 * 0.003), 2)
if self.pil: # use PIL
self.im = im if input_is_pil 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
assert im.data.contiguous, "Image not contiguous. Apply np.ascontiguousarray(im) to Annotator input images."
self.im = im if im.flags.writeable else im.copy()
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]]
self.dark_colors = {
(235, 219, 11),
(243, 243, 243),
(183, 223, 0),
(221, 111, 255),
(0, 237, 204),
(68, 243, 0),
(255, 255, 0),
(179, 255, 1),
(11, 255, 162),
}
self.light_colors = {
(255, 42, 4),
(79, 68, 255),
(255, 0, 189),
(255, 180, 0),
(186, 0, 221),
(0, 192, 38),
(255, 36, 125),
(104, 0, 123),
(108, 27, 255),
(47, 109, 252),
(104, 31, 17),
}
def get_txt_color(self, color=(128, 128, 128), txt_color=(255, 255, 255)):
"""Assign text color based on background color."""
if color in self.dark_colors:
return 104, 31, 17
elif color in self.light_colors:
return 255, 255, 255
else:
return txt_color
def circle_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), margin=2):
"""
Draws a label with a background circle centered within a given bounding box.
Args:
box (tuple): The bounding box coordinates (x1, y1, x2, y2).
label (str): The text label to be displayed.
color (tuple, optional): The background color of the rectangle (B, G, R).
txt_color (tuple, optional): The color of the text (R, G, B).
margin (int, optional): The margin between the text and the rectangle border.
"""
# If label have more than 3 characters, skip other characters, due to circle size
if len(label) > 3:
print(
f"Length of label is {len(label)}, initial 3 label characters will be considered for circle annotation!"
)
label = label[:3]
# Calculate the center of the box
x_center, y_center = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
# Get the text size
text_size = cv2.getTextSize(str(label), cv2.FONT_HERSHEY_SIMPLEX, self.sf - 0.15, self.tf)[0]
# Calculate the required radius to fit the text with the margin
required_radius = int(((text_size[0] ** 2 + text_size[1] ** 2) ** 0.5) / 2) + margin
# Draw the circle with the required radius
cv2.circle(self.im, (x_center, y_center), required_radius, color, -1)
# Calculate the position for the text
text_x = x_center - text_size[0] // 2
text_y = y_center + text_size[1] // 2
# Draw the text
cv2.putText(
self.im,
str(label),
(text_x, text_y),
cv2.FONT_HERSHEY_SIMPLEX,
self.sf - 0.15,
self.get_txt_color(color, txt_color),
self.tf,
lineType=cv2.LINE_AA,
)
def text_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), margin=5):
"""
Draws a label with a background rectangle centered within a given bounding box.
Args:
box (tuple): The bounding box coordinates (x1, y1, x2, y2).
label (str): The text label to be displayed.
color (tuple, optional): The background color of the rectangle (B, G, R).
txt_color (tuple, optional): The color of the text (R, G, B).
margin (int, optional): The margin between the text and the rectangle border.
"""
# Calculate the center of the bounding box
x_center, y_center = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
# Get the size of the text
text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, self.sf - 0.1, self.tf)[0]
# Calculate the top-left corner of the text (to center it)
text_x = x_center - text_size[0] // 2
text_y = y_center + text_size[1] // 2
# Calculate the coordinates of the background rectangle
rect_x1 = text_x - margin
rect_y1 = text_y - text_size[1] - margin
rect_x2 = text_x + text_size[0] + margin
rect_y2 = text_y + margin
# Draw the background rectangle
cv2.rectangle(self.im, (rect_x1, rect_y1), (rect_x2, rect_y2), color, -1)
# Draw the text on top of the rectangle
cv2.putText(
self.im,
label,
(text_x, text_y),
cv2.FONT_HERSHEY_SIMPLEX,
self.sf - 0.1,
self.get_txt_color(color, txt_color),
self.tf,
lineType=cv2.LINE_AA,
)
def box_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), rotated=False):
"""
Draws a bounding box to image with label.
