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Fix mkdocs.yml raw image URLs (#14213)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
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docs/en/guides/heatmaps.md
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@ -330,3 +330,74 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
| `cv::COLORMAP_DEEPGREEN` | Deep Green color map |
These colormaps are commonly used for visualizing data with different color representations.
## FAQ
### How does Ultralytics YOLOv8 generate heatmaps and what are their benefits?
Ultralytics YOLOv8 generates heatmaps by transforming complex data into a color-coded matrix where different hues represent data intensities. Heatmaps make it easier to visualize patterns, correlations, and anomalies in the data. Warmer hues indicate higher values, while cooler tones represent lower values. The primary benefits include intuitive visualization of data distribution, efficient pattern detection, and enhanced spatial analysis for decision-making. For more details and configuration options, refer to the [Heatmap Configuration](#arguments-heatmap) section.
### Can I use Ultralytics YOLOv8 to perform object tracking and generate a heatmap simultaneously?
Yes, Ultralytics YOLOv8 supports object tracking and heatmap generation concurrently. This can be achieved through its `Heatmap` solution integrated with object tracking models. To do so, you need to initialize the heatmap object and use YOLOv8's tracking capabilities. Here's a simple example:
```python
import cv2
from ultralytics import YOLO, solutions
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
heatmap_obj = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", classes_names=model.names)
while cap.isOpened():
success, im0 = cap.read()
if not success:
break
tracks = model.track(im0, persist=True, show=False)
im0 = heatmap_obj.generate_heatmap(im0, tracks)
cv2.imshow("Heatmap", im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
cap.release()
cv2.destroyAllWindows()
```
For further guidance, check the [Tracking Mode](../modes/track.md) page.
### What makes Ultralytics YOLOv8 heatmaps different from other data visualization tools like those from OpenCV or Matplotlib?
Ultralytics YOLOv8 heatmaps are specifically designed for integration with its object detection and tracking models, providing an end-to-end solution for real-time data analysis. Unlike generic visualization tools like OpenCV or Matplotlib, YOLOv8 heatmaps are optimized for performance and automated processing, supporting features like persistent tracking, decay factor adjustment, and real-time video overlay. For more information on YOLOv8's unique features, visit the [Ultralytics YOLOv8 Introduction](https://www.ultralytics.com/blog/introducing-ultralytics-yolov8).
### How can I visualize only specific object classes in heatmaps using Ultralytics YOLOv8?
You can visualize specific object classes by specifying the desired classes in the `track()` method of the YOLO model. For instance, if you only want to visualize cars and persons (assuming their class indices are 0 and 2), you can set the `classes` parameter accordingly.
```python
import cv2
from ultralytics import YOLO, solutions
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
heatmap_obj = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, view_img=True, shape="circle", classes_names=model.names)
classes_for_heatmap = [0, 2] # Classes to visualize
while cap.isOpened():
success, im0 = cap.read()
if not success:
break
tracks = model.track(im0, persist=True, show=False, classes=classes_for_heatmap)
im0 = heatmap_obj.generate_heatmap(im0, tracks)
cv2.imshow("Heatmap", im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
cap.release()
cv2.destroyAllWindows()
```
### Why should businesses choose Ultralytics YOLOv8 for heatmap generation in data analysis?
Ultralytics YOLOv8 offers seamless integration of advanced object detection and real-time heatmap generation, making it an ideal choice for businesses looking to visualize data more effectively. The key advantages include intuitive data distribution visualization, efficient pattern detection, and enhanced spatial analysis for better decision-making. Additionally, YOLOv8's cutting-edge features such as persistent tracking, customizable colormaps, and support for various export formats make it superior to other tools like TensorFlow and OpenCV for comprehensive data analysis. Learn more about business applications at [Ultralytics Plans](https://www.ultralytics.com/plans).