a
yfX @ sV d dl Z d dlZd dlZd dlmZ d dlmZmZmZm Z m
Z
d dl Z d dl m
Z d dlZd dlZd dlmZmZmZ d dlmZ d dlmZmZmZm Z m Z m Z d dl m Z m!Z!m"Z"m#Z# d dl$m%Z% G d d
d
Z&e& Z'G d d d Z(e e d
eddfddZ)eddddddfddZ*e ej+d ej,ddej+d ej-dej+dej,ddddddd dd f e
ej.ej/f e
ej.ej/f e
ej.ej/f e
ej.ej/f e e
ej.ej/f e
ej.ej/f e
ej.ej/f e ee0 e0e ee1e0f e e e1e1e2e3e ej/ d d d Z4e d6d"d#Z5d7d(d)Z6d8d+d,Z7d9d.d/Z8d:d0d1Z9d2ed3fd4d5Z:dS ); N)Path)CallableDictListOptionalUnion)Image ImageDraw ImageFont) __version__)
IS_JUPYTERLOGGER TryExceptops plt_settingsthreaded)
check_fontcheck_requirements
check_versionis_ascii)increment_pathc @ s. e Zd ZdZdd Zd
ddZedd Zd S ) Colorsa
Ultralytics color palette https://docs.ultralytics.com/reference/utils/plotting/#ultralytics.utils.plotting.Colors.
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.
## Ultralytics Color Palette
| Index | Color | HEX | RGB |
|-------|-------------------------------------------------------------------|-----------|-------------------|
| 0 | | `#042aff` | (4, 42, 255) |
| 1 | | `#0bdbeb` | (11, 219, 235) |
| 2 | | `#f3f3f3` | (243, 243, 243) |
| 3 | | `#00dfb7` | (0, 223, 183) |
| 4 | | `#111f68` | (17, 31, 104) |
| 5 | | `#ff6fdd` | (255, 111, 221) |
| 6 | | `#ff444f` | (255, 68, 79) |
| 7 | | `#cced00` | (204, 237, 0) |
| 8 | | `#00f344` | (0, 243, 68) |
| 9 | | `#bd00ff` | (189, 0, 255) |
| 10 | | `#00b4ff` | (0, 180, 255) |
| 11 | | `#dd00ba` | (221, 0, 186) |
| 12 | | `#00ffff` | (0, 255, 255) |
| 13 | | `#26c000` | (38, 192, 0) |
| 14 | | `#01ffb3` | (1, 255, 179) |
| 15 | | `#7d24ff` | (125, 36, 255) |
| 16 | | `#7b0068` | (123, 0, 104) |
| 17 | | `#ff1b6c` | (255, 27, 108) |
| 18 | | `#fc6d2f` | (252, 109, 47) |
| 19 | | `#a2ff0b` | (162, 255, 11) |
## Pose Color Palette
| Index | Color | HEX | RGB |
|-------|-------------------------------------------------------------------|-----------|-------------------|
| 0 | | `#ff8000` | (255, 128, 0) |
| 1 | | `#ff9933` | (255, 153, 51) |
| 2 | | `#ffb266` | (255, 178, 102) |
| 3 | | `#e6e600` | (230, 230, 0) |
| 4 | | `#ff99ff` | (255, 153, 255) |
| 5 | | `#99ccff` | (153, 204, 255) |
| 6 | | `#ff66ff` | (255, 102, 255) |
| 7 | | `#ff33ff` | (255, 51, 255) |
| 8 | | `#66b2ff` | (102, 178, 255) |
| 9 | | `#3399ff` | (51, 153, 255) |
| 10 | | `#ff9999` | (255, 153, 153) |
| 11 | | `#ff6666` | (255, 102, 102) |
| 12 | | `#ff3333` | (255, 51, 51) |
| 13 | | `#99ff99` | (153, 255, 153) |
| 14 | | `#66ff66` | (102, 255, 102) |
| 15 | | `#33ff33` | (51, 255, 51) |
| 16 | | `#00ff00` | (0, 255, 0) |
| 17 | | `#0000ff` | (0, 0, 255) |
| 18 | | `#ff0000` | (255, 0, 0) |
| 19 | | `#ffffff` | (255, 255, 255) |
!!! note "Ultralytics Brand Colors"
For Ultralytics brand colors see [https://www.ultralytics.com/brand](https://www.ultralytics.com/brand). Please use the official Ultralytics colors for all marketing materials.
