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sys.path.append('Engineering-Clinic-Emerging-AI-Design-Interface/Interface_Dependencies')
sys.path.append('Engineering-Clinic-Emerging-AI-Design-Interface/Interface')
sys.path.append('Engineering-Clinic-Emerging-AI-Design-Interface/yolov7-main')
sys.path.append('Engineering-Clinic-Emerging-AI-Design-Interface')
sys.path.append('Interface_Dependencies')
sys.path.append('Interface')
from run_methods import run_all, correct_video
import gradio as gr # Gradio package for interface
# Gradio Interface Code
def build_detect_interface():
with gr.Blocks(title="yolov7 Interface",theme=gr.themes.Base()) as demo:
gr.Markdown(
"""
# Image & Video Interface for yolov7 Model
### Upload your own image or video and watch yolov7 try to guess what it is!
""")
# Row for for input & output settings
with gr.Row() as file_settings:
# Allows the user to toggle between the advanced and simple interfaces
skill_type = gr.Radio(label="Skill Type",info="Choose 'Advanced' if you would like to use the advanced interface, Choose 'Simple' if you would like that version",
choices=['Advanced','Simple', 'Help'],value='Simple',show_label=True,interactive=True,visible=True)
# Allows choice for uploading image or video [for all]
file_type = gr.Radio(label="File Type",info="Choose 'Image' if you are uploading an image, Choose 'Video' if you are uploading a video",
choices=['Image','Video'],value='Image',show_label=True,interactive=True,visible=True)
# Allows choice of source, from computer or webcam [for all]
source_type = gr.Radio(label="Source Type",info="Choose 'Computer' if you are uploading from your computer, Choose 'Webcam' if you would like to use your webcam",
choices=['Computer','Webcam'],value='Computer',show_label=True,interactive=True,visible=True)
# Allows choice of which convolutional layer to show (1-17) [only for images]
conv_layer = gr.Slider(label="Convolution Layer",info="Choose a whole number from 1 to 17 to see the corresponding convolutional layer",
minimum=1,maximum=17,value=1,interactive=True,step=1,show_label=True)
# Allows choice if video from webcam is streaming or uploaded [only for webcam videos]
video_stream = gr.Checkbox(label="Stream from webcam?",info="Check this box if you would like to stream from your webcam",value=False,show_label=True,interactive=True,visible=False)
# Allows choice of which smooth gradient output to show (1-3) [only for images]
output_map = gr.Slider(label="Map Output Number",info="Choose a whole number from 1 to 3 to see the corresponding attribution map",
minimum=1,maximum=3,value=1,interactive=True,step=1,show_label=True)
# Row for all inputs & outputs
with gr.Row() as inputs_outputs:
# Default input image: Visible, Upload from computer
input_im = gr.Image(source="upload",type='filepath',label="Input Image",
show_download_button=True,show_share_button=True,interactive=True,visible=False)
# Default Boxed output image: Visible
output_box_im = gr.Image(type='filepath',label="Output Image",
show_download_button=True,show_share_button=True,interactive=False,visible=False)
# Defualt Convolutional output image: Visible
output_conv_im = gr.Image(type='filepath',label="Output Convolution",
show_download_button=True,show_share_button=True,interactive=False,visible=False)
# Default Gradient output image: Visible
output_grad_im = gr.Image(type='filepath',label="Output Smooth Gradient",
show_download_button=True,show_share_button=True,interactive=False,visible=False)
# Default label output textbox: Visible
labels = gr.Textbox(label='Top Predictions:', value = "")
# Default plaus output textbox: Visible
plaus = gr.Textbox(label = "Plausibility Score:", value="")
# Default time output textbox: Visible
formatted_time = gr.Textbox(label = 'Total and Detection Time in Seconds:', value = "",scale=6)
# Default input video: Not visible, Upload from computer
input_vid = gr.Video(source="upload",label="Input Video",
show_share_button=True,interactive=True,visible=False)
