248 lines
11 KiB
Python
248 lines
11 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 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])), "constant")
|
|
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
|