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Image_Recognition-CZ3004_SC2079_Multidisciplinary_Project-NTU_SG / app.py

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	import gradio as gr
	import torch
	from PIL import Image
	import json
	import numpy as np
	import cv2

	# Load the models
	week8_model = torch.hub.load(
	'./', 'custom', path='Weights/Week_8.pt', source='local')
	week9_model = torch.hub.load(
	'./', 'custom', path='Weights/Week_9.pt', source='local')


	def draw_own_bbox(img, x1, y1, x2, y2, label, color=(36, 255, 12), text_color=(0, 0, 0)):
	"""
	Draw bounding box on the image with text label and save both the raw and annotated image in the 'own_results' folder

	Inputs
	------
	img: numpy.ndarray - image on which the bounding box is to be drawn

	x1: int - x coordinate of the top left corner of the bounding box

	y1: int - y coordinate of the top left corner of the bounding box

	x2: int - x coordinate of the bottom right corner of the bounding box

	y2: int - y coordinate of the bottom right corner of the bounding box

	label: str - label to be written on the bounding box

	color: tuple - color of the bounding box

	text_color: tuple - color of the text label

	Returns
	-------
	None

	"""
	name_to_id = {
	"NA": 'NA',
	"Bullseye": 10,
	"One": 11,
	"Two": 12,
	"Three": 13,
	"Four": 14,
	"Five": 15,
	"Six": 16,
	"Seven": 17,
	"Eight": 18,
	"Nine": 19,
	"A": 20,
	"B": 21,
	"C": 22,
	"D": 23,
	"E": 24,
	"F": 25,
	"G": 26,
	"H": 27,
	"S": 28,
	"T": 29,
	"U": 30,
	"V": 31,
	"W": 32,
	"X": 33,
	"Y": 34,
	"Z": 35,
	"Up": 36,
	"Down": 37,
	"Right": 38,
	"Left": 39,
	"Up Arrow": 36,
	"Down Arrow": 37,
	"Right Arrow": 38,
	"Left Arrow": 39,
	"Stop": 40
	}
	# Reformat the label to {label name}-{label id}
	label = label + "-" + str(name_to_id[label])
	# Convert the coordinates to int
	x1 = int(x1)
	x2 = int(x2)
	y1 = int(y1)
	y2 = int(y2)
	# Draw the bounding box
	img = cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
	# For the text background, find space required by the text so that we can put a background with that amount of width.
	(w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1)
	# Print the text
	img = cv2.rectangle(img, (x1, y1 - 20), (x1 + w, y1), color, -1)
	img = cv2.putText(img, label, (x1, y1 - 5),
	cv2.FONT_HERSHEY_SIMPLEX, 0.6, text_color, 1)
	return img


	def yolo(img, model, toggles, signal):
	"""
	Run YOLOv5 on the image and return the results

	Inputs
	------
	img: numpy.ndarray - image on which the YOLOv5 model is to be run
	model: str - name of the model to be used
	toggles: dict - dictionary containing the toggles for the model
	signal: str - signal for position heuristic

	Returns
	-------
	output_image: PIL.Image - image with bounding boxes drawn on it
	original_results: json - json containing the original results
	filtered_image: PIL.Image - image with bounding boxes drawn on it after filtering
	filtered_results: json - json containing the filtered results
	"""
	# Load the model based on the model name
	if model == "Week 8":
	model = week8_model
	else:
	model = week9_model

	# Run the model on the image
	results = model(img)

	# Original output image and results
	original_results = json.loads(
	results.pandas().xyxy[0].to_json(orient="records"))
	output_image = Image.fromarray(results.render()[0])

	# Convert the results to a pandas dataframe and calculate the height and width of the bounding box and the area of the bounding box
	df_results = results.pandas().xyxy[0]
	df_results['bboxHt'] = df_results['ymax'] - df_results['ymin']
	df_results['bboxWt'] = df_results['xmax'] - df_results['xmin']
	df_results['bboxArea'] = df_results['bboxHt'] * df_results['bboxWt']

