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import json | |
from ultralytics import YOLO | |
# Load a model | |
model = YOLO('best_300.pt') # load an official model | |
# model = YOLO('path/to/best_300.pt') # load a custom model | |
# Predict with the model | |
# results = model.predict(source='pCard3', save=True, save_txt=True,project="playing_card",name="predict") | |
_boxes = [] | |
results = model.predict(source='pCard3/1.jpg', save=True, save_txt=True, project="playing_card", name="predict") | |
# results = model('https://cdn.britannica.com/23/194523-050-E6C02DBE/selection-American-playing-cards-jack-queen-ace.jpg') | |
for result in results: | |
r = result.numpy() | |
names = r.names | |
boxes = r.boxes | |
for box in boxes: | |
b = box.xywh[0].tolist() # get box coordinates in (top, left, bottom, right) format | |
c = int(box.cls[0]) | |
cf = float(box.conf[0]) | |
n = names[c] | |
_boxes.append({ | |
"label": c, | |
'name': n, | |
'probability': cf, | |
'bounding': b | |
}) | |
j = json.dumps(_boxes) | |
print(_boxes) | |