c s d} fdd|D _ t j _tjg dg dg dg dg dg d g d
g d g d g d
g dg dg dg dg dg dg dg dg dg dgtjd _dS )zEInitialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values().)Z042AFFZ0BDBEBZF3F3F3Z00DFB7Z111F68ZFF6FDDZFF444FZCCED00Z00F344ZBD00FFZ00B4FFZDD00BAZ00FFFFZ26C000Z01FFB3Z7D24FFZ7B0068ZFF1B6CZFC6D2FZA2FF0Bc s g | ]} d | qS )#)hex2rgb).0cself V/var/www/html/django/DPS/env/lib/python3.9/site-packages/ultralytics/utils/plotting.py
p z#Colors.__init__..) r )r" 3 )r" f ) r( r )r" r$ r" )r$ r" )r" r' r" )r" r% r" )r' r& r" )r% r$ r" )r" r$ r$ )r" r' r' )r" r% r% )r$ r" r$ )r' r" r' )r% r" r% r r" r )r r r" )r" r r r" r" r" dtypeN)palettelennnparrayuint8 pose_palette)r Zhexsr r r __init__X s6 zColors.__init__Fc C s2 | j t|| j }|r.|d |d |d fS |S )z'Converts hex color codes to RGB values. r )r. intr0 )r iZbgrr r r r __call__ s zColors.__call__c s t fdddD S )z@Converts hex color codes to RGB values (i.e. default PIL order).c 3 s, | ]$}t d | d | d dV qdS )r7 r6 Nr8 r r9 hr r r! z!Colors.hex2rgb..)r r6 tupler> r r> r r s zColors.hex2rgbN)F)__name__
__module__ __qualname____doc__r5 r: staticmethodr r r r r r s
B4
r c @ s e Zd ZdZdIddZdJd
d ZdKddZdLddZdMddZdNddZ dOdd Z
dPd d Z dQd!d"Z d#d$ Z
d%d& ZdRd'd(ZdSd*d+ZdTd,d-ZdUd/d0ZdVd2d3ZdWd5d6Zd7d8 Zd9d: Zed;d< ZdXd=d>ZdYd@dAZdZdBdCZdDdE Z d[dGdHZ dS )\ Annotatora7
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.
N Arial.ttfFabcc
s t | }t|tj}|p |p | _|pLttt|r8|jn|jd d d _ jr|r^|nt
| _ t
j _zHt|rdn|}|pttt j jd d d} tt|| _W n ty t _Y n0 ttdr fdd j_nH|jjs J d |jj r|n| _ t j d
d
_ j d _ d d
gd
dgddgddgddgddgddgddgddgddgddgddgdd gd
dgd
d gddgd dgddgddgg _ t!j"g d _#t!j"g d _$h d _%h d _&d S ) zjInitialize the Annotator class with image and line width along with color palette for keypoints and limbs.r6 g~jth?zArial.Unicode.ttfgQ ? z9.2.0c s j | dd S )Nr6 rA )fontZgetbbox)xr r r r! z$Annotator.__init__..zOImage not contiguous. Apply np.ascontiguousarray(im) to Annotator input images.r7 r;
rA )rX rX rX rX rV rV rV r r r r r r; r; r; r; r; r; r; )r; r; r; r; r; r r r r r r rX rX rX rX rX rX > )r" r" r ) r\ r\ )rZ r" ) o r" )D r\ r ) r" r7 rZ ) r )r r) > )h r { )r" * rA )/ m )r & )r" r )O r` r" )r" r ) r r^ )r" $ } rh rR )l r" N)'r
isinstancer pilmaxroundsumsizeshapelw fromarrayimr Drawdrawr r
ZtruetypestrrM ExceptionZ load_defaultr pil_versiongetsizedata
contiguousflagsZ writeablecopytfsfskeletoncolorsr4
limb_color kpt_color dark_colors light_colors)
r r
line_width font_sizerM r{ exampleZ non_asciiZ input_is_pilr r r r r5 sV
,"
zAnnotator.__init__r# r# r# r+ c C s$ || j v rdS || jv r dS |S dS )z,Assign text color based on background color.rv r+ N)r r )r color txt_colorr r r
get_txt_color s
zAnnotator.get_txt_color r6 c
C s t |dkr,tdt | d |dd }t|d |d d t|d |d d }}tt|tj| jd | jd }t|d d |d d d d | } t | j
||f| |d
||d d }
||d d } tj | j
t||
| ftj| jd | ||| jtj
d dS ) a
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.
