Delete app.py

app.py

@@ -1,153 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import streamlit as st
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-
-# most of this code has been obtained from Datature's prediction script
-# https://github.com/datature/resources/blob/main/scripts/bounding_box/prediction.py
-
-st.set_option('deprecation.showfileUploaderEncoding', False)
-
-@st.cache(allow_output_mutation=True)
-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('YOUR PROJECT NAME')
-st.text('made by XXX')
-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: [255, 0, 0], # bad -> red
-		2: [0, 255, 0] # good -> green
-	}
-
-	## 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 = 0.8
-	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))}")