Args:
box (tuple): The bounding box coordinates (x1, y1, x2, y2).
label (str): The text label to be displayed.
color (tuple, optional): The background color of the rectangle (B, G, R).
txt_color (tuple, optional): The color of the text (R, G, B).
rotated (bool, optional): Variable used to check if task is OBB
"""
txt_color = self.get_txt_color(color, txt_color)
if isinstance(box, torch.Tensor):
box = box.tolist()
if self.pil or not is_ascii(label):
if rotated:
p1 = box[0]
self.draw.polygon([tuple(b) for b in box], width=self.lw, outline=color) # PIL requires tuple box
else:
p1 = (box[0], box[1])
self.draw.rectangle(box, width=self.lw, outline=color) # box
if label:
w, h = self.font.getsize(label) # text width, height
outside = p1[1] >= h # label fits outside box
if p1[0] > self.im.size[0] - w: # size is (w, h), check if label extend beyond right side of image
p1 = self.im.size[0] - w, p1[1]
self.draw.rectangle(
(p1[0], p1[1] - h if outside else p1[1], p1[0] + w + 1, p1[1] + 1 if outside else p1[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((p1[0], p1[1] - h if outside else p1[1]), label, fill=txt_color, font=self.font)
else: # cv2
if rotated:
p1 = [int(b) for b in box[0]]
cv2.polylines(self.im, [np.asarray(box, dtype=int)], True, color, self.lw) # cv2 requires nparray box
else:
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)
# 在预测图中绘制一个中心坐标红点
# center_x = (p1[0] + p2[0]) // 2
# center_y = (p1[1] + p2[1]) // 2
# cv2.circle(self.im, (center_x, center_y), self.lw, (0, 0, 225), self.lw)
# 创建中心点坐标变量
# Center = (center_x, center_y)
# 用于在图像上添加文本
# cv2.putText(self.im, str(Center), (center_x, center_y), 0, self.lw / 3, (0, 0, 225), thickness=4, lineType=cv2.LINE_AA)
# 添加左上角、右上角、左下角和右下角的坐标
# 左上角
cv2.putText(self.im, f"({p1[0]}, {p1[1]})", (p1[0], p1[1]+20), 0, self.lw / 3, (0, 0, 225), thickness=2, lineType=cv2.LINE_AA)
# 右上角
cv2.putText(self.im, f"({p2[0]}, {p1[1]})", (p2[0], p1[1]+20), 0, self.lw / 3, (0, 0, 225), thickness=2, lineType=cv2.LINE_AA)
# 左下角
cv2.putText(self.im, f"({p1[0]}, {p2[1]})", (p1[0]-20, p2[1]+20), 0, self.lw / 3, (0, 0, 225), thickness=2, lineType=cv2.LINE_AA)
# 右下角
cv2.putText(self.im, f"({p2[0]}, {p2[1]})", (p2[0]-20, p2[1]+20), 0, self.lw / 3, (0, 0, 225), thickness=2, lineType=cv2.LINE_AA)
if label:
w, h = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0] # text width, height
h += 3 # add pixels to pad text
outside = p1[1] >= h # label fits outside box
if p1[0] > self.im.shape[1] - w: # shape is (h, w), check if label extend beyond right side of image
p1 = self.im.shape[1] - w, p1[1]
p2 = p1[0] + w, p1[1] - h if outside else p1[1] + h
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 - 1),
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, conf_thres=0.25, kpt_color=None):
"""
Plot keypoints on the image.
Args:
kpts (torch.Tensor): Keypoints, shape [17, 3] (x, y, confidence).
shape (tuple, optional): Image shape (h, w). Defaults to (640, 640).
radius (int, optional): Keypoint radius. Defaults to 5.
kpt_line (bool, optional): Draw lines between keypoints. Defaults to True.
conf_thres (float, optional): Confidence threshold. Defaults to 0.25.
kpt_color (tuple, optional): Keypoint color (B, G, R). Defaults to None.
Note:
- `kpt_line=True` currently only supports human pose plotting.
- Modifies self.im in-place.
