回传数据解析，兼容v5和v10

ultralytics/solutions/heatmap.py

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+# 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):
+        """Initializes the heatmap class with default values for Visual, Image, track, count and heatmap parameters."""
+
+        # Visual information
+        self.annotator = None
+        self.view_img = False
+        self.shape = "circle"
+
+        # Image information
+        self.imw = None
+        self.imh = None
+        self.im0 = None
+        self.view_in_counts = True
+        self.view_out_counts = True
+
+        # Heatmap colormap and heatmap np array
+        self.colormap = None
+        self.heatmap = None
+        self.heatmap_alpha = 0.5
+
+        # Predict/track information
+        self.boxes = None
+        self.track_ids = None
+        self.clss = None
+        self.track_history = defaultdict(list)
+
+        # Region & Line Information
+        self.count_reg_pts = None
+        self.counting_region = None
+        self.line_dist_thresh = 15
+        self.region_thickness = 5
+        self.region_color = (255, 0, 255)
+
+        # Object Counting Information
+        self.in_counts = 0
+        self.out_counts = 0
+        self.counting_list = []
+        self.count_txt_thickness = 0
+        self.count_txt_color = (0, 0, 0)
+        self.count_color = (255, 255, 255)
+
+        # Decay factor
+        self.decay_factor = 0.99
+
+        # Check if environment support imshow
+        self.env_check = check_imshow(warn=True)
+
+    def set_args(
+        self,
+        imw,
+        imh,
+        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_thickness=2,
+        count_txt_color=(0, 0, 0),
+        count_color=(255, 255, 255),
+        count_reg_color=(255, 0, 255),
+        region_thickness=5,
+        line_dist_thresh=15,
+        decay_factor=0.99,
+        shape="circle",
+    ):
+        """
+        Configures the heatmap colormap, width, height and display parameters.
+
+        Args:
+            colormap (cv2.COLORMAP): The colormap to be set.
+            imw (int): The width of the frame.
+            imh (int): The height of the frame.
+            heatmap_alpha (float): alpha value for heatmap display
+            view_img (bool): Flag indicating frame display
+            view_in_counts (bool): Flag to control whether to display the incounts on video stream.
+            view_out_counts (bool): Flag to control whether to display the outcounts on video stream.
+            count_reg_pts (list): Object counting region points
+            count_txt_thickness (int): Text thickness for object counting display
+            count_txt_color (RGB color): count text color value
+            count_color (RGB color): count text background color value
+            count_reg_color (RGB color): Color of object counting region
+            region_thickness (int): Object counting Region thickness
+            line_dist_thresh (int): Euclidean Distance threshold for line counter
+            decay_factor (float): value for removing heatmap area after object passed
+            shape (str): Heatmap shape, rect or circle shape supported
+        """
+        self.imw = imw
+        self.imh = imh
+        self.heatmap_alpha = heatmap_alpha
+        self.view_img = view_img
+        self.view_in_counts = view_in_counts
+        self.view_out_counts = view_out_counts
+        self.colormap = colormap
+
+        # Region and line selection
+        if count_reg_pts is not None:
+            if len(count_reg_pts) == 2:
+                print("Line Counter Initiated.")
+                self.count_reg_pts = count_reg_pts
+                self.counting_region = LineString(count_reg_pts)
+
+            elif len(count_reg_pts) == 4:
+                print("Region Counter Initiated.")
+                self.count_reg_pts = count_reg_pts
+                self.counting_region = Polygon(self.count_reg_pts)
+
+            else:
+                print("Region or line points Invalid, 2 or 4 points supported")
+                print("Using Line Counter Now")
+                self.counting_region = Polygon([(20, 400), (1260, 400)])  # dummy points
+
+        # Heatmap new frame
+        self.heatmap = np.zeros((int(self.imh), int(self.imw)), dtype=np.float32)
+
+        self.count_txt_thickness = count_txt_thickness
+        self.count_txt_color = count_txt_color
+        self.count_color = count_color
+        self.region_color = count_reg_color
+        self.region_thickness = region_thickness
+        self.decay_factor = decay_factor
+        self.line_dist_thresh = line_dist_thresh
+        self.shape = shape
+
+        # 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.
+        """
+        self.boxes = tracks[0].boxes.xyxy.cpu()
+        self.clss = tracks[0].boxes.cls.cpu().tolist()
+        self.track_ids = tracks[0].boxes.id.int().cpu().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
+        if tracks[0].boxes.id is None:
+            self.heatmap = np.zeros((int(self.imh), int(self.imw)), dtype=np.float32)
+            if self.view_img and self.env_check:
+                self.display_frames()
+            return im0
+        self.heatmap *= self.decay_factor  # decay factor
+        self.extract_results(tracks)
+        self.annotator = Annotator(self.im0, self.count_txt_thickness, None)
+
+        if self.count_reg_pts is not None:
+            # Draw counting region
+            if self.view_in_counts or self.view_out_counts:
+                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):
+                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)
+
+                # Count objects
+                if len(self.count_reg_pts) == 4:
+                    if self.counting_region.contains(Point(track_line[-1])) and track_id not in self.counting_list:
+                        self.counting_list.append(track_id)
+                        if box[0] < self.counting_region.centroid.x:
+                            self.out_counts += 1
+                        else:
+                            self.in_counts += 1
+
+                elif len(self.count_reg_pts) == 2:
+                    distance = Point(track_line[-1]).distance(self.counting_region)
+                    if distance < self.line_dist_thresh and track_id not in self.counting_list:
+                        self.counting_list.append(track_id)
+                        if box[0] < self.counting_region.centroid.x:
+                            self.out_counts += 1
+                        else:
+                            self.in_counts += 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
+
+        # 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)
+
+        incount_label = f"In Count : {self.in_counts}"
+        outcount_label = f"OutCount : {self.out_counts}"
+
+        # Display counts based on user choice
+        counts_label = None
+        if not self.view_in_counts and not self.view_out_counts:
+            counts_label = None
+        elif not self.view_in_counts:
+            counts_label = outcount_label
+        elif not self.view_out_counts:
+            counts_label = incount_label
+        else:
+            counts_label = f"{incount_label} {outcount_label}"
+
+        if self.count_reg_pts is not None and counts_label is not None:
+            self.annotator.count_labels(
+                counts=counts_label,
+                count_txt_size=self.count_txt_thickness,
+                txt_color=self.count_txt_color,
+                color=self.count_color,
+            )
+
+        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__":
+    Heatmap()