rP zLength of label is zF, initial 3 label characters will be considered for circle annotation!Nr r6 r7 g333333? ?lineType)r/ printr8 cv2 getTextSizer FONT_HERSHEY_SIMPLEXr r circler putTextr LINE_AA) r boxlabelr r marginx_centery_center text_sizeZrequired_radiustext_xtext_yr r r circle_label s* 2"(
zAnnotator.circle_labelr[ c
C s t |d |d d t |d |d d }}t|tj| jd | jd }||d d } ||d d }
| | } |
|d | } | |d | }
|
| }t| j| | f|
|f|d tj| j|| |
ftj| jd | ||| jtj
d dS ) a
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.
r r6 r7 rP g?r r N) r8 r r r r r rectangler r r r )r r r r r r r r r r r rect_x1rect_y1rect_x2rect_y2r r r
text_label# s& 2
zAnnotator.text_labelc
C s | ||}t|tjr | }| js0t|sd|r\|d }| jjdd |D | j |d n$|d |d f}| jj
|| j |d |r| j |\}}|d |k} |d | j
jd | kr| j
jd | |d f}| jj
|d | r|d | n|d |d | d | r|d d n|d | d f|d | jj|d | rJ|d | n|d f||| j d n~|rdd |d D }t| j
tj|td gd
|| j nNt|d t|d ft|d t|d f }}
tj
| j
||
|| j tjd
|rtj|d| j| jdd \}}|d 7 }|d |k} |d | j
jd | krX| j
jd | |d f}|d | | rt|d | n
|d | f}
t
| j
||
|dtj tj| j
||d | r|d d n|d | d fd| j|| jtjd
dS )a
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
r c S s g | ] }t |qS r rB r br r r r Y r! z'Annotator.box_label..widthoutliner7 fillr rM c S s g | ] }t |qS r r< r r r r r j r! r, Tr6 rP thicknessr fontScaler r N)r rz torchTensortolistr{ r r Zpolygonr r rM r r r textr polylinesr1 asarrayr8 r r r r r r ) r r r r r rotatedp1wr? outsidep2r r r box_labelH sT L8$2 *(zAnnotator.box_labelr c C sH | j rt| j | _t|dkrL|ddd d | jdd< |j
|j
krd| |j
}t j
||j
t jdd }|ddddf }|d}||| }d|| d}|jdd j}|jdgd
}|ddd }||d | }|d } | }
|r|
nt|
| jj| jdd< | j rD| | j dS ) a
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.
r r7 r6 r" N)devicer- g o@rP dim)dimsr )r{ r1 r r r r/ Zpermuter cpunumpyr tor Ztensorfloat32 unsqueezeZcumprodr| valuesflipbyter Z scale_imager r ) r masksr Zim_gpualphaZ retina_masksZ masks_colorZinv_alpha_masksmcsZim_maskZ
im_mask_npr r r r s( (
$zAnnotator.masks r T ?c C s |dur |n| j }| jr*t| j | _|j\}}|dkoB|dv } || M }t|D ]\}
} |px| rr| j|
nt
|
} | d | d }
}|
|d dkrT||d dkrTt | dkr| d }||k rqTt j
| jt|
t|f|| dt jd qT|r|jd }t| jD ]\}
}t||d d df t||d d df f}t||d d df t||d d df f}|dkr||d d df }||d d df }||k s ||k rq |d |d dks |d |d dks |d dk s |d dk r q |d |d dks |d |d dks |d dk s |d dk rXq t j| j|||pv| j|
tt| j d t jd
q | jr| | j dS ) a
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.