- If self.pil is True, converts image to numpy array and back to PIL.
"""
if self.pil:
# Convert to numpy first
self.im = np.asarray(self.im).copy()
nkpt, ndim = kpts.shape
is_pose = nkpt == 17 and ndim in {2, 3}
kpt_line &= is_pose # `kpt_line=True` for now only supports human pose plotting
for i, k in enumerate(kpts):
color_k = kpt_color or (self.kpt_color[i].tolist() 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 < conf_thres:
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 < conf_thres or conf2 < conf_thres:
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,
kpt_color or self.limb_color[i].tolist(),
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
h += 3 # add pixels to pad text
outside = xy[1] >= h # label fits outside box
p2 = xy[0] + w, xy[1] - h if outside else xy[1] + h
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)
def show(self, title=None):
"""Show the annotated image."""
Image.fromarray(np.asarray(self.im)[..., ::-1]).show(title)
def save(self, filename="image.jpg"):
"""Save the annotated image to 'filename'."""
cv2.imwrite(filename, np.asarray(self.im))
def get_bbox_dimension(self, bbox=None):
"""
Calculate the area of a bounding box.
Args:
bbox (tuple): Bounding box coordinates in the format (x_min, y_min, x_max, y_max).
Returns:
angle (degree): Degree value of angle between three points
"""
x_min, y_min, x_max, y_max = bbox
width = x_max - x_min
height = y_max - y_min
return width, height, width * height
def draw_region(self, reg_pts=None, color=(0, 255, 0), thickness=5):
"""
Draw region line.
Args:
reg_pts (list): Region Points (for line 2 points, for region 4 points)
color (tuple): Region Color value
thickness (int): Region area thickness value
"""
cv2.polylines(self.im, [np.array(reg_pts, dtype=np.int32)], isClosed=True, color=color, thickness=thickness)
def draw_centroid_and_tracks(self, track, color=(255, 0, 255), track_thickness=2):
"""
Draw centroid point and track trails.
Args:
track (list): object tracking points for trails display
color (tuple): tracks line color
track_thickness (int): track line thickness value
"""
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 queue_counts_display(self, label, points=None, region_color=(255, 255, 255), txt_color=(0, 0, 0)):
"""
Displays queue counts on an image centered at the points with customizable font size and colors.
Args:
label (str): queue counts label
points (tuple): region points for center point calculation to display text
region_color (RGB): queue region color
txt_color (RGB): text display color
"""
x_values = [point[0] for point in points]
y_values = [point[1] for point in points]
center_x = sum(x_values) // len(points)
center_y = sum(y_values) // len(points)
text_size = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0]
text_width = text_size[0]
text_height = text_size[1]
rect_width = text_width + 20
rect_height = text_height + 20
rect_top_left = (center_x - rect_width // 2, center_y - rect_height // 2)
rect_bottom_right = (center_x + rect_width // 2, center_y + rect_height // 2)
cv2.rectangle(self.im, rect_top_left, rect_bottom_right, region_color, -1)
text_x = center_x - text_width // 2
text_y = center_y + text_height // 2
# Draw text
cv2.putText(
self.im,
label,
(text_x, text_y),
0,
fontScale=self.sf,
color=txt_color,
thickness=self.tf,
lineType=cv2.LINE_AA,
)
def display_objects_labels(self, im0, text, txt_color, bg_color, x_center, y_center, margin):
"""
Display the bounding boxes labels in parking management app.
Args:
im0 (ndarray): inference image
text (str): object/class name
txt_color (bgr color): display color for text foreground
bg_color (bgr color): display color for text background
x_center (float): x position center point for bounding box
y_center (float): y position center point for bounding box
margin (int): gap between text and rectangle for better display
"""
text_size = cv2.getTextSize(text, 0, fontScale=self.sf, thickness=self.tf)[0]
text_x = x_center - text_size[0] // 2
text_y = y_center + text_size[1] // 2
rect_x1 = text_x - margin
rect_y1 = text_y - text_size[1] - margin
rect_x2 = text_x + text_size[0] + margin
rect_y2 = text_y + margin
cv2.rectangle(im0, (rect_x1, rect_y1), (rect_x2, rect_y2), bg_color, -1)
cv2.putText(im0, text, (text_x, text_y), 0, self.sf, txt_color, self.tf, lineType=cv2.LINE_AA)
def display_analytics(self, im0, text, txt_color, bg_color, margin):
"""
Display the overall statistics for parking lots.
