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Provide real-time video analysis with stream processing service. Get the most out of your visual information with the help of Nipa cloud experts. Offer fully customized algorithms to your specifications. [Learn more.](https://docs.nipa.cloud/article-categories/nvision/)\n\nNvision image processing is **synchronous**. The input requests and output responses are structured in JSON format. You can make a RESTful API call by sending the image as a base64 encoded string in the body of your request, see [RESTful and WebSocket](https://docs.nipa.cloud/knowledge-base/api-concepts/) API concepts.\n\n## Installation\n\nUsing pypi package here: https://pypi.org/project/nvision/\n\n```bash\npip install nvision\n```\n\n## Getting started\n\n```python\nimport os\nimport json\nimport base64\nfrom nvision import ObjectDetection\n\nmodel = ObjectDetection(api_key=os.getenv('API_KEY'))\n\n# base64 encoed string\nwith open(os.path.join('docs', 'street.jpg'), 'rb') as file:\n    image = file.read()\n    image = base64.b64encode(image).decode('utf-8')\n\n# make a RESTful call to the Nvision API\nresponse = model.predict(image)\n\nprint(response)\nprint(json.dumps(response.json(), indent=4, sort_keys=True))\n```\n\n## Prediction results\n\nThe API responses a list of labels with corresponding confidence scores of how likely it is these concepts are contained within the image or video.\n\nIn object detection, this service localizes and identifies multiple objects in the image such as people, animals, vehicles, furniture, etc. see machine learning services.\n\n![original photo by Ernest Ojeh on Unsplash](./docs/output.png)\n\u003e *original photo by Ernest Ojeh on Unsplash*\n\nA response is returned in JSON format similar to the following example:\n\n```json\n{\n    \"detected_objects\": [\n        {\n            \"confidence\": 0.8327999711036682,\n            \"parent\": \"accessory\",\n            \"bounding_box\": {\n                \"left\": 246,\n                \"right\": 303,\n                \"top\": 520,\n                \"bottom\": 605\n            },\n            \"name\": \"backpack\"\n        },\n        {\n            \"confidence\": 0.6195999979972839,\n            \"parent\": \"Object\",\n            \"bounding_box\": {\n                \"left\": 595,\n                \"right\": 641,\n                \"top\": 64,\n                \"bottom\": 230\n            },\n            \"name\": \"traffic light\"\n        }\n    ]\n}\n\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnipa-cloud%2Fnvision-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnipa-cloud%2Fnvision-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnipa-cloud%2Fnvision-python/lists"}