NrR > r6 rP r r7 rP r6 r r r )r r{ r1 r r r r enumerater r r r/ r r r8 r r liner ceilr )r kptsr radiusZkpt_line
conf_thresr ZnkptndimZis_poser9 kZcolor_kx_coordy_coordconfskZpos1pos2Zconf1Zconf2r r r r sN
(
00
HH
zAnnotator.kptsr7 c C s | j |||| dS )z"Add rectangle to image (PIL-only).N)r r )r xyr r r r r r r s zAnnotator.rectangletopc
C s |dkr,| j |\}}|d d| 7 < | jr|r| j |\}}| jj|d |d |d | d |d | d f|d d}d|v r|d}| j |\} }|D ]*}
| jj||
|| j d |d |7 < qn| jj|||| j d n|rltj|d| j | j
dd \}}|d 7 }|d |k} |d | | rB|d | n
|d | f} t| j || |d
tj d}tj
| j ||d| j || j
tj d d S )
z'Adds text to an image using PIL or cv2.bottomr7 r r r+
r r rP r r N)rM r r{ r r splitr r r r r r r r )
r r r r anchor box_styler r? lines_r r r r r r r s. 8
*zAnnotator.textc C s. t |tjr|nt|| _t| j| _dS )z"Update self.im from a numpy array.N)rz r r r r r r )r r r r r r s zAnnotator.fromarrayc C s t | jS )z Return annotated image as array.)r1 r r r r r r result s zAnnotator.resultc
C s t t| jddddf }tr|td zddlm} || W q t yx } z t
d| W Y d}~qd}~0 0 n |j |d dS ) zShow the annotated image..Nr ipythonr )displayz.Unable to display image in Jupyter notebooks: )title)
r r r1 r r r r IPython.displayr ImportErrorr
warningshow)r r r r er r r r
s (zAnnotator.show image.jpgc C s t |t| j dS )z'Save the annotated image to 'filename'.N)r Zimwriter1 r r )r filenamer r r save s zAnnotator.savec C s* |\}}}}|| }|| }|||| fS )z
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
r )r ZbboxZx_minZy_minZx_maxZy_maxr heightr r r get_bbox_dimension s
z Annotator.get_bbox_dimensionr* c C sV t j| jtj|tjdgd||d |D ](}t | j|d |d f|d |d q(dS ) z
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
r, TZisClosedr r r r7 r6 r N)r r r r1 r2 int32r )r Zreg_ptsr r pointr r r draw_region/ s $zAnnotator.draw_regionr" r r" c C sh t |t jd}tj| j|gd||d t| jt |d d t |d d f|d |d dS ) z
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
)r r7 r6 Fr r r r7 r6 N)
r1 Zhstackastyper reshaper r r r r8 )r trackr Ztrack_thicknesspointsr r r draw_centroid_and_tracks> s z"Annotator.draw_centroid_and_tracksr r r c
C s dd |D }dd |D }t |t| }t |t| }tj|d| j| jdd } | d }
| d } |
d } | d }
|| d ||
d f}|| d ||
d f}t| j|||d ||
d }|| d }tj| j|||fd| j|| jtj d
d S ) au
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 (tuple): RGB queue region color.
txt_color (tuple): RGB text display color.
c S s g | ] }|d qS )r r r r r r r r U r! z2Annotator.queue_counts_display..c S s g | ] }|d qS )r7 r r r r r r V r! r r r7 r6 r )r r r r N)
r~ r/ r r r r r r r r )r r r Z region_colorr Zx_valuesZy_valuesZcenter_xZcenter_yr
text_widthZ text_heightZ
rect_widthZ rect_heightZ
rect_top_leftZrect_bottom_rightr r r r r queue_counts_displayK s0
z Annotator.queue_counts_displayc
C s t j|d| j| jdd }||d d } ||d d }
| | } |
|d | } | |d | }
|
| }t || | f|
|f|d t j||| |
fd| j|| jt jd dS )a
Display the bounding boxes labels in parking management app.
Args:
im0 (ndarray): inference image
text (str): object/class name
txt_color (tuple): display color for text foreground
bg_color (tuple): 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
r r r6 r7 r r N)r r r r r r r )r im0r r bg_colorr r r r r r r r r r r r r display_objects_labelss s
z Annotator.display_objects_labelsc C s: t |jd d }t |jd d }d}| D ]\} }
| d|
} t| d| j| jd } | d dk sx| d dk r|d} |jd | d |d | }
|| d |d | }|
|d }|| d |d }|
| d |d }||d }t|||f||f|d tj|| |
|fd| j|| jtj d
|}q0d S ) at
Display the overall statistics for parking lots.
