Docs Solutions to Navigation Bar (#13249)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Richard Abrich <richard.abrich@gmail.com>

docs/en/guides/index.md

@@ -45,25 +45,6 @@ Here's a compilation of in-depth guides to help you master different aspects of
 - [Steps of a Computer Vision Project ](steps-of-a-cv-project.md) 🚀 NEW: Learn about the key steps involved in a computer vision project, including defining goals, selecting models, preparing data, and evaluating results.
 - [Defining A Computer Vision Project's Goals](defining-project-goals.md) 🚀 NEW: Walk through how to effectively define clear and measurable goals for your computer vision project. Learn the importance of a well-defined problem statement and how it creates a roadmap for your project.
 
-## Real-World Projects
-
-![Ultralytics Solutions Thumbnail](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/44c8b148-7a9d-43e4-b7bf-272a7ac4e636)
-
-- [Object Counting](object-counting.md) 🚀 NEW: Explore the process of real-time object counting with Ultralytics YOLOv8 and acquire the knowledge to effectively count objects in a live video stream.
-- [Object Cropping](object-cropping.md) 🚀 NEW: Explore object cropping using YOLOv8 for precise extraction of objects from images and videos.
-- [Object Blurring](object-blurring.md) 🚀 NEW: Apply object blurring with YOLOv8 for privacy protection in image and video processing.
-- [Workouts Monitoring](workouts-monitoring.md) 🚀 NEW: Discover the comprehensive approach to monitoring workouts with Ultralytics YOLOv8. Acquire the skills and insights necessary to effectively use YOLOv8 for tracking and analyzing various aspects of fitness routines in real time.
-- [Objects Counting in Regions](region-counting.md) 🚀 NEW: Explore counting objects in specific regions with Ultralytics YOLOv8 for precise and efficient object detection in varied areas.
-- [Security Alarm System](security-alarm-system.md) 🚀 NEW: Discover the process of creating a security alarm system with Ultralytics YOLOv8. This system triggers alerts upon detecting new objects in the frame. Subsequently, you can customize the code to align with your specific use case.
-- [Heatmaps](heatmaps.md) 🚀 NEW: Elevate your understanding of data with our Detection Heatmaps! These intuitive visual tools use vibrant color gradients to vividly illustrate the intensity of data values across a matrix. Essential in computer vision, heatmaps are skillfully designed to highlight areas of interest, providing an immediate, impactful way to interpret spatial information.
-- [Instance Segmentation with Object Tracking](instance-segmentation-and-tracking.md) 🚀 NEW: Explore our feature on [Object Segmentation](../tasks/segment.md) in Bounding Boxes Shape, providing a visual representation of precise object boundaries for enhanced understanding and analysis.
-- [VisionEye View Objects Mapping](vision-eye.md) 🚀 NEW: This feature aim computers to discern and focus on specific objects, much like the way the human eye observes details from a particular viewpoint.
-- [Speed Estimation](speed-estimation.md) 🚀 NEW: Speed estimation in computer vision relies on analyzing object motion through techniques like [object tracking](../modes/track.md), crucial for applications like autonomous vehicles and traffic monitoring.
-- [Distance Calculation](distance-calculation.md) 🚀 NEW: Distance calculation, which involves measuring the separation between two objects within a defined space, is a crucial aspect. In the context of Ultralytics YOLOv8, the method employed for this involves using the bounding box centroid to determine the distance associated with user-highlighted bounding boxes.
-- [Queue Management](queue-management.md) 🚀 NEW: Queue management is the practice of efficiently controlling and directing the flow of people or tasks, often through strategic planning and technology implementation, to minimize wait times and improve overall productivity.
-- [Parking Management](parking-management.md) 🚀 NEW: Parking management involves efficiently organizing and directing the flow of vehicles in parking areas, often through strategic planning and technology integration, to optimize space utilization and enhance user experience.
-- [Analytics](analytics.md) 📊 NEW: Analytics involves the systematic computational analysis of data or statistics. It is used for discovering, interpreting, and communicating significant patterns in data, and for applying data patterns towards effective decision-making. Analytics can be descriptive, predictive, or prescriptive in nature, and it is integral to data-driven strategies in various industries.
-
 ## Contribute to Our Guides
 
