YOLO-Patch-Based-Inference

This Python library simplifies SAHI-like inference for instance segmentation tasks, enabling the detection of small objects in images. It caters to both object detection and instance segmentation tasks, supporting a wide range of Ultralytics models.

The library also provides a sleek customization of the visualization of the inference results for all models, both in the standard approach (direct network run) and the unique patch-based variant.

Model Support: The library offers support for multiple ultralytics deep learning models, such as YOLOv8, YOLOv8-seg, YOLOv9, YOLOv9-seg, YOLOv10, FastSAM, and RTDETR. Users can select from pre-trained options or utilize custom-trained models to best meet their task requirements.

Explanation of how Patch-Based-Inference works:

Installation

You can install the library via pip:

pip install patched_yolo_infer

- Click here to visit the PyPI page of patched-yolo-infer.

Note: If CUDA support is available, it's recommended to pre-install PyTorch with CUDA support before installing the library. Otherwise, the CPU version will be installed by default.

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Notebooks

Interactive notebooks are provided to showcase the functionality of the library. These notebooks cover batch-inference procedures for detection, instance segmentation, inference custom visualization, and more. Each notebook is paired with a tutorial on YouTube, making it easy to learn and implement features.

Topic	Notebook	YouTube
Patch-Based-Inference Example
Example of custom visualization of usual inference results

For Russian users, there is a detailed video presentation of this project. YouTube video in Russian is available at this link.

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Examples:

Detection example:

Instance Segmentation example 1:

Instance Segmentation example 2:

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Usage

1. Patch-Based-Inference

To carry out patch-based inference of YOLO models using our library, you need to follow a sequential procedure. First, you create an instance of the MakeCropsDetectThem class, providing all desired parameters related to YOLO inference and the patch segmentation principle.
Subsequently, you pass the obtained object of this class to CombineDetections, which facilitates the consolidation of all predictions from each overlapping crop, followed by intelligent suppression of duplicates.
Upon completion, you receive the result, from which you can extract the desired outcome of frame processing.

The output obtained from the process includes several attributes that can be leveraged for further analysis or visualization:

1. img: This attribute contains the original image on which the inference was performed. It provides context for the detected objects.

2. confidences: This attribute holds the confidence scores associated with each detected object. These scores indicate the model's confidence level in the accuracy of its predictions.

3. boxes: These bounding boxes are represented as a list of lists, where each list contains four values: [x_min, y_min, x_max, y_max]. These values correspond to the coordinates of the top-left and bottom-right corners of each bounding box.

4. polygons: If available, this attribute provides a list containing NumPy arrays of polygon coordinates that represent segmentation masks corresponding to the detected objects. These polygons can be utilized to accurately outline the boundaries of each object.

5. classes_ids: This attribute contains the class IDs assigned to each detected object. These IDs correspond to specific object classes defined during the model training phase.

6. classes_names: These are the human-readable names corresponding to the class IDs. They provide semantic labels for the detected objects, making the results easier to interpret.

import cv2
from patched_yolo_infer import MakeCropsDetectThem, CombineDetections

# Load the image 
img_path = "test_image.jpg"
img = cv2.imread(img_path)

element_crops = MakeCropsDetectThem(
    image=img,
    model_path="yolov8m.pt",
    segment=False,
    shape_x=640,
    shape_y=640,
    overlap_x=50,
    overlap_y=50,
    conf=0.5,
    iou=0.7,
)
result = CombineDetections(element_crops, nms_threshold=0.25)  

# Final Results:
img=result.image
confidences=result.filtered_confidences
boxes=result.filtered_boxes
polygons=result.filtered_polygons
classes_ids=result.filtered_classes_id
classes_names=result.filtered_classes_names

Explanation of possible input arguments:

MakeCropsDetectThem Class implementing cropping and passing crops through a neural network for detection/segmentation:

Argument	Type	Default	Description
image	np.ndarray		The input image in BGR format.
model_path	str	"yolov8m.pt"	Path to the YOLO model.
model	ultralytics model	None	Pre-initialized model object. If provided, the model will be used directly instead of loading from model_path.
imgsz	int	640	Size of the input image for inference YOLO.
conf	float	0.5	Confidence threshold for detections YOLO.
iou	float	0.7	IoU threshold for non-maximum suppression YOLOv8 of single crop.
classes_list	List[int] or None	None	List of classes to filter detections. If None, all classes are considered.
segment	bool	False	Whether to perform segmentation (if the model supports it).
shape_x	int	700	Size of the crop in the x-coordinate.
shape_y	int	600	Size of the crop in the y-coordinate.
overlap_x	float	25	Percentage of overlap along the x-axis.
overlap_y	float	25	Percentage of overlap along the y-axis.
show_crops	bool	False	Whether to visualize the cropping.
resize_initial_size	bool	True	Whether to resize the results to the original input image size (ps: slow operation).
memory_optimize	bool	True	Memory optimization option for segmentation (less accurate results when enabled).
inference_extra_args	dict	None	Dictionary with extra ultralytics inference parameters (possible keys: half, device, max_det, augment, agnostic_nms and retina_masks)
batch_inference	bool	False	Batch inference of image crops through a neural network instead of sequential passes of crops (ps: faster inference, higher gpu memory use).