Args:
im0 (ndarray): inference image
text (dict): labels dictionary
txt_color (tuple): display color for text foreground
bg_color (tuple): display color for text background
margin (int): gap between text and rectangle for better display
r7 g{Gz?r {Gz?: r[ )r[ r[ r6 r r N)
r8 r itemsr r r r r r r )r r r r r r Zhorizontal_gapZ vertical_gapZ
text_y_offsetr valuetxtr r r r r r r r r r display_analytics s" $zAnnotator.display_analyticsc C s t | t |t | } }}t |d |d |d |d t | d |d | d |d }t |d t j }|dkrd| }|S )a1
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
r7 r g f@ih )r1 r2 Zarctan2abspi)ar r radiansangler r r estimate_pose_angle s
"Hz Annotator.estimate_pose_anglec C s |du rg d}t |D ]\}}||v r|d |d }} ||d dkr| |d dkrt|dkrx|d }
|
|k rxqtj| jt|t| f|ddtjd q| jS )
a
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.
N)r6 r[ rV r r7 rP r6 r* r r )r r/ r r r r8 r ) r Z keypointsindicesr r r r9 r r r r r r r draw_specific_points s (z Annotator.draw_specific_pointsrv c C s@ d|dd| d| }}}t |d| j| j\\}}} t|d t|d f}
|
d |
d | d f} |d |d | jd f} t | j| | d | d | d | d f|d t | j||
d| j|| j t |d| j| j\\}
}} |
d |
d | d
f}| d | d | d d f}|
d |d | j f}t | j||d |d |d |d f|d t | j||d| j|| j t |d| j| j\\}}} t|d t|d | | d f}|d |d | d f}|d |d f}t | j||d |d |d |d f|d t | j||d| j|| j d S )
a
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
.2fzSteps : r r7 r[ rY r6 r r ( N)r r r r r8 r r r )r Z
angle_textZ
count_textZ
stage_textZ
center_kptr r Zangle_text_widthZangle_text_heightr Zangle_text_positionZangle_background_positionZangle_background_sizeZcount_text_widthZcount_text_heightZcount_text_positionZcount_background_positionZcount_background_sizeZstage_text_widthZstage_text_heightZstage_text_positionZstage_background_positionZstage_background_sizer r r plot_angle_and_count_and_stage sZ $
$
z(Annotator.plot_angle_and_count_and_stagec C s |j dkrdS tj| jt|ggd|dd t|d| j| j\}}t | jt
|d d |d d d t
|d d |d d ft
|d d |d d d t
|d d d f|d |r t | j|t
|d d |d d t
|d d fd| j|| j dS ) a
Function for drawing segmented object in bounding box shape.
Args:
mask (np.ndarray): A 2D array of shape (N, 2) containing the contour points of the segmented object.
mask_color (tuple): RGB color for the contour and label background.
label (str, optional): Text label for the object. If None, no label is drawn.
txt_color (tuple): RGB color for the label text.
r NTr6 r rY r7 r ) r r r r r1 r r r r r r8 r )r maskZ
mask_colorr r r r r r r seg_bbox# s
:2.Z instances Z rY )ZrotationfontsizeclassesrN r4 )rN r4 axr: r; r r rP )rI rI rP r, r" i r off)r r7 r6 rP )r rightleftr F)/pandasseabornwarningsfilterwarnings UserWarning
