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https://github.com/THU-MIG/yolov10.git
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: 曾逸夫（Zeng Yifu） <41098760+Zengyf-CVer@users.noreply.github.com>
264 lines
10 KiB
Python
264 lines
10 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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from collections import defaultdict
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import cv2
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import numpy as np
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from ultralytics.utils.checks import check_imshow, check_requirements
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from ultralytics.utils.plotting import Annotator
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check_requirements('shapely>=2.0.0')
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from shapely.geometry import LineString, Point, Polygon
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class Heatmap:
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"""A class to draw heatmaps in real-time video stream based on their tracks."""
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def __init__(self):
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"""Initializes the heatmap class with default values for Visual, Image, track, count and heatmap parameters."""
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# Visual information
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self.annotator = None
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self.view_img = False
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self.shape = 'circle'
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# Image information
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self.imw = None
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self.imh = None
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self.im0 = None
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# Heatmap colormap and heatmap np array
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self.colormap = None
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self.heatmap = None
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self.heatmap_alpha = 0.5
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# Predict/track information
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self.boxes = None
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self.track_ids = None
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self.clss = None
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self.track_history = defaultdict(list)
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# Region & Line Information
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self.count_reg_pts = None
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self.counting_region = None
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self.line_dist_thresh = 15
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self.region_thickness = 5
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self.region_color = (255, 0, 255)
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# Object Counting Information
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self.in_counts = 0
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self.out_counts = 0
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self.counting_list = []
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self.count_txt_thickness = 0
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self.count_txt_color = (0, 0, 0)
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self.count_color = (255, 255, 255)
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# Decay factor
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self.decay_factor = 0.99
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# Check if environment support imshow
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self.env_check = check_imshow(warn=True)
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def set_args(self,
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imw,
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imh,
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colormap=cv2.COLORMAP_JET,
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heatmap_alpha=0.5,
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view_img=False,
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count_reg_pts=None,
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count_txt_thickness=2,
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count_txt_color=(0, 0, 0),
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count_color=(255, 255, 255),
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count_reg_color=(255, 0, 255),
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region_thickness=5,
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line_dist_thresh=15,
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decay_factor=0.99,
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shape='circle'):
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"""
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Configures the heatmap colormap, width, height and display parameters.
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Args:
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colormap (cv2.COLORMAP): The colormap to be set.
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imw (int): The width of the frame.
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imh (int): The height of the frame.
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heatmap_alpha (float): alpha value for heatmap display
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view_img (bool): Flag indicating frame display
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count_reg_pts (list): Object counting region points
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count_txt_thickness (int): Text thickness for object counting display
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count_txt_color (RGB color): count text color value
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count_color (RGB color): count text background color value
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count_reg_color (RGB color): Color of object counting region
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region_thickness (int): Object counting Region thickness
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line_dist_thresh (int): Euclidean Distance threshold for line counter
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decay_factor (float): value for removing heatmap area after object passed
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shape (str): Heatmap shape, rect or circle shape supported
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"""
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self.imw = imw
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self.imh = imh
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self.heatmap_alpha = heatmap_alpha
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self.view_img = view_img
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self.colormap = colormap
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# Region and line selection
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if count_reg_pts is not None:
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if len(count_reg_pts) == 2:
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print('Line Counter Initiated.')
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self.count_reg_pts = count_reg_pts
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self.counting_region = LineString(count_reg_pts)
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elif len(count_reg_pts) == 4:
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print('Region Counter Initiated.')
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self.count_reg_pts = count_reg_pts
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self.counting_region = Polygon(self.count_reg_pts)
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else:
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print('Region or line points Invalid, 2 or 4 points supported')
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print('Using Line Counter Now')
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self.counting_region = Polygon([(20, 400), (1260, 400)]) # dummy points
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# Heatmap new frame
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self.heatmap = np.zeros((int(self.imh), int(self.imw)), dtype=np.float32)
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self.count_txt_thickness = count_txt_thickness
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self.count_txt_color = count_txt_color
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self.count_color = count_color
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self.region_color = count_reg_color
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self.region_thickness = region_thickness
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self.decay_factor = decay_factor
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self.line_dist_thresh = line_dist_thresh
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self.shape = shape
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# shape of heatmap, if not selected
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if self.shape not in ['circle', 'rect']:
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print("Unknown shape value provided, 'circle' & 'rect' supported")
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print('Using Circular shape now')
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self.shape = 'circle'
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def extract_results(self, tracks):
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"""
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Extracts results from the provided data.
