# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from itertools import cycle
import cv2
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from ultralytics.solutions.solutions import BaseSolution # Import a parent class
class Analytics(BaseSolution):
"""
A class for creating and updating various types of charts for visual analytics.
This class extends BaseSolution to provide functionality for generating line, bar, pie, and area charts
based on object detection and tracking data.
Attributes:
type (str): The type of analytics chart to generate ('line', 'bar', 'pie', or 'area').
x_label (str): Label for the x-axis.
y_label (str): Label for the y-axis.
bg_color (str): Background color of the chart frame.
fg_color (str): Foreground color of the chart frame.
title (str): Title of the chart window.
max_points (int): Maximum number of data points to display on the chart.
fontsize (int): Font size for text display.
color_cycle (cycle): Cyclic iterator for chart colors.
total_counts (int): Total count of detected objects (used for line charts).
clswise_count (Dict[str, int]): Dictionary for class-wise object counts.
fig (Figure): Matplotlib figure object for the chart.
ax (Axes): Matplotlib axes object for the chart.
canvas (FigureCanvas): Canvas for rendering the chart.
Methods:
process_data: Processes image data and updates the chart.
update_graph: Updates the chart with new data points.
Examples:
>>> analytics = Analytics(analytics_type="line")
>>> frame = cv2.imread("image.jpg")
>>> processed_frame = analytics.process_data(frame, frame_number=1)
>>> cv2.imshow("Analytics", processed_frame)
"""
def __init__(self, **kwargs):
"""Initialize Analytics class with various chart types for visual data representation."""
super().__init__(**kwargs)
self.type = self.CFG["analytics_type"] # extract type of analytics
self.x_label = "Classes" if self.type in {"bar", "pie"} else "Frame#"
self.y_label = "Total Counts"
# Predefined data
self.bg_color = "#F3F3F3" # background color of frame
self.fg_color = "#111E68" # foreground color of frame
self.title = "Ultralytics Solutions" # window name
self.max_points = 45 # maximum points to be drawn on window
self.fontsize = 25 # text font size for display
figsize = (19.2, 10.8) # Set output image size 1920 * 1080
self.color_cycle = cycle(["#DD00BA", "#042AFF", "#FF4447", "#7D24FF", "#BD00FF"])
self.total_counts = 0 # count variable for storing total counts i.e. for line
self.clswise_count = {} # dictionary for class-wise counts
# Ensure line and area chart
if self.type in {"line", "area"}:
self.lines = {}
self.fig = Figure(facecolor=self.bg_color, figsize=figsize)
self.canvas = FigureCanvas(self.fig) # Set common axis properties
self.ax = self.fig.add_subplot(111, facecolor=self.bg_color)
if self.type == "line":
(self.line,) = self.ax.plot([], [], color="cyan", linewidth=self.line_width)
elif self.type in {"bar", "pie"}:
# Initialize bar or pie plot
self.fig, self.ax = plt.subplots(figsize=figsize, facecolor=self.bg_color)
self.canvas = FigureCanvas(self.fig) # Set common axis properties
self.ax.set_facecolor(self.bg_color)
self.color_mapping = {}
if self.type == "pie": # Ensure pie chart is circular
self.ax.axis("equal")
def process_data(self, im0, frame_number):
"""
Processes image data and runs object tracking to update analytics charts.
Args:
im0 (np.ndarray): Input image for processing.
frame_number (int): Video frame number for plotting the data.
Returns:
(np.ndarray): Processed image with updated analytics chart.
Raises:
ModuleNotFoundError: If an unsupported chart type is specified.
Examples:
>>> analytics = Analytics(analytics_type="line")
>>> frame = np.zeros((480, 640, 3), dtype=np.uint8)
>>> processed_frame = analytics.process_data(frame, frame_number=1)
"""
self.extract_tracks(im0) # Extract tracks
if self.type == "line":
for _ in self.boxes:
self.total_counts += 1
im0 = self.update_graph(frame_number=frame_number)
self.total_counts = 0
elif self.type in {"pie", "bar", "area"}:
self.clswise_count = {}
for box, cls in zip(self.boxes, self.clss):
if self.names[int(cls)] in self.clswise_count:
self.clswise_count[self.names[int(cls)]] += 1
else:
self.clswise_count[self.names[int(cls)]] = 1
im0 = self.update_graph(frame_number=frame_number, count_dict=self.clswise_count, plot=self.type)
else:
raise ModuleNotFoundError(f"{self.type} chart is not supported ❌")
return im0
def update_graph(self, frame_number, count_dict=None, plot="line"):
"""
Updates the graph with new data for single or multiple classes.