FutureWarningr
infor8 r| Z DataFrameZpairplotdictpltsavefigclosesubplotsravelr7 r1 ZlinspacerangeZpatchesZ set_colorr Z
set_ylabelr/ Z
set_xticksZset_xticklabelslistr Z
set_xlabelZhistplotr xywh2xyxyr r Zonesr3 zipr r r imshowaxisZspinesZ set_visible)boxesclsnamessave_diron_plotrM rN ncrN rH r4 r9 Zimgr r sfnamer r r plot_labelsu sJ "& $ " rh zim.jpggR Q?rY FTc C s< t | tjst| } t| dd}|r`|ddddf dd d|ddddf< |ddddf | | |ddddf< t |
} t | |j } |t
| d t
| d t
| d t
| d
dd|rdndf } |r8|jjd d d tt|d
}
t| ddddf j|
ddd | S )a
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)
```
r rA Nr6 r7 r )r r7 )r rP )r r )r r6 T)parentsexist_okz.jpg._ )ZqualityZ subsampling)rz r r stackr xyxy2xywhviewr| r r\ longZ
clip_boxesr r8 parentmkdirr r with_suffixr r r ) Zxyxyr fileZgainpadZsquareZBGRr r cropfr r r save_one_box s
4,D$rw r, )r r% z
images.jpgi r; r )images batch_idxra bboxesconfsr r pathsrg rb rd max_size max_subplotsr r returnc -
s t | tjr | } t |tjr4| }t |tjrL| }t |tjrj| t}t |tjr| }t |tjr| }| j\}}}}t || }t
|d t
| d dkr| d9 } t
j
t | t | dfdt
jd}t|D ]X}t|| t|| }}| | ddd|||| ||| ddf< q
| t || }|dk rt || }t || }t|t fd d
||fD }t|| d }t|t|d |d
| d}t|D ]}t|| t|| }}|j|||| || gdddd |rr|j|d |d ft|| jdd dd t|dkr||k}|| d}|du } t|r|| } |dur|| nd} t| rB| ddddf dkr | dddgf |9 < | dddgf |9 < n"|dk rB| dddf |9 < | d |7 < | d |7 < | jd dk} | rt | nt | } t | t
j ! D ]v\} }!|| }"t"|"}#| r| #|"|"n|"}"| s| | |kr| r|" n|" d | | d }$|j$|!|$|#| d qnLt|rd|D ]<}"t"|"}#| rD| #|"|"n|"}"|j||f|" |#d
d q&t|r6|| % }%t|%r|%d d ks|%d d kr|%d |9 < |%d |9 < n|dk r|%|9 }%|%d |7 < |%d |7 < tt|%D ],} | s | | |kr|j&|%| |d! qt|r|jd |jd kr`|| }&nN||g }&|' }'t
(|')|'ddfd }(t
j*|&|'dd"}&t
+|&|(kd#d$}&t
,|j-% })tt|&D ]} | s| | |krt"|| }#|&| j\}*}+|*|ks|+|kr>|&| t
j},t|,||f},|,t.},n|&| t.},t/0t1j |)||| ||| ddf |, d% t
2|#d& |)||| ||| ddf |,< W d n1 s0 Y q|3|) q|
st
,|j-S |j-4| |
r|
| dS )'a
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.
r r r7 r" rP r, r6 Nc 3 s | ]}t | V qd S )Nr< rC nsr r r@ ' r! z plot_images..r rY T)r r r{ r r+ )r r[ r( ) r r )r r r8 rA g?.).r ).r7 r r& z.1f)r r )r r g)\(?)r )r_ g ?g g?g333333?)5rz r r r floatr r r8 r minr1 r r| fullr3 rZ Z transposemathr resizerC rI r} r r r namer/ r Zxywhr2xyxyxyxyr\ r Zint64r r getr r r r~ Zaranger repeatwherer r bool
contextlibsuppressr r2 r r )-rx ry ra rz r{ r r r| rg rb rd r} r~ r r bsr r? r Zmosaicr9 rN r4 scalefsZ annotatoridxrG labelsr` r Zis_obbjr r r r Zkpts_Z image_masksnlindexr mhmwr* r r r plot_images s -
( "4
""".
