261 lines
10 KiB
Python
261 lines
10 KiB
Python
|
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||
|
|
|
||
|
|
from collections import defaultdict
|
||
|
|
|
||
|
|
import cv2
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from ultralytics.utils.checks import check_imshow, check_requirements
|
||
|
|
from ultralytics.utils.plotting import Annotator
|
||
|
|
|
||
|
|
check_requirements("shapely>=2.0.0")
|
||
|
|
|
||
|
|
from shapely.geometry import LineString, Point, Polygon
|
||
|
|
|
||
|
|
|
||
|
|
class Heatmap:
|
||
|
|
"""A class to draw heatmaps in real-time video stream based on their tracks."""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
names,
|
||
|
|
imw=0,
|
||
|
|
imh=0,
|
||
|
|
colormap=cv2.COLORMAP_JET,
|
||
|
|
heatmap_alpha=0.5,
|
||
|
|
view_img=False,
|
||
|
|
view_in_counts=True,
|
||
|
|
view_out_counts=True,
|
||
|
|
count_reg_pts=None,
|
||
|
|
count_txt_color=(0, 0, 0),
|
||
|
|
count_bg_color=(255, 255, 255),
|
||
|
|
count_reg_color=(255, 0, 255),
|
||
|
|
region_thickness=5,
|
||
|
|
line_dist_thresh=15,
|
||
|
|
line_thickness=2,
|
||
|
|
decay_factor=0.99,
|
||
|
|
shape="circle",
|
||
|
|
):
|
||
|
|
"""Initializes the heatmap class with default values for Visual, Image, track, count and heatmap parameters."""
|
||
|
|
|
||
|
|
# Visual information
|
||
|
|
self.annotator = None
|
||
|
|
self.view_img = view_img
|
||
|
|
self.shape = shape
|
||
|
|
|
||
|
|
self.initialized = False
|
||
|
|
self.names = names # Classes names
|
||
|
|
|
||
|
|
# Image information
|
||
|
|
self.imw = imw
|
||
|
|
self.imh = imh
|
||
|
|
self.im0 = None
|
||
|
|
self.tf = line_thickness
|
||
|
|
self.view_in_counts = view_in_counts
|
||
|
|
self.view_out_counts = view_out_counts
|
||
|
|
|
||
|
|
# Heatmap colormap and heatmap np array
|
||
|
|
self.colormap = colormap
|
||
|
|
self.heatmap = None
|
||
|
|
self.heatmap_alpha = heatmap_alpha
|
||
|
|
|
||
|
|
# Predict/track information
|
||
|
|
self.boxes = []
|
||
|
|
self.track_ids = []
|
||
|
|
self.clss = []
|
||
|
|
self.track_history = defaultdict(list)
|
||
|
|
|
||
|
|
# Region & Line Information
|
||
|
|
self.counting_region = None
|
||
|
|
self.line_dist_thresh = line_dist_thresh
|
||
|
|
self.region_thickness = region_thickness
|
||
|
|
self.region_color = count_reg_color
|
||
|
|
|
||
|
|
# Object Counting Information
|
||
|
|
self.in_counts = 0
|
||
|
|
self.out_counts = 0
|
||
|
|
self.count_ids = []
|
||
|
|
self.class_wise_count = {}
|
||
|
|
self.count_txt_color = count_txt_color
|
||
|
|
self.count_bg_color = count_bg_color
|
||
|
|
self.cls_txtdisplay_gap = 50
|
||
|
|
|
||
|
|
# Decay factor
|
||
|
|
self.decay_factor = decay_factor
|
||
|
|
|
||
|
|
# Check if environment supports imshow
|
||
|
|
self.env_check = check_imshow(warn=True)
|
||
|
|
|
||
|
|
# Region and line selection
|
||
|
|
self.count_reg_pts = count_reg_pts
|
||
|
|
print(self.count_reg_pts)
|
||
|
|
if self.count_reg_pts is not None:
|
||
|
|
if len(self.count_reg_pts) == 2:
|
||
|
|
print("Line Counter Initiated.")