Args:
frame_number (int): The current frame number.
count_dict (Dict[str, int] | None): Dictionary with class names as keys and counts as values for multiple
classes. If None, updates a single line graph.
plot (str): Type of the plot. Options are 'line', 'bar', 'pie', or 'area'.
Returns:
(np.ndarray): Updated image containing the graph.
Examples:
>>> analytics = Analytics()
>>> frame_number = 10
>>> count_dict = {"person": 5, "car": 3}
>>> updated_image = analytics.update_graph(frame_number, count_dict, plot="bar")
"""
if count_dict is None:
# Single line update
x_data = np.append(self.line.get_xdata(), float(frame_number))
y_data = np.append(self.line.get_ydata(), float(self.total_counts))
if len(x_data) > self.max_points:
x_data, y_data = x_data[-self.max_points :], y_data[-self.max_points :]
self.line.set_data(x_data, y_data)
self.line.set_label("Counts")
self.line.set_color("#7b0068") # Pink color
self.line.set_marker("*")
self.line.set_markersize(self.line_width * 5)
else:
labels = list(count_dict.keys())
counts = list(count_dict.values())
if plot == "area":
color_cycle = cycle(["#DD00BA", "#042AFF", "#FF4447", "#7D24FF", "#BD00FF"])
# Multiple lines or area update
x_data = self.ax.lines[0].get_xdata() if self.ax.lines else np.array([])
y_data_dict = {key: np.array([]) for key in count_dict.keys()}
if self.ax.lines:
for line, key in zip(self.ax.lines, count_dict.keys()):
y_data_dict[key] = line.get_ydata()
x_data = np.append(x_data, float(frame_number))
max_length = len(x_data)
for key in count_dict.keys():
y_data_dict[key] = np.append(y_data_dict[key], float(count_dict[key]))
if len(y_data_dict[key]) < max_length:
y_data_dict[key] = np.pad(y_data_dict[key], (0, max_length - len(y_data_dict[key])))
if len(x_data) > self.max_points:
x_data = x_data[1:]
for key in count_dict.keys():
y_data_dict[key] = y_data_dict[key][1:]
self.ax.clear()
for key, y_data in y_data_dict.items():
color = next(color_cycle)
self.ax.fill_between(x_data, y_data, color=color, alpha=0.7)
self.ax.plot(
x_data,
y_data,
color=color,
linewidth=self.line_width,
marker="o",
markersize=self.line_width * 5,
label=f"{key} Data Points",
)
if plot == "bar":
self.ax.clear() # clear bar data
for label in labels: # Map labels to colors
if label not in self.color_mapping:
self.color_mapping[label] = next(self.color_cycle)
colors = [self.color_mapping[label] for label in labels]
bars = self.ax.bar(labels, counts, color=colors)
for bar, count in zip(bars, counts):
self.ax.text(
bar.get_x() + bar.get_width() / 2,
bar.get_height(),
str(count),
ha="center",
va="bottom",
color=self.fg_color,
)
# Create the legend using labels from the bars
for bar, label in zip(bars, labels):
bar.set_label(label) # Assign label to each bar
self.ax.legend(loc="upper left", fontsize=13, facecolor=self.fg_color, edgecolor=self.fg_color)
if plot == "pie":
total = sum(counts)
percentages = [size / total * 100 for size in counts]
start_angle = 90
self.ax.clear()
# Create pie chart and create legend labels with percentages
wedges, autotexts = self.ax.pie(
counts, labels=labels, startangle=start_angle, textprops={"color": self.fg_color}, autopct=None
)
legend_labels = [f"{label} ({percentage:.1f}%)" for label, percentage in zip(labels, percentages)]
# Assign the legend using the wedges and manually created labels
self.ax.legend(wedges, legend_labels, title="Classes", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
self.fig.subplots_adjust(left=0.1, right=0.75) # Adjust layout to fit the legend
# Common plot settings
self.ax.set_facecolor("#f0f0f0") # Set to light gray or any other color you like
self.ax.set_title(self.title, color=self.fg_color, fontsize=self.fontsize)
self.ax.set_xlabel(self.x_label, color=self.fg_color, fontsize=self.fontsize - 3)
self.ax.set_ylabel(self.y_label, color=self.fg_color, fontsize=self.fontsize - 3)
# Add and format legend
legend = self.ax.legend(loc="upper left", fontsize=13, facecolor=self.bg_color, edgecolor=self.bg_color)
for text in legend.get_texts():
text.set_color(self.fg_color)
# Redraw graph, update view, capture, and display the updated plot
self.ax.relim()
self.ax.autoscale_view()
self.canvas.draw()
im0 = np.array(self.canvas.renderer.buffer_rgba())
im0 = cv2.cvtColor(im0[:, :, :3], cv2.COLOR_RGBA2BGR)
self.display_output(im0)
return im0 # Return the image