$
6H r path/to/results.csvc C s ddl }ddlm} | r"t| jnt|}|rNtjddddd\} }
g d} nf|rrtjdd d
dd\} }
g d } nB|rtjdd d
dd\} }
g d} n tjddddd\} }
g d} |
}
t| d} t
| sJ d| d| D ]}
z| |
}dd |j
D }|jdddf }t| D ]t\}}|jdd|f d}|
| j||d|
jdd d |
| j|||ddd d dd |
| j|| d d q$W q ty } z"td!|
d"| W Y d}~qd}~0 0 q|
d# |d$ }| j|d%d& t |r|| dS )'aR
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)
```
r Ngaussian_filter1dr6 )rU rU Tr@ )r7 rA r6 rP rW ) rU )r7 r6 rP rA r[ rU rX rY rT rQ rS r; rV rW rZ rL rX ) rU )r7 r6 rP rA r[ rU rV rY rZ rQ rS r; rR r rW rX rL rT r[ )rL rU )
r7 r6 rP rA r[ rW rX rY rU rV z results*.csvz No results.csv files found in z, nothing to plot.c S s g | ] }| qS r striprC r r r r r! z plot_results..r .)markerr linewidth
markersizerP sigma:Zsmoothr r rL )rF z WARNING: Plotting error for r r7 z results.pngr= r> )rM
scipy.ndimager r rp rU rX rY r[ globr/ resolveread_csvr5 r r r plotstem set_titler r
r legendrV rW )rs dirsegmentZposeZclassifyrd pdr rc ZfigrH r filesrv r rf rN r9 r r4 r rg r r r plot_results sF
. r r viridisrB nonec sR t j |d\ fddttD }tj ||||d dS )a{
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)
r9 c
s` g | ]X}t tj| d dd jd d t tj | d dd jd d f qS )T)rK r7 r )r r1 Zdigitizer r= rv r7 vZxedgesZyedgesr r r s &&z%plt_color_scatter..)r cmapr
edgecolorsN)r1 Z histogram2drZ r/ rU Zscatter)r rv r: r r r r r r r plt_color_scatter s
r tune_results.csvc C s ddl }ddlm} dd }t| } || }d}dd |jD |d }|j}|dddf }t|} t
t |d }
t j
d
d d t|D ]\} } |dd| | f }
|
| }t |
|
| d t|
|d
ddd t j|| ddd t j| d|dddid t jddd | |
dkrt g q|| d tdt |d }t j
d d d t j||d dd d t j|||d!d"d#d$d%d& t d' t d( t d) t d t || d* dS )+a
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")
r Nr c S s* t j| dd t td| dS )z#Save one matplotlib plot to 'file'.r= r> zSaved N)rU rV rW r
rS )rs r r r _save_one_file s z)plot_tune_results.._save_one_filer7 c S s g | ] }| qS r r rC r r r r r! z%plot_tune_results..r )rY rY Tr@ r rB r )r r r zk+rS )r z = z.3gr rX )ZfontdictZbothrW )r_ Z labelsizeztune_scatter_plots.png)rY rU ofitness)r Z linestyler rP r r Zsmoothedr6 r zFitness vs IterationZ IterationZFitnessztune_fitness.png) rM r r r r r5 r r1 Zargmaxr r r/ rU Zfigurer Zsubplotr r r| r Z tick_paramsZyticks with_namerZ ZxlabelZylabelgridr )Zcsv_filer r r r Znum_metrics_columnskeysrN r r r0 r9 r r mur r r plot_tune_results sB
r , c
C s g }t | D ]d\}}|d|ddf dd\}}}t|jd df|}|t||t ||fd q t|d
}|dddf |dddf |ddddf |dddf fS )ZConvert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting.NrU )rA r7 r7 r7 r r6 r ) r r r r r r appendcatr rm r ) outputmax_dettargetsr9 r r r ra r r r r output_to_target s &"r c
C s g }t | D ]Z\}}|d| dd\}}}}t|jd df|} |t| ||||fd q t|d }|dddf |dddf |ddddf |dddf fS )r N)rA r7 r7 r7 r7 r r6 r ) r r r r r r r r r )
r r r r9 r r r ra r" r r r r output_to_rotated_target# s r zruns/detect/expc C sL dD ]}||v r dS qt | tjrH| j\}}}} |dkrH| dkrH|d| d|dd d }
tj| d |d d
} t||}tj t
|d d d d
\}} | } tj
ddd t|D ](}
| |
| |
| |
d qtd|
d| d| d tj|
ddd t tt|
d| d dS )a
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').
> ZPoseZ
RTDETRDecoderZDetectZOBBZClassifySegmentNr7 stager r r z
_features.pngr r rW T)rA g?)ZwspaceZhspacerJ zSaving z... (/)r Ztight)r? Z bbox_inchesz.npy)rz r r r r chunkr r rU rX r r rY Zsubplots_adjustrZ r^ Zsqueezer_ r
rS rV rW r1 r r rr r )rN module_typer r0 rc mr Zchannelsr r rv blocksrH r9 r r r feature_visualization. s&
r )r r FFFN)r r rB r )r )r )r );r r rO pathlibr typingr r r r r r Zmatplotlib.pyplotZpyplotrU r r1 r ZPILr r r
r r Zultralytics.utilsr r
r r r r Zultralytics.utils.checksr r r r Zultralytics.utils.filesr r r rI rh rw Zzerosr r3 r Zndarrayr r8 r r r r r r r r r r r r r s `6/
& ?
7