Update heatmaps solution (#16720)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Muhammad Rizwan Munawar
GitHub
parent
e56f9140fb
commit
4884311991
6 changed files with 139 additions and 350 deletions
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@ -1,249 +1,93 @@
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# 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.solutions.object_counter import ObjectCounter # Import object counter class
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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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class Heatmap(ObjectCounter):
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"""A class to draw heatmaps in real-time video stream based on their tracks."""
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def __init__(
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self,
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names,
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colormap=cv2.COLORMAP_JET,
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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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shape="circle",
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):
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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 = view_img
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self.shape = shape
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def __init__(self, **kwargs):
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"""Initializes function for heatmap class with default values."""
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super().__init__(**kwargs)
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self.initialized = False
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self.names = names # Classes names
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self.initialized = False # bool variable for heatmap initialization
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if self.region is not None: # check if user provided the region coordinates
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self.initialize_region()
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# Image information
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self.im0 = None
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self.tf = line_thickness
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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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# store colormap
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self.colormap = cv2.COLORMAP_PARULA if self.CFG["colormap"] is None else self.CFG["colormap"]
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# Heatmap colormap and heatmap np array
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self.colormap = colormap
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self.heatmap = None
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# Predict/track information
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self.boxes = []
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self.track_ids = []
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self.clss = []
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self.track_history = defaultdict(list)
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# Region & Line Information
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self.counting_region = None
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self.line_dist_thresh = line_dist_thresh
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self.region_thickness = region_thickness
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self.region_color = count_reg_color
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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 = count_txt_color
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self.count_bg_color = count_bg_color
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self.cls_txtdisplay_gap = 50
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# Check if environment supports imshow
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self.env_check = check_imshow(warn=True)
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# Region and line selection
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self.count_reg_pts = count_reg_pts
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print(self.count_reg_pts)
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if self.count_reg_pts is not None:
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if len(self.count_reg_pts) == 2:
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print("Line Counter Initiated.")
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self.counting_region = LineString(self.count_reg_pts)
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elif len(self.count_reg_pts) >= 3:
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print("Polygon Counter Initiated.")
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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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# 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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def heatmap_effect(self, box):
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"""
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Extracts results from the provided data.
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Efficient calculation of heatmap area and effect location for applying colormap.
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Args:
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tracks (list): List of tracks obtained from the object tracking process.
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box (list): Bounding Box coordinates data [x0, y0, x1, y1]
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"""
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if tracks[0].boxes.id is not None:
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self.boxes = tracks[0].boxes.xyxy.cpu()
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self.clss = tracks[0].boxes.cls.tolist()
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self.track_ids = tracks[0].boxes.id.int().tolist()
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x0, y0, x1, y1 = map(int, box)
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radius_squared = (min(x1 - x0, y1 - y0) // 2) ** 2
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def generate_heatmap(self, im0, tracks):
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# Create a meshgrid with region of interest (ROI) for vectorized distance calculations
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xv, yv = np.meshgrid(np.arange(x0, x1), np.arange(y0, y1))
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# Calculate squared distances from the center
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dist_squared = (xv - ((x0 + x1) // 2)) ** 2 + (yv - ((y0 + y1) // 2)) ** 2
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# Create a mask of points within the radius
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within_radius = dist_squared <= radius_squared
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# Update only the values within the bounding box in a single vectorized operation
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self.heatmap[y0:y1, x0:x1][within_radius] += 2
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def generate_heatmap(self, im0):
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"""
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Generate heatmap based on tracking data.
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Generate heatmap for each frame using Ultralytics.
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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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im0 (ndarray): Input image array for processing
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Returns:
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im0 (ndarray): Processed image for further usage
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"""
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self.im0 = im0
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self.heatmap = np.zeros_like(im0, dtype=np.float32) * 0.99 if not self.initialized else self.heatmap
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self.initialized = True # Initialize heatmap only once
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# Initialize heatmap only once
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if not self.initialized:
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self.heatmap = np.zeros((int(self.im0.shape[0]), int(self.im0.shape[1])), dtype=np.float32)
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self.initialized = True
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self.annotator = Annotator(im0, line_width=self.line_width) # Initialize annotator
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self.extract_tracks(im0) # Extract tracks
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self.heatmap *= 0.99 # decay factor
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# Iterate over bounding boxes, track ids and classes index
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for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
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# Draw bounding box and counting region
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self.heatmap_effect(box)
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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.track_ids:
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# Draw counting region
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if self.count_reg_pts is not None:
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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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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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if self.region is not None:
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self.annotator.draw_region(reg_pts=self.region, color=(104, 0, 123), thickness=self.line_width * 2)
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self.store_tracking_history(track_id, box) # Store track history
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self.store_classwise_counts(cls) # store classwise counts in dict
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# Store tracking previous position and perform object counting
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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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self.count_objects(self.track_line, box, track_id, prev_position, cls) # Perform object counting
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if self.count_reg_pts is not 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]) * (
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self.counting_region.centroid.x - prev_position[0]
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) > 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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if self.count_reg_pts is not None:
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labels_dict = {}
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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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continue
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elif not self.view_in_counts:
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labels_dict[str.capitalize(key)] = f"OUT {value['OUT']}"
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elif not self.view_out_counts:
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labels_dict[str.capitalize(key)] = f"IN {value['IN']}"
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else:
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labels_dict[str.capitalize(key)] = f"IN {value['IN']} OUT {value['OUT']}"
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if labels_dict is not None:
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self.annotator.display_analytics(self.im0, labels_dict, self.count_txt_color, self.count_bg_color, 10)
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self.display_counts(im0) if self.region is not None else None # Display the counts on the frame
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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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self.im0 = cv2.addWeighted(self.im0, 0.5, heatmap_colored, 0.5, 0)
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im0 = (
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im0
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if self.track_data.id is None
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else cv2.addWeighted(
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im0,
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0.5,
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cv2.applyColorMap(
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cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8), self.colormap
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),
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0.5,
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0,
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)
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)
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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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classes_names = {0: "person", 1: "car"} # example class names
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heatmap = Heatmap(classes_names)
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self.display_output(im0) # display output with base class function
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return im0 # return output image for more usage
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