CombineDetections Class implementing combining masks/boxes from multiple crops + NMS (Non-Maximum Suppression):

Argument	Type	Default	Description
element_crops	MakeCropsDetectThem		Object containing crop information.
nms_threshold	float	0.3	IoU/IoS threshold for non-maximum suppression. The lower the value, the fewer objects remain after suppression.
match_metric	str	IOS	Matching metric, either 'IOU' or 'IOS'.
class_agnostic_nms	bool	True	Determines the NMS mode in object detection. When set to True, NMS operates across all classes, ignoring class distinctions and suppressing less confident bounding boxes globally. Otherwise, NMS is applied separately for each class.
intelligent_sorter	bool	True	Enable sorting by area and rounded confidence parameter. If False, sorting will be done only by confidence (usual nms).
sorter_bins	int	5	Number of bins to use for intelligent_sorter. A smaller number of bins makes the NMS more reliant on object sizes rather than confidence scores.

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2. Custom inference visualization:

Visualizes results of patch-based object detection or segmentation on an image.
Possible arguments of the visualize_results function:

Argument	Type	Default	Description
img	numpy.ndarray		The input image in BGR format.
boxes	list		A list of bounding boxes in the format [x_min, y_min, x_max, y_max].
classes_ids	list		A list of class IDs for each detection.
confidences	list	[]	A list of confidence scores corresponding to each bounding box.
classes_names	list	[]	A list of class names corresponding to the class IDs.
polygons	list	[]	A list containing NumPy arrays of polygon coordinates that represent segmentation masks.
masks	list	[]	A list of segmentation binary masks.
segment	bool	False	Whether to perform instance segmentation visualization.
show_boxes	bool	True	Whether to show bounding boxes.
show_class	bool	True	Whether to show class labels.
fill_mask	bool	False	Whether to fill the segmented regions with color.
alpha	float	0.3	The transparency of filled masks.
color_class_background	tuple	(0, 0, 255)	The background BGR color for class labels.
color_class_text	tuple	(255, 255, 255)	The text color for class labels.
thickness	int	4	The thickness of bounding box and text.
font	cv2.font	cv2.FONT_HERSHEY_SIMPLEX	The font type for class labels.
font_scale	float	1.5	The scale factor for font size.
delta_colors	int	seed=0	The random seed offset for color variation.
dpi	int	150	Final visualization size (plot is bigger when dpi is higher).
random_object_colors	bool	False	If true, colors for each object are selected randomly.
show_confidences	bool	False	If true and show_class=True, confidences near class are visualized.
axis_off	bool	True	If true, axis is turned off in the final visualization.
show_classes_list	list	[]	If empty, visualize all classes. Otherwise, visualize only classes in the list.
list_of_class_colors	list	None	A list of tuples representing the colors for each class in BGR format. If provided, these colors will be used for displaying the classes instead of random colors.
return_image_array	bool	False	If True, the function returns the image (BGR np.array) instead of displaying it.

Example of using:

from patched_yolo_infer import visualize_results

# Assuming result is an instance of the CombineDetections class
result = CombineDetections(...) 

# Visualizing the results using the visualize_results function
visualize_results(
    img=result.image,
    confidences=result.filtered_confidences,
    boxes=result.filtered_boxes,
    polygons=result.filtered_polygons,
    classes_ids=result.filtered_classes_id,
    classes_names=result.filtered_classes_names,
    segment=False,
)

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Tips for achieving the best Patch-Based-Inference results

1. Optimal Crop Size and Overlap: Ensuring high-quality results involves carefully selecting the size of crops (patches) and their overlap. It is advisable not to create an excessive number of crops, and to set the overlap between 15% to 40%.

2. Visualizing Crops: To review the crops generated, set show_crops=True during the initialization of the MakeCropsDetectThem element. This will display the number of patches and an image showing how these patches look based on your initialized parameters (shape_x, shape_y, overlap_x, and overlap_y).

3. Crop Size Considerations: The size of each crop must exceed the size of the largest object intended to be detected in the image. Otherwise, the object may not be detected.

4. Enhancing Detection Within Patches: To detect more objects within a single crop, increase the imgsz parameter and lower the confidence threshold (conf). All parameters available for configuring Ultralytics model inference are also accessible during the initialization of the MakeCropsDetectThem element.