# Default Boxed output video: Not visible
output_box_vid = gr.Video(label="Output Video",show_share_button=True,visible=False)
#An option to bring up a help menu
help_box = gr.Textbox(lines=5, label="Help Box", visible=False)
# List of components for clearing
clear_comp_list = [input_im, output_box_im, output_conv_im, output_grad_im, labels, plaus, formatted_time, input_vid, output_box_vid]
# Row for start, clear and demo buttons
with gr.Row() as buttons:
start_but = gr.Button(value="Start")
demo1_but = gr.Button(value="ImageDemo")
demo2_but = gr.Button(value="VideoDemo")
clear_but = gr.ClearButton(value='Clear All',components=clear_comp_list,
interactive=True,visible=True)
# Row for model settings
with gr.Row() as model_settings:
# Pixel size of the inference [Possibly useless, may remove]
inf_size = gr.Number(label='Inference Size (pixels)',value=640,precision=0,visible=False)
# Object confidence threshold
obj_conf_thr = gr.Number(label='Object Confidence Threshold',value=0.25,visible=False)
# Intersection of union threshold
iou_thr = gr.Number(label='IOU threshold for NMS',value=0.45,visible=False)
# Agnostic NMS boolean
agnostic_nms = gr.Checkbox(label='Agnostic NMS',value=True, visible=False)
# Normailze gradient boolean
norm = gr.Checkbox(label='Normalize Gradient',value=False,visible=False)
# Weights File Upload
weights = gr.File(label='Weights File',type='file',file_count='single',file_types=["pt"],value="weights/yolov7.pt",visible=False)
#Consider making this its own file
def change_file_type(file, source, is_stream, skill_type):
"""
Changes the visible components of the gradio interface
Args:
file (str): Type of the file (image or video)
source (str): If the file is uploaded or from webcam
is_stream (bool): If the video is streaming or uploaded
Returns:
Dictionary: Each component of the interface that needs to be updated.
"""
help_string = ("Variables within the interface: \n \
Simple Mode: \n \
Skill Type - The mode the interface will be set to. Simple is recommended for new users! \n \
File Type - Set the interface for image or video input \n \
Source Type - Set the input for media to computer(will allow uploads) or webcam(will use your devices webcam) \n \
stream from webcam? - Toggle the ability to livestream video from webcam \n \
Start - Run the interface with the set parameters and media \n \
ImageDemo - Run a demo to test the interface with a stock image \n \
VideoDemo - Run a demo to test the interface with a stock video \n \
ClearAll - Clear all of the input/output figures on the interface \n \
Advanced Mode: \n \
Convolution Layer - The convolutional layor extracted from the neural network. There are 17 of these to scroll between. \n \
Map Output Number - The attribution map. 1 is bounding box, 2 is object, and 3 is class. Currently, this is from the model not loss. \n \
Top Predictions - A list of the predictions along with their confidence percentage \n \
Plausibility Score - The measurement of how much the predicted bounding boxes overlap with the attributions generated (bounding box model output) \n \
Inference Size - The resolution of the image that will be interpreted (default is 640 by 640 pixels) \n \
Object Confidence Threshold - The minimum confidence required to display a bounding box \n \
IOU Threshold for NMS - The minimum confidence required for a bounding box to surpass another in cases of multiple predictions \n \
Agnostic NMS - Enable non max suppression to use the bounding boxes with the highest confidence in prediction \n \
Normalize Gradient - Scale the gradient vector to have a magnitude of 1 \n \
Weights File - A .pt file that contains the weight values that make the filter. This contains the classes and will greatly affect how an image or video is classified")
if file == "Image":
if source == "Computer":
if skill_type == "Advanced":
return {
conv_layer: gr.Slider(visible=True),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=True),
input_im: gr.Image(source="upload",type='filepath',label="Input Image",
show_download_button=True,show_share_button=True,interactive=True,visible=True,streaming=False,scale=3),
