`ultralytics 8.2.20` new `Analytics` class with plotting visuals (#12955)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>pull/13051/head v8.2.20
Muhammad Rizwan Munawar
8 months ago
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comments: true |
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description: Comprehensive Guide to Understanding and Creating Line Graphs, Bar Plots, and Pie Charts |
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keywords: Analytics, Data Visualization, Line Graphs, Bar Plots, Pie Charts, Quickstart Guide, Data Analysis, Python, Visualization Tools |
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--- |
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|
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# Analytics using Ultralytics YOLOv8 📊 |
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## Introduction |
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This guide provides a comprehensive overview of three fundamental types of data visualizations: line graphs, bar plots, and pie charts. Each section includes step-by-step instructions and code snippets on how to create these visualizations using Python. |
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### Visual Samples |
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| Line Graph | Bar Plot | Pie Chart | |
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|:------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:| |
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|  |  |  | |
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### Why Graphs are Important |
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- Line graphs are ideal for tracking changes over short and long periods and for comparing changes for multiple groups over the same period. |
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- Bar plots, on the other hand, are suitable for comparing quantities across different categories and showing relationships between a category and its numerical value. |
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- Lastly, pie charts are effective for illustrating proportions among categories and showing parts of a whole. |
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!!! Analytics "Analytics Examples" |
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=== "Line Graph" |
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```python |
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import cv2 |
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from ultralytics import YOLO, solutions |
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model = YOLO("yolov8s.pt") |
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cap = cv2.VideoCapture("Path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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out = cv2.VideoWriter("line_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h)) |
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analytics = solutions.Analytics( |
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type="line", |
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writer=out, |
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im0_shape=(w, h), |
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view_img=True, |
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) |
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total_counts = 0 |
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frame_count = 0 |
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while cap.isOpened(): |
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success, frame = cap.read() |
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if success: |
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frame_count += 1 |
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results = model.track(frame, persist=True, verbose=True) |
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if results[0].boxes.id is not None: |
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boxes = results[0].boxes.xyxy.cpu() |
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for box in boxes: |
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total_counts += 1 |
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analytics.update_line(frame_count, total_counts) |
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total_counts = 0 |
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if cv2.waitKey(1) & 0xFF == ord("q"): |
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break |
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else: |
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break |
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cap.release() |
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out.release() |
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cv2.destroyAllWindows() |
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``` |
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=== "Pie Chart" |
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```python |
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import cv2 |
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from ultralytics import YOLO, solutions |
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model = YOLO("yolov8s.pt") |
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cap = cv2.VideoCapture("Path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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out = cv2.VideoWriter("pie_chart.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h)) |
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analytics = solutions.Analytics( |
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type="pie", |
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writer=out, |
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im0_shape=(w, h), |
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view_img=True, |
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) |
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clswise_count = {} |
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while cap.isOpened(): |
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success, frame = cap.read() |
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if success: |
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results = model.track(frame, persist=True, verbose=True) |
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if results[0].boxes.id is not None: |
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boxes = results[0].boxes.xyxy.cpu() |
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clss = results[0].boxes.cls.cpu().tolist() |
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for box, cls in zip(boxes, clss): |
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if model.names[int(cls)] in clswise_count: |
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clswise_count[model.names[int(cls)]] += 1 |
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else: |
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clswise_count[model.names[int(cls)]] = 1 |
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analytics.update_pie(clswise_count) |
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clswise_count = {} |
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if cv2.waitKey(1) & 0xFF == ord("q"): |
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break |
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else: |
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break |
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cap.release() |
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out.release() |
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cv2.destroyAllWindows() |
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``` |
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=== "Bar Plot" |
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```python |
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import cv2 |
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from ultralytics import YOLO, solutions |
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model = YOLO("yolov8s.pt") |
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cap = cv2.VideoCapture("Path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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out = cv2.VideoWriter("bar_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h)) |
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analytics = solutions.Analytics( |
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type="bar", |
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writer=out, |
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im0_shape=(w, h), |
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view_img=True, |
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) |
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clswise_count = {} |
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while cap.isOpened(): |
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success, frame = cap.read() |
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if success: |
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results = model.track(frame, persist=True, verbose=True) |
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if results[0].boxes.id is not None: |
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boxes = results[0].boxes.xyxy.cpu() |