Args:
im0 (ndarray): inference image
text (dict): labels dictionary
txt_color (bgr color): display color for text foreground
bg_color (bgr color): display color for text background
margin (int): gap between text and rectangle for better display
"""
horizontal_gap = int(im0.shape[1] * 0.02)
vertical_gap = int(im0.shape[0] * 0.01)
text_y_offset = 0
for label, value in text.items():
txt = f"{label}: {value}"
text_size = cv2.getTextSize(txt, 0, self.sf, self.tf)[0]
if text_size[0] < 5 or text_size[1] < 5:
text_size = (5, 5)
text_x = im0.shape[1] - text_size[0] - margin * 2 - horizontal_gap
text_y = text_y_offset + text_size[1] + margin * 2 + vertical_gap
rect_x1 = text_x - margin * 2
rect_y1 = text_y - text_size[1] - margin * 2
rect_x2 = text_x + text_size[0] + margin * 2
rect_y2 = text_y + margin * 2
cv2.rectangle(im0, (rect_x1, rect_y1), (rect_x2, rect_y2), bg_color, -1)
cv2.putText(im0, txt, (text_x, text_y), 0, self.sf, txt_color, self.tf, lineType=cv2.LINE_AA)
text_y_offset = rect_y2
@staticmethod
def estimate_pose_angle(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=None, shape=(640, 640), radius=2, conf_thres=0.25):
"""
Draw specific keypoints for gym steps counting.
Args:
keypoints (list): Keypoints data to be plotted.
indices (list, optional): Keypoint indices to be plotted. Defaults to [2, 5, 7].
shape (tuple, optional): Image size for model inference. Defaults to (640, 640).
radius (int, optional): Keypoint radius. Defaults to 2.
conf_thres (float, optional): Confidence threshold for keypoints. Defaults to 0.25.
Returns:
(numpy.ndarray): Image with drawn keypoints.
Note:
Keypoint format: [x, y] or [x, y, confidence].
Modifies self.im in-place.
"""
if indices is None:
indices = [2, 5, 7]
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 < conf_thres:
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, color=(104, 31, 17), txt_color=(255, 255, 255)
):
"""
Plot the pose angle, count value and step stage.
Args:
angle_text (str): angle value for workout monitoring
count_text (str): counts value for workout monitoring
stage_text (str): stage decision for workout monitoring
center_kpt (list): centroid pose index for workout monitoring
color (tuple): text background color for workout monitoring
txt_color (tuple): text foreground color for workout monitoring
"""
angle_text, count_text, stage_text = (f" {angle_text:.2f}", f"Steps : {count_text}", f" {stage_text}")
# Draw angle
(angle_text_width, angle_text_height), _ = cv2.getTextSize(angle_text, 0, self.sf, self.tf)
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 + (self.tf * 2))
cv2.rectangle(
self.im,
angle_background_position,
(
angle_background_position[0] + angle_background_size[0],
angle_background_position[1] + angle_background_size[1],
),
color,
-1,
)
cv2.putText(self.im, angle_text, angle_text_position, 0, self.sf, txt_color, self.tf)
# Draw Counts
(count_text_width, count_text_height), _ = cv2.getTextSize(count_text, 0, self.sf, self.tf)
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 + self.tf)
cv2.rectangle(
self.im,
count_background_position,
(
count_background_position[0] + count_background_size[0],
count_background_position[1] + count_background_size[1],
),
color,
-1,
)
cv2.putText(self.im, count_text, count_text_position, 0, self.sf, txt_color, self.tf)
# Draw Stage
(stage_text_width, stage_text_height), _ = cv2.getTextSize(stage_text, 0, self.sf, self.tf)
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],
),
color,
-1,
)
cv2.putText(self.im, stage_text, stage_text_position, 0, self.sf, txt_color, self.tf)
def seg_bbox(self, mask, mask_color=(255, 0, 255), label=None, txt_color=(255, 255, 255)):
"""
Function for drawing segmented object in bounding box shape.
