MMDetection counts detected objects

MMDetection counts detected objects

In MMDetection, you can count the number of detected objects by accessing the detection results. The detection results typically include information about the bounding boxes, class labels, and confidence scores for each detected object.

Here’s a general guide on how you can count the number of detected objects using MMDetection:

import mmcv
from mmdet.apis import inference_detector, init_detector, show_result_pyplot

# Load the configuration file and checkpoint file
config_file = 'path/to/config_file.py'
checkpoint_file = 'path/to/checkpoint_file.pth'
device = 'cuda:0'  # You can change it to 'cpu' if you want to run inference on CPU

# Initialize the model
model = init_detector(config_file, checkpoint_file, device=device)

# Load an image for inference
img = mmcv.imread('path/to/image.jpg')

# Run inference
result = inference_detector(model, img)

# Count the number of detected objects
num_detected_objects = sum([len(obj) for obj in result])

# Print the count
print(f'Number of detected objects: {num_detected_objects}')

# Visualize the result (optional)
show_result_pyplot(model, img, result)

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