	# Label with largest bbox height will be last
	df_results = df_results.sort_values('bboxArea', ascending=False)

	# Filter out Bullseye
	pred_list = df_results
	if 'Ignore Bullseye' in toggles:
	pred_list = pred_list[pred_list['name'] != 'Bullseye']

	# If no predictions, return the empty results
	if len(pred_list) == 0:
	return [output_image, original_results, output_image, original_results]
	# If only one prediction, no need to filter
	elif len(pred_list) == 1:
	pred = pred_list.iloc[0]
	# If more than one prediction, filter the predictions
	else:
	pred_shortlist = []
	current_area = pred_list.iloc[0]['bboxArea']

	# For each prediction, check if the confidence is greater than 0.5 and if the area is greater than 80% of the current area or 60% if the prediction is 'One'
	for _, row in pred_list.iterrows():
	if row['confidence'] > 0.5 and ((current_area * 0.8 <= row['bboxArea']) or (row['name'] == 'One' and current_area * 0.6 <= row['bboxArea'])):
	# Add the prediction to the shortlist
	pred_shortlist.append(row)
	# Update the current area to the area of the prediction
	current_area = row['bboxArea']

	# If only 1 prediction remains after filtering by confidence and area
	if len(pred_shortlist) == 1:
	# Choose that prediction
	pred = pred_shortlist[0]

	# If multiple predictions remain after filtering by confidence and area
	else:
	# Use signal of {signal} to filter further

	# Sort the predictions by xmin
	pred_shortlist.sort(key=lambda x: x['xmin'])

	# If signal is 'L', choose the first prediction in the list, i.e. leftmost in the image
	if signal == 'L':
	pred = pred_shortlist[0]

	# If signal is 'R', choose the last prediction in the list, i.e. rightmost in the image
	elif signal == 'R':
	pred = pred_shortlist[-1]

	# If signal is 'C', choose the prediction that is central in the image
	else:
	# Loop through the predictions shortlist
	for i in range(len(pred_shortlist)):
	# If the xmin of the prediction is between 250 and 774, i.e. the center of the image, choose that prediction
	if pred_shortlist[i]['xmin'] > 250 and pred_shortlist[i]['xmin'] < 774:
	pred = pred_shortlist[i]
	break

	# If no prediction is central, choose the one with the largest area
	if isinstance(pred, str):
	# Choosing one with largest area if none are central
	pred_shortlist.sort(key=lambda x: x['bboxArea'])
	pred = pred_shortlist[-1]
	# Draw the bounding box on the image
	filtered_img = draw_own_bbox(np.array(
	img), pred['xmin'], pred['ymin'], pred['xmax'], pred['ymax'], pred['name'])
	return [output_image, original_results, filtered_img, json.loads(pred.to_json(orient="records"))]

	# Define the interface
	inputs = [gr.inputs.Image(type='pil', label="Original Image"),
	gr.inputs.Radio(['Week 8', 'Week 9'], type="value",
	default='Week 8', label='Model Selection'),
	gr.CheckboxGroup(["Ignore Bullseye", "Biggest BBox Only and Position-Based Heuristics",], value=[
	"Ignore Bullseye", "Biggest BBox Only and Position-Based Heuristics"], label="Heuristic Toggles"),
	gr.inputs.Radio(['Left', 'Center', 'Right', 'Disabled'],
	type="value", default='Center', label='Position Heuristic'),
	]
	outputs = [gr.outputs.Image(type="pil", label="Output Image"),
	gr.outputs.JSON(label="Output JSON"),
	gr.outputs.Image(type="pil", label="Filtered Output Image"),
	gr.outputs.JSON(label="Filtered Output JSON")
	]
	# Define the examples
	examples = [['Examples/One.jpg'], ['Examples/Two.jpg'], ['Examples/Three.jpg'], ['Examples/1.jpg'], ['Examples/2.jpg'], ['Examples/3.jpg'], ['Examples/4.jpg'], ['Examples/5.jpg'], ['Examples/6.jpg'],
	['Examples/7.jpg'], ['Examples/8.jpg'], ['Examples/9.jpg'], ['Examples/10.jpg'], ['Examples/11.jpg'], ['Examples/12.jpg']]

