File size: 9,513 Bytes
f6228f9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
# 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 to create and update various types of charts (line, bar, pie, area) for visual analytics."""
def __init__(self, **kwargs):
"""Initialize the Analytics class with various chart types."""
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 = "#00F344" # 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 classwise 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 = {}
self.ax.axis("equal") if type == "pie" else None # Ensure pie chart is circular
def process_data(self, im0, frame_number):
"""
Process the image data, run object tracking.
Args:
im0 (ndarray): Input image for processing.
frame_number (int): Video frame # for plotting the data.
"""
self.extract_tracks(im0) # Extract tracks
if self.type == "line":
for box in self.boxes:
self.total_counts += 1
im0 = self.update_graph(frame_number=frame_number)
self.total_counts = 0
elif self.type == "pie" or self.type == "bar" or self.type == "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"):
"""
Update the graph (line or area) with new data for single or multiple classes.
Args:
frame_number (int): The current frame number.
count_dict (dict, optional): 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 i.e. line, bar or area.
"""
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
|