# Ultralytics YOLO 🚀, AGPL-3.0 license 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 IS_COLAB, IS_KAGGLE, 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 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. """ 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) 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=None, 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. """ radius = radius if radius is not None else self.lw 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=int(np.ceil(self.lw / 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.""" im = Image.fromarray(np.asarray(self.im)[..., ::-1]) # Convert numpy array to PIL Image with RGB to BGR if IS_COLAB or IS_KAGGLE: # can not use IS_JUPYTER as will run for all ipython environments try: display(im) # noqa - display() function only available in ipython environments except ImportError as e: LOGGER.warning(f"Unable to display image in Jupyter notebooks: {e}") else: im.show(title=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) # Draw small circles at the corner points for point in reg_pts: cv2.circle(self.im, (point[0], point[1]), thickness * 2, color, -1) # -1 fills the circle 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 (tuple): RGB queue region color. txt_color (tuple): 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 (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 """ 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 (tuple): display color for text foreground bg_color (tuple): 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, 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]. 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. """ indices = indices or [2, 5, 7] points = [(int(k[0]), int(k[1])) for i, k in enumerate(keypoints) if i in indices and k[2] >= conf_thres] # Draw lines between consecutive points for start, end in zip(points[:-1], points[1:]): cv2.line(self.im, start, end, (0, 255, 0), 2, lineType=cv2.LINE_AA) # Draw circles for keypoints for pt in points: cv2.circle(self.im, pt, radius, (0, 0, 255), -1, lineType=cv2.LINE_AA) return self.im def plot_workout_information(self, display_text, position, color=(104, 31, 17), txt_color=(255, 255, 255)): """ Draw text with a background on the image. Args: display_text (str): The text to be displayed. position (tuple): Coordinates (x, y) on the image where the text will be placed. color (tuple, optional): Text background color txt_color (tuple, optional): Text foreground color """ (text_width, text_height), _ = cv2.getTextSize(display_text, 0, self.sf, self.tf) # Draw background rectangle cv2.rectangle( self.im, (position[0], position[1] - text_height - 5), (position[0] + text_width + 10, position[1] - text_height - 5 + text_height + 10 + self.tf), color, -1, ) # Draw text cv2.putText(self.im, display_text, position, 0, self.sf, txt_color, self.tf) return text_height 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, optional): Text background color txt_color (tuple, optional): Text foreground color """ # Format text angle_text, count_text, stage_text = f" {angle_text:.2f}", f"Steps : {count_text}", f" {stage_text}" # Draw angle, count and stage text angle_height = self.plot_workout_information( angle_text, (int(center_kpt[0]), int(center_kpt[1])), color, txt_color ) count_height = self.plot_workout_information( count_text, (int(center_kpt[0]), int(center_kpt[1]) + angle_height + 20), color, txt_color ) self.plot_workout_information( stage_text, (int(center_kpt[0]), int(center_kpt[1]) + angle_height + count_height + 40), color, txt_color ) 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 (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. """ if mask.size == 0: # no masks to plot return 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, pixels_distance, centroids, line_color, centroid_color): """ Plot the distance and line on frame. Args: pixels_distance (float): Pixels distance between two bbox centroids. centroids (list): Bounding box centroids data. line_color (tuple): RGB distance line color. centroid_color (tuple): RGB bounding box centroid color. """ # Get the text size (text_width_m, text_height_m), _ = cv2.getTextSize( f"Pixels Distance: {pixels_distance:.2f}", 0, self.sf, self.tf ) # Define corners with 10-pixel margin and draw rectangle top_left = (15, 25) bottom_right = (15 + text_width_m + 20, 25 + text_height_m + 20) cv2.rectangle(self.im, top_left, bottom_right, centroid_color, -1) # Calculate the position for the text with a 10-pixel margin and draw text text_position = (top_left[0] + 10, top_left[1] + text_height_m + 10) cv2.putText( self.im, f"Pixels Distance: {pixels_distance:.2f}", text_position, 0, self.sf, (255, 255, 255), 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) try: im[y : y + h, x : x + w, :][mask] = ( im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6 ) except: # noqa E722 pass 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