Args:
mask (list): masks data list for instance segmentation area plotting
mask_color (RGB): mask foreground color
label (str): Detection label text
txt_color (RGB): text color
"""
cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)
text_size, _ = cv2.getTextSize(label, 0, self.sf, self.tf)
cv2.rectangle(
self.im,
(int(mask[0][0]) - text_size[0] // 2 - 10, int(mask[0][1]) - text_size[1] - 10),
(int(mask[0][0]) + text_size[0] // 2 + 10, int(mask[0][1] + 10)),
mask_color,
-1,
)
if label:
cv2.putText(
self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1])), 0, self.sf, txt_color, self.tf
)
def plot_distance_and_line(self, distance_m, distance_mm, centroids, line_color, centroid_color):
"""
Plot the distance and line on frame.
Args:
distance_m (float): Distance between two bbox centroids in meters.
distance_mm (float): Distance between two bbox centroids in millimeters.
centroids (list): Bounding box centroids data.
line_color (RGB): Distance line color.
centroid_color (RGB): Bounding box centroid color.
"""
(text_width_m, text_height_m), _ = cv2.getTextSize(f"Distance M: {distance_m:.2f}m", 0, self.sf, self.tf)
cv2.rectangle(self.im, (15, 25), (15 + text_width_m + 10, 25 + text_height_m + 20), line_color, -1)
cv2.putText(
self.im,
f"Distance M: {distance_m:.2f}m",
(20, 50),
0,
self.sf,
centroid_color,
self.tf,
cv2.LINE_AA,
)
(text_width_mm, text_height_mm), _ = cv2.getTextSize(f"Distance MM: {distance_mm:.2f}mm", 0, self.sf, self.tf)
cv2.rectangle(self.im, (15, 75), (15 + text_width_mm + 10, 75 + text_height_mm + 20), line_color, -1)
cv2.putText(
self.im,
f"Distance MM: {distance_mm:.2f}mm",
(20, 100),
0,
self.sf,
centroid_color,
self.tf,
cv2.LINE_AA,
)
cv2.line(self.im, centroids[0], centroids[1], line_color, 3)
cv2.circle(self.im, centroids[0], 6, centroid_color, -1)
cv2.circle(self.im, centroids[1], 6, centroid_color, -1)
def visioneye(self, box, center_point, color=(235, 219, 11), pin_color=(255, 0, 255)):
"""
Function for pinpoint human-vision eye mapping and plotting.
Args:
box (list): Bounding box coordinates
center_point (tuple): center point for vision eye view
color (tuple): object centroid and line color value
pin_color (tuple): visioneye point color value
"""
center_bbox = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
cv2.circle(self.im, center_point, self.tf * 2, pin_color, -1)
cv2.circle(self.im, center_bbox, self.tf * 2, color, -1)
cv2.line(self.im, center_point, center_bbox, color, self.tf)
@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 # scope for faster 'import ultralytics'
import seaborn # scope for faster 'import ultralytics'
# 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 = pandas.DataFrame(boxes, columns=["x", "y", "width", "height"])
# Seaborn correlogram
seaborn.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")
seaborn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9)
seaborn.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: Union[torch.Tensor, np.ndarray],
batch_idx: Union[torch.Tensor, np.ndarray],
cls: Union[torch.Tensor, np.ndarray],
bboxes: Union[torch.Tensor, np.ndarray] = np.zeros(0, dtype=np.float32),
confs: Optional[Union[torch.Tensor, np.ndarray]] = None,
masks: Union[torch.Tensor, np.ndarray] = np.zeros(0, dtype=np.uint8),
kpts: Union[torch.Tensor, np.ndarray] = np.zeros((0, 51), dtype=np.float32),
paths: Optional[List[str]] = None,
fname: str = "images.jpg",
names: Optional[Dict[int, str]] = None,
on_plot: Optional[Callable] = None,
max_size: int = 1920,
max_subplots: int = 16,
save: bool = True,
conf_thres: float = 0.25,
) -> Optional[np.ndarray]:
"""
Plot image grid with labels, bounding boxes, masks, and keypoints.