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clss = results[0].boxes.cls.cpu().tolist() |
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for box, cls in zip(boxes, clss): |
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if model.names[int(cls)] in clswise_count: |
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clswise_count[model.names[int(cls)]] += 1 |
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else: |
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clswise_count[model.names[int(cls)]] = 1 |
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analytics.update_bar(clswise_count) |
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clswise_count = {} |
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if cv2.waitKey(1) & 0xFF == ord("q"): |
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break |
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else: |
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break |
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cap.release() |
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out.release() |
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cv2.destroyAllWindows() |
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``` |
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### Argument `Analytics` |
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Here's a table with the `Analytics` arguments: |
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| Name | Type | Default | Description | |
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|--------------|-------------------|---------------|---------------------------------------------| |
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| `type` | `str` | `None` | Type of data or object. | |
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| `im0_shape` | `tuple` | `None` | Shape of the initial image. | |
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| `writer` | `cv2.VideoWriter` | `None` | Object for writing video files. | |
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| `title` | `str` | `ultralytics` | Title for the visualization. | |
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| `x_label` | `str` | `x` | Label for the x-axis. | |
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| `y_label` | `str` | `y` | Label for the y-axis. | |
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| `bg_color` | `str` | `white` | Background color. | |
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| `fg_color` | `str` | `black` | Foreground color. | |
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| `line_color` | `str` | `yellow` | Color of the lines. | |
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| `line_width` | `int` | `2` | Width of the lines. | |
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| `fontsize` | `int` | `13` | Font size for text. | |
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| `view_img` | `bool` | `False` | Flag to display the image or video. | |
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| `save_img` | `bool` | `True` | Flag to save the image or video. | |
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### Arguments `model.track` |
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| Name | Type | Default | Description | |
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|-----------|---------|----------------|-------------------------------------------------------------| |
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| `source` | `im0` | `None` | source directory for images or videos | |
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| `persist` | `bool` | `False` | persisting tracks between frames | |
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| `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' | |
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| `conf` | `float` | `0.3` | Confidence Threshold | |
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| `iou` | `float` | `0.5` | IOU Threshold | |
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| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | |
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| `verbose` | `bool` | `True` | Display the object tracking results | |
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## Conclusion |
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Understanding when and how to use different types of visualizations is crucial for effective data analysis. Line graphs, bar plots, and pie charts are fundamental tools that can help you convey your data's story more clearly and effectively. |
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from itertools import cycle |
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import cv2 |
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import matplotlib.pyplot as plt |
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import numpy as np |
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from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas |
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from matplotlib.figure import Figure |
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class Analytics: |
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"""A class to create and update various types of charts (line, bar, pie) for visual analytics.""" |
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def __init__( |
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self, |
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type, |
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writer, |
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im0_shape, |
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title="ultralytics", |
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x_label="x", |
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y_label="y", |
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bg_color="white", |
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fg_color="black", |
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line_color="yellow", |
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line_width=2, |
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fontsize=13, |
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view_img=False, |
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save_img=True, |
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): |
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""" |
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Initialize the Analytics class with various chart types. |
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Args: |
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type (str): Type of chart to initialize ('line', 'bar', or 'pie'). |
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writer: Video writer object to save the frames. |
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im0_shape (tuple): Shape of the input image (width, height). |
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title (str): Title of the chart. |
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x_label (str): Label for the x-axis. |
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y_label (str): Label for the y-axis. |
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bg_color (str): Background color of the chart. |
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fg_color (str): Foreground (text) color of the chart. |
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line_color (str): Line color for line charts. |
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line_width (int): Width of the lines in line charts. |
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fontsize (int): Font size for chart text. |
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view_img (bool): Whether to display the image. |
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save_img (bool): Whether to save the image. |
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""" |
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self.bg_color = bg_color |
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self.fg_color = fg_color |
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self.view_img = view_img |
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self.save_img = save_img |
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self.title = title |
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self.writer = writer |
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# Set figure size based on image shape |
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figsize = (im0_shape[0] / 100, im0_shape[1] / 100) |
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if type == "line": |
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# Initialize line plot |
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fig = Figure(facecolor=self.bg_color, figsize=figsize) |
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self.canvas = FigureCanvas(fig) |
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self.ax = fig.add_subplot(111, facecolor=self.bg_color) |
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(self.line,) = self.ax.plot([], [], color=line_color, linewidth=line_width) |
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elif type == "bar" or type == "pie": |
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# Initialize bar or pie plot |
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self.fig, self.ax = plt.subplots(figsize=figsize, facecolor=self.bg_color) |
