Example of utilizing a function to visualize basic Ultralytics model inference results and managing overlapping image crops:¶
In [ ]:
!pip install patched_yolo_infer
In [9]:
import cv2
import matplotlib.pyplot as plt
from ultralytics import YOLO
import numpy as np
from patched_yolo_infer import (
visualize_results_usual_yolo_inference,
get_crops,
)
Inference visualisation:¶
1. function visualize_results_usual_yolo_inference:¶
Visualizes the results of usual YOLOv8 or YOLOv8-seg inference on an image
Args:
img (numpy.ndarray): The input image in BGR format.
model: The object detection or segmentation model (yolov8).
imgsz (int): The input image size for the model. Default is 640.
conf (float): The confidence threshold for detection. Default is 0.5.
iou (float): The intersection over union threshold for detection. Default is 0.7.
segment (bool): Whether to perform instance segmentation. Default is False.
show_boxes (bool): Whether to show bounding boxes. Default is True.
show_class (bool): Whether to show class labels. Default is True.
fill_mask (bool): Whether to fill the segmented regions with color. Default is False.
alpha (float): The transparency of filled masks. Default is 0.3.
color_class_background (tuple): The background bgr color for class labels. Default is (0, 0, 255) (red).
color_class_text (tuple): The text color for class labels. Default is (255, 255, 255) (white).
thickness (int): The thickness of bounding box and text. Default is 4.
font: The font type for class labels. Default is cv2.FONT_HERSHEY_SIMPLEX.
font_scale (float): The scale factor for font size. Default is 1.5.
delta_colors (int): The random seed offset for color variation. Default is 0.
dpi (int): Final visualization size (plot is bigger when dpi is higher).
random_object_colors (bool): If True, colors for each object are selected randomly.
show_confidences (bool): If True and show_class=True, confidences near class are visualized.
axis_off (bool): 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.
return_image_array (bool): If True, the function returns the image (BGR np.array) instead of displaying it.
Returns:
None
YOLOv8-seg - instance segmentation vizualization example:¶
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# Load the image
img_path = '/content/image-test.jpg'
img = cv2.imread(img_path)
plt.imshow(cv2.cvtColor(img.copy(), cv2.COLOR_BGR2RGB));
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# Define the parameters
imgsz = 640
conf = 0.4
iou = 0.7
# Load the YOLOv8 model
model = YOLO("yolov8m-seg.pt") # Make sure the model file is in the correct path
visualize_results_usual_yolo_inference(
img,
model,
imgsz,
conf,
iou,
segment=True,
delta_colors=3,
thickness=8,
font_scale=2.5,
show_boxes=False,
fill_mask=True,
alpha=0.3,
random_object_colors=False,
show_confidences=True,
)
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visualize_results_usual_yolo_inference(
img,
model,
imgsz,
conf,
iou,
segment=True,
thickness=8,
font_scale=2.5,
show_boxes=True,
fill_mask=False,
alpha=0.4,
random_object_colors=True,
show_confidences=False,
)
YOLOv8 - detection vizualization example:¶
In [ ]:
# Load the YOLOv8 model
model = YOLO("yolov8m.pt") # Make sure the model file is in the correct path
visualize_results_usual_yolo_inference(
img,
model,
imgsz,
conf,
iou,
segment=False,
delta_colors=3,
thickness=9,
font_scale=2.5,
show_boxes=True,
random_object_colors=False,
show_confidences=False,
)
In [ ]:
visualize_results_usual_yolo_inference(
img,
model,
imgsz,
conf,
iou,
segment=False,
delta_colors=0,
thickness=9,
font_scale=2.5,
show_boxes=True,
random_object_colors=False,
show_confidences=True,
show_classes_list=[3,4,5,6,7]
)
Cropping with Overlapping:¶
2. function get_crops:¶
Preprocessing of the image. Generating crops with overlapping.
Args:
image_full (array): numpy array of a BGR image.
shape_x (int): size of the crop in the x-coordinate.
shape_y (int): size of the crop in the y-coordinate.
overlap_x (float, optional): Percentage of overlap along the x-axis
(how much subsequent crops borrow information from previous ones). Default is 15.
overlap_y (float, optional): Percentage of overlap along the y-axis
(how much subsequent crops borrow information from previous ones). Default is 15.
show (bool): enables the mode to display images using plt.imshow. Default is False.
save_crops (bool): enables saving generated images. Default is False.
save_folder (str): folder path to save the images. Default is "crops_folder".
start_name (str): starting name for saved images. Default is "image".
resize (bool): If True, the image is resized to fit the last crop exactly.
If False, the image is not resized. Default is False.
Returns:
data_all_crops (list): List containing cropped images.
In [ ]:
img_path = '/content/image-test.jpg'
image = cv2.imread(img_path)
data_all_crops = get_crops(
image,
shape_x=800,
shape_y=500,
overlap_x=50,
overlap_y=10,
show=True,
save_crops=True,
start_name="img",
resize=True,
)
Number of generated images: 77
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np.array(data_all_crops).shape
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(77, 500, 800, 3)
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data_all_crops = get_crops(image, shape_x=500, shape_y=500, overlap_x=0, overlap_y=0, show=True)
Number of generated images: 54
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data_all_crops = get_crops(image, shape_x=1200, shape_y=1200, overlap_x=50, overlap_y=50, show=True)
Number of generated images: 28
Exemple of one outut crop from the list:
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plt.imshow(cv2.cvtColor(data_all_crops[4].copy(), cv2.COLOR_BGR2RGB));
In [18]:
img_path = '/content/image-test.jpg'
img = cv2.imread(img_path)
YOLOv9 example¶
In [28]:
model = YOLO('yolov9c.pt')
visualize_results_usual_yolo_inference(
img,
model,
imgsz,
conf,
iou,
segment=False,
delta_colors=3,
thickness=8,
font_scale=2.0,
show_boxes=True,
random_object_colors=False,
show_confidences=False,
dpi=150
)
SAM example:¶
In [24]:
from ultralytics import FastSAM
# Define the parameters
imgsz = 1024
conf = 0.5
iou = 0.7
# Load the YOLOv8 model
model = FastSAM('FastSAM-x.pt')
visualize_results_usual_yolo_inference(
img,
model,
imgsz,
conf,
iou,
segment=True,
thickness=8,
show_boxes=False,
fill_mask=True,
alpha=0.4,
random_object_colors=True,
show_class=False,
dpi=150
)
RTDTR example:¶
In [29]:
from ultralytics import RTDETR
# Load a COCO-pretrained RT-DETR-l model
model = RTDETR('rtdetr-l.pt')
visualize_results_usual_yolo_inference(
img,
model,
imgsz,
conf,
iou,
segment=False,
delta_colors=3,
thickness=8,
font_scale=2.0,
show_boxes=True,
random_object_colors=False,
show_confidences=False,
dpi=150
)