 We welcome contributions from the community! If you've mastered a particular aspect of Ultralytics YOLO that's not yet covered in our guides, we encourage you to share your expertise. Writing a guide is a great way to give back to the community and help us make our documentation more comprehensive and user-friendly.

docs/en/hub/index.md

@@ -6,11 +6,32 @@ keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, AI platform, object d
 
 # Ultralytics HUB
 
-<a href="https://bit.ly/ultralytics_hub" target="_blank">
-  <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image">
-</a>
+<div align="center">
+<a href="https://bit.ly/ultralytics_hub" target="_blank"><img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
+<a href="https://docs.ultralytics.com/zh/hub/">中文</a> |
+<a href="https://docs.ultralytics.com/ko/hub/">한국어</a> |
+<a href="https://docs.ultralytics.com/ja/hub/">日本語</a> |
+<a href="https://docs.ultralytics.com/ru/hub/">Русский</a> |
+<a href="https://docs.ultralytics.com/de/hub/">Deutsch</a> |
+<a href="https://docs.ultralytics.com/fr/hub/">Français</a> |
+<a href="https://docs.ultralytics.com/es/hub/">Español</a> |
+<a href="https://docs.ultralytics.com/pt/hub/">Português</a> |
+<a href="https://docs.ultralytics.com/tr/hub/">Türkçe</a> |
+<a href="https://docs.ultralytics.com/vi/hub/">Tiếng Việt</a> |
+<a href="https://docs.ultralytics.com/hi/hub/">हिन्दी</a> |
+<a href="https://docs.ultralytics.com/ar/hub/">العربية</a>
+<br>
 <br>
+
+<a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a> <a href="https://colab.research.google.com/github/ultralytics/hub/blob/main/hub.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
+</div>
+
+👋 Hello from the [Ultralytics](https://ultralytics.com/) Team! We've been working hard these last few months to launch [Ultralytics HUB](https://bit.ly/ultralytics_hub), a new web tool for training and deploying all your YOLOv5 and YOLOv8 🚀 models from one spot!
+
+We hope that the resources here will help you get the most out of HUB. Please browse the HUB <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <a href="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
+
 <div align="center">
+  <br>
   <a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
   <a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
@@ -24,17 +45,8 @@ keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, AI platform, object d
   <a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
   <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
-  <br>
-  <br>
-  <a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
-    <img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI"></a>
-  <a href="https://colab.research.google.com/github/ultralytics/hub/blob/main/hub.ipynb">
-    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
 </div>
 
-👋 Hello from the [Ultralytics](https://ultralytics.com/) Team!
-
-We've been working hard over the past few months to launch [Ultralytics HUB](https://bit.ly/ultralytics_hub), a new platform for training, monitoring, and deploying all your YOLOv5 and YOLOv8 🚀 models from one spot!
 
 ## Introduction
 

docs/en/hub/inference-api.md

@@ -69,13 +69,13 @@ curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
 
 See the table below for a full list of available inference arguments.
 
-| Argument     | Default | Type    | Description                            |
-|--------------|---------|---------|----------------------------------------|
-| `image`      |         | `image` | image file                             |
-| `url`        |         | `str`   | URL of the image if not passing a file |
-| `size`       | `640`   | `int`   | valid range `32` - `1280` pixels       |
-| `confidence` | `0.25`  | `float` | valid range `0.01` - `1.0`             |
-| `iou`        | `0.45`  | `float` | valid range `0.0` - `0.95`             |
+| Argument     | Default | Type    | Description                                                          |
+|--------------|---------|---------|----------------------------------------------------------------------|
+| `image`      |         | `image` | Image file to be used for inference.                                 |
+| `url`        |         | `str`   | URL of the image if not passing a file.                              |
+| `size`       | `640`   | `int`   | Size of the input image, valid range is `32` - `1280` pixels.        |
+| `confidence` | `0.25`  | `float` | Confidence threshold for predictions, valid range `0.01` - `1.0`.    |
+| `iou`        | `0.45`  | `float` | Intersection over Union (IoU) threshold, valid range `0.0` - `0.95`. |
 