|
||
|
|
self.counting_region = LineString(self.count_reg_pts)
|
||
|
|
elif len(self.count_reg_pts) >= 3:
|
||
|
|
print("Polygon Counter Initiated.")
|
||
|
|
self.counting_region = Polygon(self.count_reg_pts)
|
||
|
|
else:
|
||
|
|
print("Invalid Region points provided, region_points must be 2 for lines or >= 3 for polygons.")
|
||
|
|
print("Using Line Counter Now")
|
||
|
|
self.counting_region = LineString(self.count_reg_pts)
|
||
|
|
|
||
|
|
# Shape of heatmap, if not selected
|
||
|
|
if self.shape not in {"circle", "rect"}:
|
||
|
|
print("Unknown shape value provided, 'circle' & 'rect' supported")
|
||
|
|
print("Using Circular shape now")
|
||
|
|
self.shape = "circle"
|
||
|
|
|
||
|
|
def extract_results(self, tracks):
|
||
|
|
"""
|
||
|
|
Extracts results from the provided data.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
tracks (list): List of tracks obtained from the object tracking process.
|
||
|
|
"""
|
||
|
|
if tracks[0].boxes.id is not None:
|
||
|
|
self.boxes = tracks[0].boxes.xyxy.cpu()
|
||
|
|
self.clss = tracks[0].boxes.cls.tolist()
|
||
|
|
self.track_ids = tracks[0].boxes.id.int().tolist()
|
||
|
|
|
||
|
|
def generate_heatmap(self, im0, tracks):
|
||
|
|
"""
|
||
|
|
Generate heatmap based on tracking data.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
im0 (nd array): Image
|
||
|
|
tracks (list): List of tracks obtained from the object tracking process.
|
||
|
|
"""
|
||
|
|
self.im0 = im0
|
||
|
|
|
||
|
|
# Initialize heatmap only once
|
||
|
|
if not self.initialized:
|
||
|
|
self.heatmap = np.zeros((int(self.im0.shape[0]), int(self.im0.shape[1])), dtype=np.float32)
|
||
|
|
self.initialized = True
|
||
|
|
|
||
|
|
self.heatmap *= self.decay_factor # decay factor
|
||
|
|
|
||
|
|
self.extract_results(tracks)
|
||
|
|
self.annotator = Annotator(self.im0, self.tf, None)
|
||
|
|
|
||
|
|
if self.track_ids:
|
||
|
|
# Draw counting region
|
||
|
|
if self.count_reg_pts is not None:
|
||
|
|
self.annotator.draw_region(
|
||
|
|
reg_pts=self.count_reg_pts, color=self.region_color, thickness=self.region_thickness
|
||
|
|
)
|
||
|
|
|
||
|
|
for box, cls, track_id in zip(self.boxes, self.clss, self.track_ids):
|
||
|
|
# Store class info
|
||
|
|
if self.names[cls] not in self.class_wise_count:
|
||
|
|
self.class_wise_count[self.names[cls]] = {"IN": 0, "OUT": 0}
|
||
|
|
|
||
|
|
if self.shape == "circle":
|
||
|
|
center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
|
||
|
|
radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2
|
||
|
|
|
||
|
|
y, x = np.ogrid[0 : self.heatmap.shape[0], 0 : self.heatmap.shape[1]]
|
||
|
|
mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius**2
|
||
|
|
|
||
|
|
self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += (
|
||
|
|
2 * mask[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])]
|
||
|
|
)
|
||
|
|
|
||
|
|
else:
|
||
|
|
self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += 2
|
||
|
|
|
||
|
|
# Store tracking hist
|
||
|
|
track_line = self.track_history[track_id]
|
||
|
|
track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
|
||
|
|
if len(track_line) > 30:
|
||
|
|
track_line.pop(0)
|
||
|
|
|
||
|
|
prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None
|
||
|
|
|
||
|
|
if self.count_reg_pts is not None:
|
||
|
|
# Count objects in any polygon
|
||
|
|
if len(self.count_reg_pts) >= 3:
|
||
|
|
