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import json
from typing import Any, Dict, List
import tensorflow as tf
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
class PreTrainedPipeline():
def __init__(self, path: str):
# load the model
self.model = tf.saved_model.load('./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
# class PreTrainedPipeline():
# def __init__(self, path: str):
# # load the model
# self.model = tf.saved_model.load('./saved_model')
# def __call__(self, inputs: "Image.Image")-> List[Dict[str, Any]]:
# image = np.array(inputs)
# image = tf.cast(image, tf.float32)
# image = tf.image.resize(image, [150, 150])
# image = np.expand_dims(image, axis = 0)
# predictions = self.model.predict(image)
# labels = []
# 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
# # -----------------
# def load_model():
# return tf.saved_model.load('./saved_model')
# def load_label_map(label_map_path):
# """
# Reads label map in the format of .pbtxt and parse into dictionary
# Args:
# label_map_path: the file path to the label_map
# Returns:
# dictionary with the format of {label_index: {'id': label_index, 'name': label_name}}
# """
# label_map = {}
# with open(label_map_path, "r") as label_file:
# for line in label_file:
# if "id" in line:
# label_index = int(line.split(":")[-1])
# label_name = next(label_file).split(":")[-1].strip().strip('"')
# label_map[label_index] = {"id": label_index, "name": label_name}
# return label_map
# def predict_class(image, model):
# image = tf.cast(image, tf.float32)
# image = tf.image.resize(image, [150, 150])
# image = np.expand_dims(image, axis = 0)
# return model.predict(image)
# def plot_boxes_on_img(color_map, classes, bboxes, image_origi, origi_shape):
# for idx, each_bbox in enumerate(bboxes):
# color = color_map[classes[idx]]
# ## Draw bounding box
# cv2.rectangle(
# image_origi,
# (int(each_bbox[1] * origi_shape[1]),
# int(each_bbox[0] * origi_shape[0]),),
# (int(each_bbox[3] * origi_shape[1]),
# int(each_bbox[2] * origi_shape[0]),),
# color,
# 2,
# )
# ## Draw label background
# cv2.rectangle(
# image_origi,
# (int(each_bbox[1] * origi_shape[1]),
# int(each_bbox[2] * origi_shape[0]),),
# (int(each_bbox[3] * origi_shape[1]),
# int(each_bbox[2] * origi_shape[0] + 15),),
# color,
# -1,
# )
# ## Insert label class & score
# cv2.putText(
# image_origi,
# "Class: {}, Score: {}".format(
# str(category_index[classes[idx]]["name"]),
# str(round(scores[idx], 2)),
# ),
# (int(each_bbox[1] * origi_shape[1]),
# int(each_bbox[2] * origi_shape[0] + 10),),
# cv2.FONT_HERSHEY_SIMPLEX,
# 0.3,
# (0, 0, 0),
# 1,
# cv2.LINE_AA,
# )
# return image_origi
# # Webpage code starts here
# #TODO change this
# st.title('Distribution Grid - Belgium - Equipment detection')
# st.text('made by LabelFlow')
# st.markdown('## Description about your project')
# with st.spinner('Model is being loaded...'):
# model = load_model()
# # ask user to upload an image
# file = st.file_uploader("Upload image", type=["jpg", "png"])
# if file is None:
# st.text('Waiting for upload...')
# else:
# st.text('Running inference...')
# # open image
# test_image = Image.open(file).convert("RGB")
# origi_shape = np.asarray(test_image).shape
# # resize image to default shape
# default_shape = 320
# image_resized = np.array(test_image.resize((default_shape, default_shape)))
# ## Load color map
# category_index = load_label_map("./label_map.pbtxt")
# # TODO Add more colors if there are more classes
# # color of each label. check label_map.pbtxt to check the index for each class
# color_map = {
# 1: [69, 109, 42],
# 2: [107, 46, 186],
# 3: [9, 35, 183],
# 4: [27, 1, 30],
# 5: [0, 0, 0],
# 6: [5, 6, 7],
# 7: [11, 5, 12],
# 8: [209, 205, 211],
# 9: [17, 17, 17],
# 10: [101, 242, 50],
# 11: [51, 204, 170],
# 12: [106, 0, 132],
# 13: [7, 111, 153],
# 14: [8, 10, 9],
# 15: [234, 250, 252],
# 16: [58, 68, 30],
# 17: [24, 178, 117],
# 18: [21, 22, 21],
# 19: [53, 104, 83],
# 20: [12, 5, 10],
# 21: [223, 192, 249],
# 22: [234, 234, 234],
# 23: [119, 68, 221],
# 24: [224, 174, 94],
# 25: [140, 74, 116],
# 26: [90, 102, 1],
# 27: [216, 143, 208]
# }
# ## The model input needs to be a tensor
# input_tensor = tf.convert_to_tensor(image_resized)
# ## The model expects a batch of images, so add an axis with `tf.newaxis`.
# input_tensor = input_tensor[tf.newaxis, ...]
# ## Feed image into model and obtain output
# detections_output = model(input_tensor)
# num_detections = int(detections_output.pop("num_detections"))
# detections = {key: value[0, :num_detections].numpy() for key, value in detections_output.items()}
# detections["num_detections"] = num_detections
# ## Filter out predictions below threshold
# # if threshold is higher, there will be fewer predictions
# # TODO change this number to see how the predictions change
# confidence_threshold = 0.6
# indexes = np.where(detections["detection_scores"] > confidence_threshold)
# ## Extract predicted bounding boxes
# bboxes = detections["detection_boxes"][indexes]
# # there are no predicted boxes
# if len(bboxes) == 0:
# st.error('No boxes predicted')
# # there are predicted boxes
# else:
# st.success('Boxes predicted')
# classes = detections["detection_classes"][indexes].astype(np.int64)
# scores = detections["detection_scores"][indexes]
# # plot boxes and labels on image
# image_origi = np.array(Image.fromarray(image_resized).resize((origi_shape[1], origi_shape[0])))
# image_origi = plot_boxes_on_img(color_map, classes, bboxes, image_origi, origi_shape)
# # show image in web page
# st.image(Image.fromarray(image_origi), caption="Image with predictions", width=400)
# st.markdown("### Predicted boxes")
# for idx in range(len((bboxes))):
# st.markdown(f"* Class: {str(category_index[classes[idx]]['name'])}, confidence score: {str(round(scores[idx], 2))}")
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