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306 lines
12 KiB
306 lines
12 KiB
# 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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self.names = None # Classes names |
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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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self.tf = 2 |
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self.view_in_counts = True |
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self.view_out_counts = True |
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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.count_ids = [] |
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self.class_wise_count = {} |
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self.count_txt_color = (0, 0, 0) |
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self.count_bg_color = (255, 255, 255) |
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self.cls_txtdisplay_gap = 50 |
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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( |
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self, |
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imw, |
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imh, |
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classes_names=None, |
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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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view_in_counts=True, |
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view_out_counts=True, |
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count_reg_pts=None, |
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count_txt_color=(0, 0, 0), |
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count_bg_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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line_thickness=2, |
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decay_factor=0.99, |
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shape="circle", |
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): |
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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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classes_names (dict): Classes names |
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line_thickness (int): Line thickness for bounding boxes. |
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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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view_in_counts (bool): Flag to control whether to display the incounts on video stream. |
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view_out_counts (bool): Flag to control whether to display the outcounts on video stream. |
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count_reg_pts (list): Object counting region points |
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count_txt_color (RGB color): count text color value |
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count_bg_color (RGB color): count highlighter line color |
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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.tf = line_thickness |
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self.names = classes_names |
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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.view_in_counts = view_in_counts |
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self.view_out_counts = view_out_counts |
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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(self.count_reg_pts) |
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elif len(count_reg_pts) >= 3: |
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print("Polygon 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("Invalid Region points provided, region_points must be 2 for lines or >= 3 for polygons.") |
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print("Using Line Counter Now") |
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self.counting_region = LineString(self.count_reg_pts) |
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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_color = count_txt_color |
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self.count_bg_color = count_bg_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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self.heatmap = np.zeros((int(self.imh), int(self.imw)), dtype=np.float32) |
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if self.view_img and self.env_check: |
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self.display_frames() |
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return im0 |
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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.tf, None) |
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if self.count_reg_pts is not None: |
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# Draw counting region |
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if self.view_in_counts or self.view_out_counts: |
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self.annotator.draw_region( |
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reg_pts=self.count_reg_pts, color=self.region_color, thickness=self.region_thickness |
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) |
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for box, cls, track_id in zip(self.boxes, self.clss, self.track_ids): |
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# Store class info |
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if self.names[cls] not in self.class_wise_count: |
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if len(self.names[cls]) > 5: |
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self.names[cls] = self.names[cls][:5] |
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self.class_wise_count[self.names[cls]] = {"in": 0, "out": 0} |
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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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) |
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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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prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None |
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# Count objects in any polygon |
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if len(self.count_reg_pts) >= 3: |
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is_inside = self.counting_region.contains(Point(track_line[-1])) |
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if prev_position is not None and is_inside and track_id not in self.count_ids: |
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self.count_ids.append(track_id) |
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if (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0]) > 0: |
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self.in_counts += 1 |
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self.class_wise_count[self.names[cls]]["in"] += 1 |
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else: |
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self.out_counts += 1 |
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self.class_wise_count[self.names[cls]]["out"] += 1 |
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# Count objects using line |
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elif len(self.count_reg_pts) == 2: |
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if prev_position is not None and track_id not in self.count_ids: |
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distance = Point(track_line[-1]).distance(self.counting_region) |
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if distance < self.line_dist_thresh and track_id not in self.count_ids: |
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self.count_ids.append(track_id) |
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if (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0]) > 0: |
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self.in_counts += 1 |
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self.class_wise_count[self.names[cls]]["in"] += 1 |
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else: |
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self.out_counts += 1 |
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self.class_wise_count[self.names[cls]]["out"] += 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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) |
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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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label = "Ultralytics Analytics \t" |
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for key, value in self.class_wise_count.items(): |
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if value["in"] != 0 or value["out"] != 0: |
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if not self.view_in_counts and not self.view_out_counts: |
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label = None |
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elif not self.view_in_counts: |
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label += f"{str.capitalize(key)}: IN {value['in']} \t" |
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elif not self.view_out_counts: |
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label += f"{str.capitalize(key)}: OUT {value['out']} \t" |
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else: |
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label += f"{str.capitalize(key)}: IN {value['in']} OUT {value['out']} \t" |
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label = label.rstrip() |
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label = label.split("\t") |
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if self.count_reg_pts is not None and label is not None: |
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self.annotator.display_counts( |
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counts=label, |
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count_txt_color=self.count_txt_color, |
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count_bg_color=self.count_bg_color, |
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) |
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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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