	# Define the gradio app
	with gr.Blocks(css="#custom_header {min-height: 2rem; text-align: center} #custom_title {min-height: 2rem}") as demo:
	gr.Markdown("# YOLOv5 Symbol Recognition for CZ3004/SC2079 Multi-Disciplinary Project",
	elem_id="custom_header")
	gr.Markdown("Gradio Demo for YOLOv5 Symbol Recognition for CZ3004 Multi-Disciplinary Project. To use it, simply upload your image, or click one of the examples to load them. CZ3004 is a module in Nanyang Technological University's Computer Science curriculum that involves creating a robot car that can navigate within an arena and around obstacles. Part of the assessment is to go to obstacles and detect alphanumeric symbols pasted on them.", elem_id="custom_title")
	gr.Markdown("The two models available, Week 8 and Week 9, are for different subtasks. Week 8 model (as assessment was done in Week 8 of the school semester), \
	is able to detect all symbols seen in the first three example images below. Week 9 model is limited to just the bullseye, left and right arrow symbols. \
	Additionally, Week 9 model has been further trained on extreme edge cases where there is harsh sunlight behind the symbol/obstacle (seen in some of the examples).", elem_id="custom_title")
	gr.Markdown("Heuristics used are based on AY22-23 Semester 2's edition of MDP. These include ignoring the bullseye symbol, taking only the biggest bounding box, and filtering similar sized detections by the expected position of the symbol based on where the robot is supposed to be relative to the symbol.", elem_id="custom_title")
	gr.Markdown("This demo is part of a guide that is currently work-in-progress, for future CZ3004/SC2079 students to refer to. On a local environment, inference should be around 100ms at worst, and can be made faster with a GPU and/or conversion to a more optimized model format.", elem_id="custom_title")

	with gr.Row():
	with gr.Column():
	with gr.Box():
	gr.Markdown("## Inputs", elem_id="custom_header")
	input_image = gr.inputs.Image(
	type='pil', label="Original Image")
	btn = gr.Button(value="Submit")
	btn.style(full_width=True)
	with gr.Column():
	with gr.Box():
	gr.Markdown("## Parameters", elem_id="custom_header")
	model_selection = gr.inputs.Radio(
	['Week 8', 'Week 9'], type="value", default='Week 8', label='Model Selection')
	toggles = gr.CheckboxGroup(["Ignore Bullseye", "Biggest BBox Only and Position-Based Heuristics",], value=[
	"Ignore Bullseye", "Biggest BBox Only and Position-Based Heuristics"], label="Heuristic Toggles")
	radios = gr.inputs.Radio(['Left', 'Center', 'Right', 'Disabled'],
	type="value", default='Center', label='Position Heuristic')
	with gr.Row():
	with gr.Box():
	with gr.Column():
	gr.Markdown("## Raw Outputs", elem_id="custom_header")
	output_image = gr.outputs.Image(
	type="pil", label="Output Image")
	output_json = gr.outputs.JSON(label="Output JSON")
	with gr.Box():
	with gr.Column():
	gr.Markdown("## Filtered Outputs", elem_id="custom_header")
	filtered_image = gr.outputs.Image(
	type="pil", label="Filtered Output Image")
	filtered_json = gr.outputs.JSON(label="Filtered Output JSON")
	with gr.Row():
	gr.Examples(examples=examples,
	inputs=input_image,
	outputs=output_image,
	fn=yolo,
	cache_examples=False)
	btn.click(yolo, inputs=[input_image, model_selection, toggles, radios], outputs=[
	output_image, output_json, filtered_image, filtered_json])
	# Run the gradio app
	demo.launch(debug=True)