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Browse files- pipeline.py +19 -14
pipeline.py
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@@ -11,12 +11,17 @@ from PIL import Image
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# most of this code has been obtained from Datature's prediction script
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# https://github.com/datature/resources/blob/main/scripts/bounding_box/prediction.py
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# load the model
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self.model = tf.saved_model.load(os.path.join(path, "saved_model"))
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# # convert img to numpy array, resize and normalize to make the prediction
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# img = np.array(inputs)
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@@ -76,12 +81,12 @@ class PreTrainedPipeline():
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# "score": 1.0,
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# })
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# most of this code has been obtained from Datature's prediction script
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# https://github.com/datature/resources/blob/main/scripts/bounding_box/prediction.py
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def load_model():
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return tf.saved_model.load('./saved_model')
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model = load_model()
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# class PreTrainedPipeline():
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# def __init__(self, path: str):
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# # load the model
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# self.model = tf.saved_model.load(os.path.join(path, "saved_model"))
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# def __call__(self, inputs: "Image.Image")-> List[Dict[str, Any]]:
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# # convert img to numpy array, resize and normalize to make the prediction
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# img = np.array(inputs)
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# "score": 1.0,
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# })
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labels = [{"score":0.9509243965148926,"label":"car","box":{"xmin":142,"ymin":106,"xmax":376,"ymax":229}},
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{"score":0.9981777667999268,"label":"car","box":{"xmin":405,"ymin":146,"xmax":640,"ymax":297}},
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{"score":0.9963648915290833,"label":"car","box":{"xmin":0,"ymin":115,"xmax":61,"ymax":167}},
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{"score":0.974663257598877,"label":"car","box":{"xmin":155,"ymin":104,"xmax":290,"ymax":141}},
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{"score":0.9986898303031921,"label":"car","box":{"xmin":39,"ymin":117,"xmax":169,"ymax":188}},
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{"score":0.9998276233673096,"label":"person","box":{"xmin":172,"ymin":60,"xmax":482,"ymax":396}},
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{"score":0.9996274709701538,"label":"skateboard","box":{"xmin":265,"ymin":348,"xmax":440,"ymax":413}}]
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return labels
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