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)