pipeline.py

import json
from typing import Any, Dict, List
import tensorflow as tf
from tensorflow.keras.models import load_model
import base64
import io
import os
import numpy as np
from PIL import Image

# most of this code has been obtained from Datature's prediction script
# https://github.com/datature/resources/blob/main/scripts/bounding_box/prediction.py

# def load_model():
# 	return tf.saved_model.load('./saved_model')

# model = load_model()

class PreTrainedPipeline():
    def __init__(self, path: str):
        # load the model
        self.model = tf.saved_model.load(os.path.join(path, "saved_model"))

    def __call__(self, inputs: "Image.Image")-> List[Dict[str, Any]]:

        # # convert img to numpy array, resize and normalize to make the prediction
        # img = np.array(inputs)

        # im = tf.image.resize(img, (128, 128))
        # im = tf.cast(im, tf.float32) / 255.0
        # pred_mask = self.model.predict(im[tf.newaxis, ...])
        
        # # take the best performing class for each pixel
        # # the output of argmax looks like this [[1, 2, 0], ...]
        # pred_mask_arg = tf.argmax(pred_mask, axis=-1)

        # labels = []
        
        # # convert the prediction mask into binary masks for each class
        # binary_masks = {}
        # mask_codes = {}
        
        # # when we take tf.argmax() over pred_mask, it becomes a tensor object
        # # the shape becomes TensorShape object, looking like this TensorShape([128]) 
        # # we need to take get shape, convert to list and take the best one
        
        # rows = pred_mask_arg[0][1].get_shape().as_list()[0]
        # cols = pred_mask_arg[0][2].get_shape().as_list()[0]
        
        # for cls in range(pred_mask.shape[-1]):

        #     binary_masks[f"mask_{cls}"] = np.zeros(shape = (pred_mask.shape[1], pred_mask.shape[2])) #create masks for each class
            
        #     for row in range(rows):

        #         for col in range(cols):

        #             if pred_mask_arg[0][row][col] == cls:
                        
        #                 binary_masks[f"mask_{cls}"][row][col] = 1
        #             else:
        #                 binary_masks[f"mask_{cls}"][row][col] = 0

        #     mask = binary_masks[f"mask_{cls}"]
        #     mask *= 255
        #     img = Image.fromarray(mask.astype(np.int8), mode="L")
               
        #     # we need to make it readable for the widget
        #     with io.BytesIO() as out:
        #         img.save(out, format="PNG")
        #         png_string = out.getvalue()
        #         mask = base64.b64encode(png_string).decode("utf-8")

        #     mask_codes[f"mask_{cls}"] = mask
    

        #     # widget needs the below format, for each class we return label and mask string
        #     labels.append({
        #         "label": f"LABEL_{cls}",
        #         "mask": mask_codes[f"mask_{cls}"],
        #         "score": 1.0,
        #     })
		
		labels = [{"score":0.9509243965148926,"label":"car","box":{"xmin":142,"ymin":106,"xmax":376,"ymax":229}},
					{"score":0.9981777667999268,"label":"car","box":{"xmin":405,"ymin":146,"xmax":640,"ymax":297}},
					{"score":0.9963648915290833,"label":"car","box":{"xmin":0,"ymin":115,"xmax":61,"ymax":167}},
					{"score":0.974663257598877,"label":"car","box":{"xmin":155,"ymin":104,"xmax":290,"ymax":141}},
					{"score":0.9986898303031921,"label":"car","box":{"xmin":39,"ymin":117,"xmax":169,"ymax":188}},
					{"score":0.9998276233673096,"label":"person","box":{"xmin":172,"ymin":60,"xmax":482,"ymax":396}},
					{"score":0.9996274709701538,"label":"skateboard","box":{"xmin":265,"ymin":348,"xmax":440,"ymax":413}}]

# return labels