 ## Response
 

docs/en/solutions/index.md

@@ -0,0 +1,38 @@
+---
+comments: true
+description: Discover real-world applications and solutions using Ultralytics' YOLO models. Learn how to implement object counting, cropping, blurring, workout monitoring, security systems, and more with YOLOv8.
+keywords: Ultralytics, YOLO, Object Detection, Real-World Projects, Object Counting, Object Cropping, Object Blurring, Workout Monitoring, Security Systems, Heatmaps, Instance Segmentation, Object Tracking, VisionEye, Speed Estimation, Distance Calculation, Queue Management, Parking Management, Analytics
+---
+
+# Ultralytics Solutions: Harness YOLOv8 to Solve Real-World Problems
+
+Ultralytics Solutions provide cutting-edge applications of YOLO models, offering real-world solutions like object counting, blurring, and security systems, enhancing efficiency and accuracy in diverse industries. Discover the power of YOLOv8 for practical, impactful implementations.
+
+![Ultralytics Solutions Thumbnail](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/44c8b148-7a9d-43e4-b7bf-272a7ac4e636)
+
+## Solutions
+
+Here's our curated list of Ultralytics solutions that can be used to create awesome computer vision projects.
+
+- [Object Counting](../guides/object-counting.md) 🚀 NEW: Learn to perform real-time object counting with YOLOv8. Gain the expertise to accurately count objects in live video streams.
+- [Object Cropping](../guides/object-cropping.md) 🚀 NEW: Master object cropping with YOLOv8 for precise extraction of objects from images and videos.
+- [Object Blurring](../guides/object-blurring.md) 🚀 NEW: Apply object blurring using YOLOv8 to protect privacy in image and video processing.
+- [Workouts Monitoring](../guides/workouts-monitoring.md) 🚀 NEW: Discover how to monitor workouts using YOLOv8. Learn to track and analyze various fitness routines in real time.
+- [Objects Counting in Regions](../guides/region-counting.md) 🚀 NEW: Count objects in specific regions using YOLOv8 for accurate detection in varied areas.
+- [Security Alarm System](../guides/security-alarm-system.md) 🚀 NEW: Create a security alarm system with YOLOv8 that triggers alerts upon detecting new objects. Customize the system to fit your specific needs.
+- [Heatmaps](../guides/heatmaps.md) 🚀 NEW: Utilize detection heatmaps to visualize data intensity across a matrix, providing clear insights in computer vision tasks.
+- [Instance Segmentation with Object Tracking](../guides/instance-segmentation-and-tracking.md) 🚀 NEW: Implement instance segmentation and object tracking with YOLOv8 to achieve precise object boundaries and continuous monitoring.
+- [VisionEye View Objects Mapping](../guides/vision-eye.md) 🚀 NEW: Develop systems that mimic human eye focus on specific objects, enhancing the computer's ability to discern and prioritize details.
+- [Speed Estimation](../guides/speed-estimation.md) 🚀 NEW: Estimate object speed using YOLOv8 and object tracking techniques, crucial for applications like autonomous vehicles and traffic monitoring.
+- [Distance Calculation](../guides/distance-calculation.md) 🚀 NEW: Calculate distances between objects using bounding box centroids in YOLOv8, essential for spatial analysis.
+- [Queue Management](../guides/queue-management.md) 🚀 NEW: Implement efficient queue management systems to minimize wait times and improve productivity using YOLOv8.
+- [Parking Management](../guides/parking-management.md) 🚀 NEW: Organize and direct vehicle flow in parking areas with YOLOv8, optimizing space utilization and user experience.
+- [Analytics](../guides/analytics.md) 📊 NEW: Conduct comprehensive data analysis to discover patterns and make informed decisions, leveraging YOLOv8 for descriptive, predictive, and prescriptive analytics.
+
+## Contribute to Our Solutions
+
+We welcome contributions from the community! If you've mastered a particular aspect of Ultralytics YOLO that's not yet covered in our solutions, we encourage you to share your expertise. Writing a guide is a great way to give back to the community and help us make our documentation more comprehensive and user-friendly.
+
+To get started, please read our [Contributing Guide](../help/contributing.md) for guidelines on how to open up a Pull Request (PR) 🛠️. We look forward to your contributions!
+
+Let's work together to make the Ultralytics YOLO ecosystem more robust and versatile 🙏!

