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--- |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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tags: |
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- MobileNetV2 |
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- accident-detection |
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library_name: transformers |
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--- |
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An image classification model for detecting car crashes from traffic cams. An easier to run version of Crashly is currently in development. To run this model, use the following code snippet. |
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``` |
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import numpy as np |
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from PIL import Image |
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import tensorflow as tf |
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# Load TFLite model and allocate tensors. |
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interpreter = tf.lite.Interpreter(model_path="{model_name}.tflite") |
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interpreter.allocate_tensors() |
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# Get input and output tensors. |
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input_details = interpreter.get_input_details() |
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output_details = interpreter.get_output_details() |
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input_shape = input_details[0]['shape'] |
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# Load and preprocess image |
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def load_image(image_path): |
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img = Image.open(image_path).convert('RGB') |
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img = img.resize([input_shape[1], input_shape[2]]) |
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img = np.asarray(img, dtype='float32') / 255 |
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# Return a scaled array between -1 and 1 |
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return img * 2 - 1 |
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if __name__ == "__main__": |
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input_data = load_image("/tmp/your-image-here.jpg") |
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interpreter.set_tensor(input_details[0]['index'], input_data) |
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interpreter.invoke() |
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# The function `get_tensor()` returns a copy of the tensor data. |
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# Use `tensor()` in order to get a pointer to the tensor. |
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output_data = interpreter.get_tensor(output_details[0]['index']) |
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print(output_data) |
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``` |
