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268 lines
12 KiB
268 lines
12 KiB
--- |
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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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# 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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=== "Multiple Lines" |
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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("multiple_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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max_points=200, |
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) |
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frame_count = 0 |
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data = {} |
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labels = [] |
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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) |
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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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track_ids = results[0].boxes.id.int().cpu().tolist() |
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clss = results[0].boxes.cls.cpu().tolist() |
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for box, track_id, cls in zip(boxes, track_ids, clss): |
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# Store each class label |
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if model.names[int(cls)] not in labels: |
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labels.append(model.names[int(cls)]) |
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# Store each class count |
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if model.names[int(cls)] in data: |
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data[model.names[int(cls)]] += 1 |
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else: |
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data[model.names[int(cls)]] = 0 |
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# update lines every frame |
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analytics.update_multiple_lines(data, labels, frame_count) |
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data = {} # clear the data list for next frame |
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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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| `max_points` | `int` | `50` | For multiple lines, total points drawn on frame, before deleting initial points. | |
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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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