is_inside = self.counting_region.contains(Point(track_line[-1]))
|
||
|
|
|
||
|
|
if prev_position is not None and is_inside and track_id not in self.count_ids:
|
||
|
|
self.count_ids.append(track_id)
|
||
|
|
|
||
|
|
if (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0]) > 0:
|
||
|
|
self.in_counts += 1
|
||
|
|
self.class_wise_count[self.names[cls]]["IN"] += 1
|
||
|
|
else:
|
||
|
|
self.out_counts += 1
|
||
|
|
self.class_wise_count[self.names[cls]]["OUT"] += 1
|
||
|
|
|
||
|
|
# Count objects using line
|
||
|
|
elif len(self.count_reg_pts) == 2:
|
||
|
|
if prev_position is not None and track_id not in self.count_ids:
|
||
|
|
distance = Point(track_line[-1]).distance(self.counting_region)
|
||
|
|
if distance < self.line_dist_thresh and track_id not in self.count_ids:
|
||
|
|
self.count_ids.append(track_id)
|
||
|
|
|
||
|
|
if (box[0] - prev_position[0]) * (
|
||
|
|
self.counting_region.centroid.x - prev_position[0]
|
||
|
|
) > 0:
|
||
|
|
self.in_counts += 1
|
||
|
|
self.class_wise_count[self.names[cls]]["IN"] += 1
|
||
|
|
else:
|
||
|
|
self.out_counts += 1
|
||
|
|
self.class_wise_count[self.names[cls]]["OUT"] += 1
|
||
|
|
|
||
|
|
else:
|
||
|
|
for box, cls in zip(self.boxes, self.clss):
|
||
|
|
if self.shape == "circle":
|
||
|
|
center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
|
||
|
|
radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2
|
||
|
|
|
||
|
|
y, x = np.ogrid[0 : self.heatmap.shape[0], 0 : self.heatmap.shape[1]]
|
||
|
|
mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius**2
|
||
|
|
|
||
|
|
self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += (
|
||
|
|
2 * mask[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])]
|
||
|
|
)
|
||
|
|
|
||
|
|
else:
|
||
|
|
self.heatmap[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] += 2
|
||
|
|
|
||
|
|
if self.count_reg_pts is not None:
|
||
|
|
labels_dict = {}
|
||
|
|
|
||
|
|
for key, value in self.class_wise_count.items():
|
||
|
|
if value["IN"] != 0 or value["OUT"] != 0:
|
||
|
|
if not self.view_in_counts and not self.view_out_counts:
|
||
|
|
continue
|
||
|
|
elif not self.view_in_counts:
|
||
|
|
labels_dict[str.capitalize(key)] = f"OUT {value['OUT']}"
|
||
|
|
elif not self.view_out_counts:
|
||
|
|
labels_dict[str.capitalize(key)] = f"IN {value['IN']}"
|
||
|
|
else:
|
||
|
|
labels_dict[str.capitalize(key)] = f"IN {value['IN']} OUT {value['OUT']}"
|
||
|
|
|
||
|
|
if labels_dict is not None:
|
||
|
|
self.annotator.display_analytics(self.im0, labels_dict, self.count_txt_color, self.count_bg_color, 10)
|
||
|
|
|
||
|
|
# Normalize, apply colormap to heatmap and combine with original image
|
||
|
|
heatmap_normalized = cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX)
|
||
|
|
heatmap_colored = cv2.applyColorMap(heatmap_normalized.astype(np.uint8), self.colormap)
|
||
|
|
self.im0 = cv2.addWeighted(self.im0, 1 - self.heatmap_alpha, heatmap_colored, self.heatmap_alpha, 0)
|
||
|
|
|
||
|
|
if self.env_check and self.view_img:
|
||
|
|
self.display_frames()
|
||
|
|
|
||
|
|
return self.im0
|
||
|
|
|
||
|
|
def display_frames(self):
|
||
|
|
"""Display frame."""
|
||
|
|
cv2.imshow("Ultralytics Heatmap", self.im0)
|
||
|
|
|
||
|
|
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||
|
|
return
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
classes_names = {0: "person", 1: "car"} # example class names
|
||
|
|
heatmap = Heatmap(classes_names)
|