5. Handling Duplicate Suppression Issues: If you encounter issues with duplicate suppression from overlapping patches, consider adjusting the nms_threshold and sorter_bins parameters in CombineDetections or modifying the overlap and size parameters of the patches themselves. (PS: often lowering sorter_bins to 4 or 2 can help).

6. Handling Multi-Class Detection Issues: If you are working on a multi-class detection or instance segmentation task, it may be beneficial to switch the mode to class_agnostic_nms=False in the CombineDetections parameters. The default mode, with class_agnostic_nms set to True, is particularly effective when handling a large number of closely related classes in pre-trained YOLO networks (for example, when there is often confusion between classes like car and truck). If in your scenario, an object of one class can physically be inside an object of another class, you should definitely set class_agnostic_nms=False for such cases.

7. High-Quality Instance Segmentation: For tasks requiring high-quality results in instance segmentation, detailed guidance is provided in the next section of the README.

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How to improve the quality of the algorithm for the task of instance segmentation:

In this approach, all operations under the hood are performed on binary masks of recognized objects. Storing these masks consumes a lot of memory, so this method requires more RAM and slightly more processing time. However, the accuracy of recognition significantly improves, which is especially noticeable in cases where there are many objects of different sizes and they are densely packed. Therefore, we recommend using this approach in production if accuracy is important and not speed, and if your computational resources allow storing hundreds of binary masks in RAM.

The difference in the approach to using the function lies in specifying the parameter memory_optimize=False in the MakeCropsDetectThem class. In such a case, the informative values after processing will be the following:

1. img: This attribute contains the original image on which the inference was performed. It provides context for the detected objects.

2. confidences: This attribute holds the confidence scores associated with each detected object. These scores indicate the model's confidence level in the accuracy of its predictions.

3. boxes: These bounding boxes are represented as a list of lists, where each list contains four values: [x_min, y_min, x_max, y_max]. These values correspond to the coordinates of the top-left and bottom-right corners of each bounding box.

4. masks: This attribute provides segmentation binary masks corresponding to the detected objects. These masks can be used to precisely delineate object boundaries.

5. classes_ids: This attribute contains the class IDs assigned to each detected object. These IDs correspond to specific object classes defined during the model training phase.

6. classes_names: These are the human-readable names corresponding to the class IDs. They provide semantic labels for the detected objects, making the results easier to interpret.

Here's how you can obtain them:

img=result.image
confidences=result.filtered_confidences
boxes=result.filtered_boxes
masks=result.filtered_masks
classes_ids=result.filtered_classes_id
classes_names=result.filtered_classes_names

An example of working with this mode is presented in Google Colab notebook -

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How to automatically determine optimal parameters for patches (crops):

To efficiently process a large number of images of varying sizes and contents, manually selecting the optimal patch sizes and overlaps can be difficult.. To address this, an algorithm has been developed to automatically calculate the best parameters for patches (crops).

The auto_calculate_crop_values function operates in two modes:

1. Resolution-Based Analysis: This mode evaluates the resolution of the source images to determine the optimal patch sizes and overlaps. It is faster but may not yield the highest quality results because it does not take into account the actual objects present in the images.

2. Neural Network-Based Analysis: This advanced mode employs a neural network to analyze the images. The algorithm performs a standard inference of the network on the entire image and identifies the largest detected objects. Based on the sizes of these objects, the algorithm selects patch parameters to ensure that the largest objects are fully contained within a patch, and overlapping patches ensure comprehensive coverage. In this mode, it is necessary to input the model that will be used for patch-based inference in the subsequent steps.

Possible arguments of the auto_calculate_crop_values function:

Argument	Type	Default	Description
image	np.ndarray		The input image in BGR format.
mode	str	"network_based"	The type of analysis to perform. Can be "resolution_based" for Resolution-Based Analysis or "network_based" for Neural Network-Based Analysis.
model	ultralytics model	YOLO("yolov8m.pt")	Pre-initialized model object for "network_based" mode. If not provided, the default YOLOv8m model will be used.
classes_list	list	None	A list of class indices to consider for object detection in "network_based" mode. If None, all classes will be considered.
conf	float	0.25	The confidence threshold for detection in "network_based" mode.

Example of using:

import cv2
from ultralytics import YOLO
from patched_yolo_infer import auto_calculate_crop_values

# Load the image
img_path = "test_image.jpg"
img = cv2.imread(img_path)

# Calculate the optimal crop size and overlap for an image
shape_x, shape_y, overlap_x, overlap_y = auto_calculate_crop_values(
    image=img, mode="network_based", model=YOLO("yolov8m.pt")
)

An example of working with auto_calculate_crop_values is presented in Google Colab notebook -