mkdocs.yml

@@ -156,11 +156,13 @@ nav:
           - models/index.md
       - Datasets:
           - datasets/index.md
+      - Solutions:
+          - solutions/index.md
       - Guides:
           - guides/index.md
-      - New 🚀 Analytics: guides/analytics.md
       - Explorer:
           - datasets/explorer/index.md
+      - NEW 🚀 Analytics: guides/analytics.md # for promotion of new pages
       - Languages:
           - 🇬🇧&nbsp English: https://ultralytics.com/docs/
           - 🇨🇳&nbsp 简体中文: https://docs.ultralytics.com/zh/
@@ -184,6 +186,20 @@ nav:
           - Configuration: usage/cfg.md
           - Simple Utilities: usage/simple-utilities.md
           - Advanced Customization: usage/engine.md
+      - Modes:
+          - modes/index.md
+      - Tasks:
+          - tasks/index.md
+      - Models:
+          - models/index.md
+      - Datasets:
+          - datasets/index.md
+      - Solutions:
+          - solutions/index.md
+      - Guides:
+          - guides/index.md
+      - Explorer:
+          - datasets/explorer/index.md
   - Modes:
       - modes/index.md
       - Train: modes/train.md
@@ -217,7 +233,7 @@ nav:
       - YOLO-World (Real-Time Open-Vocabulary Object Detection): models/yolo-world.md
   - Datasets:
       - datasets/index.md
-      - NEW 🚀 Explorer:
+      - Explorer:
           - datasets/explorer/index.md
           - Explorer API: datasets/explorer/api.md
           - Explorer Dashboard: datasets/explorer/dashboard.md
@@ -269,11 +285,22 @@ nav:
           - DOTA8: datasets/obb/dota8.md
       - Multi-Object Tracking:
           - datasets/track/index.md
-  - NEW 🚀 Explorer:
-      - datasets/explorer/index.md
-      - Explorer API: datasets/explorer/api.md
-      - Explorer Dashboard Demo: datasets/explorer/dashboard.md
-      - VOC Exploration Example: datasets/explorer/explorer.ipynb
+  - NEW 🚀 Solutions:
+      - solutions/index.md
+      - NEW 🚀 Analytics: guides/analytics.md
+      - Object Counting: guides/object-counting.md
+      - Object Cropping: guides/object-cropping.md
+      - Object Blurring: guides/object-blurring.md
+      - Workouts Monitoring: guides/workouts-monitoring.md
+      - Objects Counting in Regions: guides/region-counting.md
+      - Security Alarm System: guides/security-alarm-system.md
+      - Heatmaps: guides/heatmaps.md
+      - Instance Segmentation with Object Tracking: guides/instance-segmentation-and-tracking.md
+      - VisionEye Mapping: guides/vision-eye.md
+      - Speed Estimation: guides/speed-estimation.md
+      - Distance Calculation: guides/distance-calculation.md
+      - Queue Management: guides/queue-management.md
+      - Parking Management: guides/parking-management.md
   - Guides:
       - guides/index.md
       - YOLO Common Issues: guides/yolo-common-issues.md
@@ -295,21 +322,11 @@ nav:
       - OpenVINO Latency vs Throughput modes: guides/optimizing-openvino-latency-vs-throughput-modes.md
       - Steps of a Computer Vision Project: guides/steps-of-a-cv-project.md
       - Defining A Computer Vision Project's Goals: guides/defining-project-goals.md
-      - Real-World Projects:
-          - NEW 🚀 Analytics: guides/analytics.md
-          - Object Counting: guides/object-counting.md
-          - Object Cropping: guides/object-cropping.md
-          - Object Blurring: guides/object-blurring.md
-          - Workouts Monitoring: guides/workouts-monitoring.md
-          - Objects Counting in Regions: guides/region-counting.md
-          - Security Alarm System: guides/security-alarm-system.md
-          - Heatmaps: guides/heatmaps.md
-          - Instance Segmentation with Object Tracking: guides/instance-segmentation-and-tracking.md
-          - VisionEye Mapping: guides/vision-eye.md
-          - Speed Estimation: guides/speed-estimation.md
-          - Distance Calculation: guides/distance-calculation.md
-          - Queue Management: guides/queue-management.md
-          - Parking Management: guides/parking-management.md
+      - Explorer:
+          - datasets/explorer/index.md
+          - Explorer API: datasets/explorer/api.md
+          - Explorer Dashboard Demo: datasets/explorer/dashboard.md
+          - VOC Exploration Example: datasets/explorer/explorer.ipynb
       - YOLOv5:
           - yolov5/index.md
           - Quickstart: yolov5/quickstart_tutorial.md