Args:
images: Batch of images to plot. Shape: (batch_size, channels, height, width).
batch_idx: Batch indices for each detection. Shape: (num_detections,).
cls: Class labels for each detection. Shape: (num_detections,).
bboxes: Bounding boxes for each detection. Shape: (num_detections, 4) or (num_detections, 5) for rotated boxes.
confs: Confidence scores for each detection. Shape: (num_detections,).
masks: Instance segmentation masks. Shape: (num_detections, height, width) or (1, height, width).
kpts: Keypoints for each detection. Shape: (num_detections, 51).
paths: List of file paths for each image in the batch.
fname: Output filename for the plotted image grid.
names: Dictionary mapping class indices to class names.
on_plot: Optional callback function to be called after saving the plot.
max_size: Maximum size of the output image grid.
max_subplots: Maximum number of subplots in the image grid.
save: Whether to save the plotted image grid to a file.
conf_thres: Confidence threshold for displaying detections.
Returns:
np.ndarray: Plotted image grid as a numpy array if save is False, None otherwise.
Note:
This function supports both tensor and numpy array inputs. It will automatically
convert tensor inputs to numpy arrays for processing.
"""
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()
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 in range(bs):
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
mosaic[y : y + h, x : x + w, :] = images[i].transpose(1, 2, 0)
# 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(bs):
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")
labels = confs is None
if len(bboxes):
boxes = bboxes[idx]
conf = confs[idx] if confs is not None else None # check for confidence presence (label vs pred)
if len(boxes):
if boxes[:, :4].max() <= 1.1: # if normalized with tolerance 0.1
boxes[..., [0, 2]] *= w # scale to pixels
boxes[..., [1, 3]] *= h
elif scale < 1: # absolute coords need scale if image scales
boxes[..., :4] *= scale
boxes[..., 0] += x
boxes[..., 1] += y
is_obb = boxes.shape[-1] == 5 # xywhr
boxes = ops.xywhr2xyxyxyxy(boxes) if is_obb else ops.xywh2xyxy(boxes)
for j, box in enumerate(boxes.astype(np.int64).tolist()):
c = classes[j]
color = colors(c)
c = names.get(c, c) if names else c
if labels or conf[j] > conf_thres:
label = f"{c}" if labels else f"{c} {conf[j]:.1f}"
annotator.box_label(box, label, color=color, rotated=is_obb)
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] > conf_thres:
annotator.kpts(kpts_[j], conf_thres=conf_thres)
# 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 in range(len(image_masks)):
if labels or conf[j] > conf_thres:
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)
if not save:
return np.asarray(annotator.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 # scope for faster 'import ultralytics'
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 # scope for faster 'import ultralytics'
from scipy.ndimage import gaussian_filter1d
def _save_one_file(file):
"""Save one matplotlib plot to 'file'."""
plt.savefig(file, dpi=200)
plt.close()
LOGGER.info(f"Saved {file}")
# 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=0.8, 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([])
_save_one_file(csv_file.with_name("tune_scatter_plots.png"))
# 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()
_save_one_file(csv_file.with_name("tune_fitness.png"))
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:-1], targets[:, -1]
def output_to_rotated_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, angle = o[:max_det].cpu().split((4, 1, 1, 1), 1)
j = torch.full((conf.shape[0], 1), i)
targets.append(torch.cat((j, cls, box, angle, conf), 1))
targets = torch.cat(targets, 0).numpy()
return targets[:, 0], targets[:, 1], targets[:, 2:-1], targets[:, -1]
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", "Segment", "Pose", "Classify", "OBB", "RTDETRDecoder"}: # all model heads
if m in module_type:
return
if isinstance(x, torch.Tensor):
_, 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
_, 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
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