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Args:
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tracks (list): List of tracks obtained from the object tracking process.
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"""
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self.boxes = tracks[0].boxes.xyxy.cpu()
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self.clss = tracks[0].boxes.cls.cpu().tolist()
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self.track_ids = tracks[0].boxes.id.int().cpu().tolist()
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def generate_heatmap(self, im0, tracks):
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"""
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Generate heatmap based on tracking data.
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Args:
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im0 (nd array): Image
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tracks (list): List of tracks obtained from the object tracking process.
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"""
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self.im0 = im0
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if tracks[0].boxes.id is None:
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if self.view_img and self.env_check:
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self.display_frames()
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return
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else:
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return
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self.heatmap *= self.decay_factor # decay factor
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self.extract_results(tracks)
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self.annotator = Annotator(self.im0, self.count_txt_thickness, None)
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if self.count_reg_pts is not None:
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# Draw counting region
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self.annotator.draw_region(reg_pts=self.count_reg_pts,
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color=self.region_color,
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thickness=self.region_thickness)
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for box, cls, track_id in zip(self.boxes, self.clss, self.track_ids):
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if self.shape == 'circle':
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center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
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radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2
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y, x = np.ogrid[0:self.heatmap.shape[0], 0:self.heatmap.shape[1]]
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mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius ** 2
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self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += \
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(2 * mask[int(box[1]):int(box[3]), int(box[0]):int(box[2])])
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else:
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self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 2
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# Store tracking hist
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track_line = self.track_history[track_id]
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track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
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if len(track_line) > 30:
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track_line.pop(0)
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# Count objects
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if len(self.count_reg_pts) == 4:
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if self.counting_region.contains(Point(track_line[-1])):
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if track_id not in self.counting_list:
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self.counting_list.append(track_id)
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if box[0] < self.counting_region.centroid.x:
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self.out_counts += 1
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else:
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self.in_counts += 1
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elif len(self.count_reg_pts) == 2:
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distance = Point(track_line[-1]).distance(self.counting_region)
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if distance < self.line_dist_thresh:
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if track_id not in self.counting_list:
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self.counting_list.append(track_id)
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if box[0] < self.counting_region.centroid.x:
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self.out_counts += 1
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else:
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self.in_counts += 1
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else:
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for box, cls in zip(self.boxes, self.clss):
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if self.shape == 'circle':
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center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
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radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2
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y, x = np.ogrid[0:self.heatmap.shape[0], 0:self.heatmap.shape[1]]
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mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius ** 2
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self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += \
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(2 * mask[int(box[1]):int(box[3]), int(box[0]):int(box[2])])
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else:
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self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 2
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# Normalize, apply colormap to heatmap and combine with original image
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heatmap_normalized = cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX)
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heatmap_colored = cv2.applyColorMap(heatmap_normalized.astype(np.uint8), self.colormap)
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if self.count_reg_pts is not None:
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incount_label = 'InCount : ' + f'{self.in_counts}'
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outcount_label = 'OutCount : ' + f'{self.out_counts}'
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self.annotator.count_labels(in_count=incount_label,
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out_count=outcount_label,
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count_txt_size=self.count_txt_thickness,
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txt_color=self.count_txt_color,
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color=self.count_color)
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self.im0 = cv2.addWeighted(self.im0, 1 - self.heatmap_alpha, heatmap_colored, self.heatmap_alpha, 0)
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if self.env_check and self.view_img:
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self.display_frames()
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return self.im0
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def display_frames(self):
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"""Display frame."""
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cv2.imshow('Ultralytics Heatmap', self.im0)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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return
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if __name__ == '__main__':
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Heatmap()
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