tests/test_cli.py

@@ -3,6 +3,7 @@
 import subprocess
 
 import pytest
+from PIL import Image
 
 from tests import CUDA_DEVICE_COUNT, CUDA_IS_AVAILABLE
 from ultralytics.cfg import TASK2DATA, TASK2MODEL, TASKS
@@ -74,26 +75,27 @@ def test_fastsam(task="segment", model=WEIGHTS_DIR / "FastSAM-s.pt", data="coco8
     sam_model = FastSAM(model)  # or FastSAM-x.pt
 
     # Run inference on an image
-    everything_results = sam_model(source, device="cpu", retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
+    for s in (source, Image.open(source)):
+        everything_results = sam_model(s, device="cpu", retina_masks=True, imgsz=320, conf=0.4, iou=0.9)
 
-    # Remove small regions
-    new_masks, _ = Predictor.remove_small_regions(everything_results[0].masks.data, min_area=20)
+        # Remove small regions
+        new_masks, _ = Predictor.remove_small_regions(everything_results[0].masks.data, min_area=20)
 
-    # Everything prompt
-    prompt_process = FastSAMPrompt(source, everything_results, device="cpu")
-    ann = prompt_process.everything_prompt()
+        # Everything prompt
+        prompt_process = FastSAMPrompt(s, everything_results, device="cpu")
+        ann = prompt_process.everything_prompt()
 
-    # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
-    ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
+        # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
+        ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
 
-    # Text prompt
-    ann = prompt_process.text_prompt(text="a photo of a dog")
+        # Text prompt
+        ann = prompt_process.text_prompt(text="a photo of a dog")
 
-    # Point prompt
-    # Points default [[0,0]] [[x1,y1],[x2,y2]]
-    # Point_label default [0] [1,0] 0:background, 1:foreground
-    ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
-    prompt_process.plot(annotations=ann, output="./")
+        # Point prompt
+        # Points default [[0,0]] [[x1,y1],[x2,y2]]
+        # Point_label default [0] [1,0] 0:background, 1:foreground
+        ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
+        prompt_process.plot(annotations=ann, output="./")
 
 
 def test_mobilesam():

ultralytics/models/fastsam/prompt.py

@@ -24,6 +24,8 @@ class FastSAMPrompt:
 
     def __init__(self, source, results, device="cuda") -> None:
         """Initializes FastSAMPrompt with given source, results and device, and assigns clip for linear assignment."""
+        if isinstance(source, (str, Path)) and os.path.isdir(source):
+            raise ValueError(f"FastSAM only accepts image paths and PIL Image sources, not directories.")
         self.device = device
         self.results = results
         self.source = source
@@ -261,8 +263,6 @@ class FastSAMPrompt:
 
     def _crop_image(self, format_results):
         """Crops an image based on provided annotation format and returns cropped images and related data."""
-        if os.path.isdir(self.source):
-            raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
         image = Image.fromarray(cv2.cvtColor(self.results[0].orig_img, cv2.COLOR_BGR2RGB))
         ori_w, ori_h = image.size
         annotations = format_results
@@ -287,8 +287,6 @@ class FastSAMPrompt:
         """Modifies the bounding box properties and calculates IoU between masks and bounding box."""
         if self.results[0].masks is not None:
             assert bbox[2] != 0 and bbox[3] != 0
-            if os.path.isdir(self.source):
-                raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
             masks = self.results[0].masks.data
             target_height, target_width = self.results[0].orig_shape
             h = masks.shape[1]
@@ -321,8 +319,6 @@ class FastSAMPrompt:
     def point_prompt(self, points, pointlabel):  # numpy
         """Adjusts points on detected masks based on user input and returns the modified results."""
         if self.results[0].masks is not None:
-            if os.path.isdir(self.source):
-                raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
             masks = self._format_results(self.results[0], 0)
             target_height, target_width = self.results[0].orig_shape
             h = masks[0]["segmentation"].shape[0]