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PythonImportHelper-v2-Completion.json
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[
{
"label": "YOLO",
"importPath": "ultralytics",
"description": "ultralytics",
"isExtraImport": true,
"detail": "ultralytics",
"documentation": {}
},
{
"label": "YOLO",
"importPath": "ultralytics",
"description": "ultralytics",
"isExtraImport": true,
"detail": "ultralytics",
"documentation": {}
},
{
"label": "YOLO",
"importPath": "ultralytics",
"description": "ultralytics",
"isExtraImport": true,
"detail": "ultralytics",
"documentation": {}
},
{
"label": "YOLO",
"importPath": "ultralytics",
"description": "ultralytics",
"isExtraImport": true,
"detail": "ultralytics",
"documentation": {}
},
{
"label": "cv2",
"kind": 6,
"isExtraImport": true,
"importPath": "cv2",
"description": "cv2",
"detail": "cv2",
"documentation": {}
},
{
"label": "matplotlib.pyplot",
"kind": 6,
"isExtraImport": true,
"importPath": "matplotlib.pyplot",
"description": "matplotlib.pyplot",
"detail": "matplotlib.pyplot",
"documentation": {}
},
{
"label": "model",
"kind": 5,
"importPath": "ex1",
"description": "ex1",
"peekOfCode": "model = YOLO(\"yolo11n.pt\") # n, s, m, l, x versions available\n# Perform object detection on an image\nresults = model.predict(source=\"C:/Users/13/Desktop/YOLO/park.PNG\")\n# Display the results\nresults[0].show() # Show the first image results",
"detail": "ex1",
"documentation": {}
},
{
"label": "results",
"kind": 5,
"importPath": "ex1",
"description": "ex1",
"peekOfCode": "results = model.predict(source=\"C:/Users/13/Desktop/YOLO/park.PNG\")\n# Display the results\nresults[0].show() # Show the first image results",
"detail": "ex1",
"documentation": {}
},
{
"label": "model",
"kind": 5,
"importPath": "ex2",
"description": "ex2",
"peekOfCode": "model = YOLO(\"yolo11n.pt\") # Load your YOLO model\n# Step 2: Initialize webcam stream\ncap = cv2.VideoCapture(0) # Use 0 for the default webcam\nif not cap.isOpened():\n print(\"Error: Could not open webcam.\")\n exit()\nwhile True:\n # Step 3: Read a frame from the webcam\n ret, frame = cap.read()\n if not ret:",
"detail": "ex2",
"documentation": {}
},
{
"label": "cap",
"kind": 5,
"importPath": "ex2",
"description": "ex2",
"peekOfCode": "cap = cv2.VideoCapture(0) # Use 0 for the default webcam\nif not cap.isOpened():\n print(\"Error: Could not open webcam.\")\n exit()\nwhile True:\n # Step 3: Read a frame from the webcam\n ret, frame = cap.read()\n if not ret:\n print(\"Error: Could not read frame.\")\n break",
"detail": "ex2",
"documentation": {}
},
{
"label": "model",
"kind": 5,
"importPath": "ex3",
"description": "ex3",
"peekOfCode": "model = YOLO(\"yolo11n.pt\")\n# Kamera ochish\ncap = cv2.VideoCapture(0)\nif not cap.isOpened():\n print(\"Error: Could not open webcam.\")\n exit()\nwhile True:\n ret, frame = cap.read()\n if not ret:\n print(\"Error: Could not read frame.\")",
"detail": "ex3",
"documentation": {}
},
{
"label": "cap",
"kind": 5,
"importPath": "ex3",
"description": "ex3",
"peekOfCode": "cap = cv2.VideoCapture(0)\nif not cap.isOpened():\n print(\"Error: Could not open webcam.\")\n exit()\nwhile True:\n ret, frame = cap.read()\n if not ret:\n print(\"Error: Could not read frame.\")\n break\n # Model orqali bashorat",
"detail": "ex3",
"documentation": {}
},
{
"label": "model",
"kind": 5,
"importPath": "ex4sig",
"description": "ex4sig",
"peekOfCode": "model = YOLO(\"yolo11n.pt\") # pretrained YOLO11n model\n# Run batched inference on a list of images\nresults = model([r\"C:\\Users\\13\\Desktop\\YOLO\\park.PNG\"]) # return a list of Results objects\n# Process results list\nfor result in results:\n boxes = result.boxes # Boxes object for bounding box outputs\n masks = result.masks # Masks object for segmentation masks outputs\n keypoints = result.keypoints # Keypoints object for pose outputs\n probs = result.probs # Probs object for classification outputs\n obb = result.obb # Oriented boxes object for OBB outputs",
"detail": "ex4sig",
"documentation": {}
},
{
"label": "results",
"kind": 5,
"importPath": "ex4sig",
"description": "ex4sig",
"peekOfCode": "results = model([r\"C:\\Users\\13\\Desktop\\YOLO\\park.PNG\"]) # return a list of Results objects\n# Process results list\nfor result in results:\n boxes = result.boxes # Boxes object for bounding box outputs\n masks = result.masks # Masks object for segmentation masks outputs\n keypoints = result.keypoints # Keypoints object for pose outputs\n probs = result.probs # Probs object for classification outputs\n obb = result.obb # Oriented boxes object for OBB outputs\n result.show() # display to screen\n result.save(filename=\"result.jpg\") # save to disk",
"detail": "ex4sig",
"documentation": {}
}
]