example code

[mohammdkaraca]

          Below is an example of how you can write code to implement video stream processing and save the final processed video with the results of patch-based instance segmentation inference (as in the example from the GIF of the previous comment):

import cv2
from ultralytics import YOLO
from patched_yolo_infer import MakeCropsDetectThem, CombineDetections, visualize_results

# Load the YOLOv8 model
model = YOLO("yolov8m-seg.pt")  #or yolov8m-seg.engine in case of TensorRT

# Open the video file
cap = cv2.VideoCapture("video.mp4")

# Check if the video file was successfully opened
if not cap.isOpened():
    exit()

# Get the frames per second (fps) of the video
fps = cap.get(cv2.CAP_PROP_FPS)
# Get the width and height of the video frames
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for MP4
out = cv2.VideoWriter('output.mp4', fourcc, fps, (width, height))

while True:
    # Read a frame from the video
    ret, frame = cap.read()

    # Break the loop if there are no more frames
    if not ret:
        break

    # Detect elements in the frame using the YOLOv8 model
    element_crops = MakeCropsDetectThem(
        image=frame,
        model=model,
        segment=True,
        shape_x=640,
        shape_y=500,
        overlap_x=35,
        overlap_y=35,
        conf=0.2,
        iou=0.75,
        imgsz=640,
        resize_initial_size=True,
        show_crops=False,
        batch_inference=True,
        classes_list=[0, 1, 2, 3, 4, 5, 6]
    )

    # Combine the detections from the different crops
    result = CombineDetections(element_crops, nms_threshold=0.2, match_metric='IOS')

    # Visualize the results on the frame
    frame = 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=True,
        thickness=3,
        show_boxes=False,
        fill_mask=True,
        show_class=False,
        alpha=1,
        return_image_array=True
    )

    # Resize the frame for display
    scale = 0.5
    frame_resized = cv2.resize(frame, (-1, -1), fx=scale, fy=scale)

    # Display the frame
    cv2.imshow('video', frame_resized)

    # Write the frame to the output video file
    out.write(frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the video capture and writer objects
cap.release()
out.release()

# Close all OpenCV windows
cv2.destroyAllWindows()

Originally posted by @Koldim2001 in https://github.com/Koldim2001/YOLO-Patch-Based-Inference/issues/8#issuecomment-2200714066

[mohammdkaraca]

hi I contacted you a couple of months ago about this library but back then it was to slow to be able to use it for my case I saw you telling someone that i got faster i tried the code you gave them but it doesent work it has an error here is the error itself:

"C:\Users\Mohammad karaca\PycharmProjects\yolo_teknofest.venv\Scripts\python.exe" "C:\Users\Mohammad karaca\PycharmProjects\yolo_teknofest.venv\patched_yolo.py" Traceback (most recent call last): File "C:\Users\Mohammad karaca\PycharmProjects\yolo_teknofest.venv\patched_yolo.py", line 34, in element_crops = MakeCropsDetectThem( TypeError: init() got an unexpected keyword argument 'batch_inference'

Process finished with exit code 1

[Koldim2001]

@mohammdkaraca Good afternoon. I suppose you have an older version of the library in this python environment. I recommend updating it ->

pip install --upgrade patched_yolo_infer

[Koldim2001]

@mohammdkaraca Good afternoon. Has the library update solved your problem?