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self.ax.set_facecolor(self.bg_color) |
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color_palette = [ |
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(31, 119, 180), |
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(255, 127, 14), |
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(44, 160, 44), |
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(214, 39, 40), |
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(148, 103, 189), |
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(140, 86, 75), |
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(227, 119, 194), |
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(127, 127, 127), |
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(188, 189, 34), |
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(23, 190, 207), |
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] |
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self.color_palette = [(r / 255, g / 255, b / 255, 1) for r, g, b in color_palette] |
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self.color_cycle = cycle(self.color_palette) |
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self.color_mapping = {} |
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# Ensure pie chart is circular |
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self.ax.axis("equal") if type == "pie" else None |
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# Set common axis properties |
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self.ax.set_title(self.title, color=self.fg_color, fontsize=fontsize) |
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self.ax.set_xlabel(x_label, color=self.fg_color, fontsize=fontsize - 3) |
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self.ax.set_ylabel(y_label, color=self.fg_color, fontsize=fontsize - 3) |
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self.ax.tick_params(axis="both", colors=self.fg_color) |
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def update_line(self, frame_number, total_counts): |
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""" |
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Update the line graph with new data. |
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Args: |
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frame_number (int): The current frame number. |
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total_counts (int): The total counts to plot. |
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""" |
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# Update line graph data |
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x_data = self.line.get_xdata() |
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y_data = self.line.get_ydata() |
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x_data = np.append(x_data, float(frame_number)) |
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y_data = np.append(y_data, float(total_counts)) |
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self.line.set_data(x_data, y_data) |
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self.ax.relim() |
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self.ax.autoscale_view() |
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self.canvas.draw() |
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im0 = np.array(self.canvas.renderer.buffer_rgba()) |
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im0 = cv2.cvtColor(im0[:, :, :3], cv2.COLOR_RGBA2BGR) |
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# Display and save the updated graph |
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cv2.imshow(self.title, im0) if self.view_img else None |
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self.writer.write(im0) if self.save_img else None |
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def update_bar(self, count_dict): |
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""" |
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Update the bar graph with new data. |
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Args: |
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count_dict (dict): Dictionary containing the count data to plot. |
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""" |
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# Update bar graph data |
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self.ax.clear() |
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self.ax.set_facecolor(self.bg_color) |
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labels = list(count_dict.keys()) |
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counts = list(count_dict.values()) |
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# Map labels to colors |
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for label in labels: |
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if label not in self.color_mapping: |
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self.color_mapping[label] = next(self.color_cycle) |
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colors = [self.color_mapping[label] for label in labels] |
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bars = self.ax.bar(labels, counts, color=colors) |
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for bar, count in zip(bars, counts): |
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self.ax.text( |
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bar.get_x() + bar.get_width() / 2, |
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bar.get_height(), |
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str(count), |
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ha="center", |
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va="bottom", |
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color=self.fg_color, |
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) |
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# Display and save the updated graph |
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canvas = FigureCanvas(self.fig) |
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canvas.draw() |
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buf = canvas.buffer_rgba() |
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im0 = np.asarray(buf) |
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im0 = cv2.cvtColor(im0, cv2.COLOR_RGBA2BGR) |
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self.writer.write(im0) if self.save_img else None |
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cv2.imshow(self.title, im0) if self.view_img else None |
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def update_pie(self, classes_dict): |
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""" |
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Update the pie chart with new data. |
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Args: |
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classes_dict (dict): Dictionary containing the class data to plot. |
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""" |
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# Update pie chart data |
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labels = list(classes_dict.keys()) |
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sizes = list(classes_dict.values()) |
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total = sum(sizes) |
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percentages = [size / total * 100 for size in sizes] |
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start_angle = 90 |
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self.ax.clear() |
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# Create pie chart without labels inside the slices |
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wedges, autotexts = self.ax.pie(sizes, autopct=None, startangle=start_angle, textprops={"color": self.fg_color}) |
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# Construct legend labels with percentages |
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legend_labels = [f"{label} ({percentage:.1f}%)" for label, percentage in zip(labels, percentages)] |
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self.ax.legend(wedges, legend_labels, title="Classes", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1)) |
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# Adjust layout to fit the legend |
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self.fig.tight_layout() |
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self.fig.subplots_adjust(left=0.1, right=0.75) |
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# Display and save the updated chart |
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im0 = self.fig.canvas.draw() |
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im0 = np.array(self.fig.canvas.renderer.buffer_rgba()) |
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im0 = cv2.cvtColor(im0[:, :, :3], cv2.COLOR_RGBA2BGR) |
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self.writer.write(im0) if self.save_img else None |
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cv2.imshow(self.title, im0) if self.view_img else None |
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if __name__ == "__main__": |
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Analytics("line", writer=None, im0_shape=None) |
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