output_box_im: gr.Image(visible=True,scale=3),
output_conv_im: gr.Image(visible=True,scale=3),
output_grad_im: gr.Image(visible=True,scale=3),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=True),
labels: gr.Textbox(visible=True,scale=1),
plaus: gr.Textbox(visible=True,scale=1),
formatted_time: gr.Textbox(visible=True,scale=1),
help_box: gr.Textbox(visible=False, value=help_string),
inf_size: gr.Number(visible=True),
obj_conf_thr: gr.Number(visible=True),
iou_thr: gr.Number(visible=True),
agnostic_nms: gr.Checkbox(visible=True),
weights: gr.File(visible=True)
}
elif skill_type == "Simple":
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(source="upload",type='filepath',label="Input Image",
show_download_button=True,show_share_button=True,interactive=True,visible=True,streaming=False,scale=3),
output_box_im: gr.Image(visible=True,scale=3),
output_conv_im: gr.Image(visible=False,scale=3),
output_grad_im: gr.Image(visible=False,scale=3),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False,scale=1),
plaus: gr.Textbox(visible=False,scale=1),
formatted_time: gr.Textbox(visible=False,scale=1),
help_box: gr.Textbox(visible=False, value=help_string),
inf_size: gr.Number(visible=False),
obj_conf_thr: gr.Number(visible=False),
iou_thr: gr.Number(visible=False),
agnostic_nms: gr.Checkbox(visible=False),
weights: gr.File(visible=False)
}
# Else its help
else:
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(source="upload",type='filepath',label="Input Image",
show_download_button=True,show_share_button=True,interactive=True,visible=False,streaming=False,scale=3),
output_box_im: gr.Image(visible=False,scale=3),
output_conv_im: gr.Image(visible=False,scale=3),
output_grad_im: gr.Image(visible=False,scale=3),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False,scale=1),
plaus: gr.Textbox(visible=False,scale=1),
formatted_time: gr.Textbox(visible=False,scale=1),
help_box: gr.Textbox(visible=True, value=help_string,scale=10),
inf_size: gr.Number(visible=False),
obj_conf_thr: gr.Number(visible=False),
iou_thr: gr.Number(visible=False),
agnostic_nms: gr.Checkbox(visible=False),
weights: gr.File(visible=False)
}
elif source == "Webcam":
if skill_type == "Advanced":
return {
conv_layer: gr.Slider(visible=True),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=True),
input_im: gr.Image(type='pil',source="webcam",label="Input Image",
visible=True,interactive=True,streaming=False,scale=3),
output_box_im: gr.Image(visible=True,scale=3),
output_conv_im: gr.Image(visible=True,scale=3),
output_grad_im: gr.Image(visible=True,scale=3),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=True),
labels: gr.Textbox(visible=True),
plaus: gr.Textbox(visible=True),
formatted_time: gr.Textbox(visible=True),
help_box: gr.Textbox(visible=False, value=help_string),
inf_size: gr.Number(visible=True),
obj_conf_thr: gr.Number(visible=True),
iou_thr: gr.Number(visible=True),
agnostic_nms: gr.Checkbox(visible=True),
weights: gr.File(visible=True)
}
elif skill_type == "Simple":
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(type='pil',source="webcam",label="Input Image",
visible=True,interactive=True,streaming=False,scale=3),
output_box_im: gr.Image(visible=True,scale=3),
output_conv_im: gr.Image(visible=False,scale=3),
output_grad_im: gr.Image(visible=False,scale=3),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False),
plaus: gr.Textbox(visible=False),
formatted_time: gr.Textbox(visible=False),
help_box: gr.Textbox(visible=False, value=help_string),
inf_size: gr.Number(visible=False),
obj_conf_thr: gr.Number(visible=False),
iou_thr: gr.Number(visible=False),
agnostic_nms: gr.Checkbox(visible=False),
weights: gr.File(visible=False)
}
else:
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(source="upload",type='filepath',label="Input Image",
show_download_button=True,show_share_button=True,interactive=True,visible=False,streaming=False,scale=3),
output_box_im: gr.Image(visible=False,scale=3),
output_conv_im: gr.Image(visible=False,scale=3),
output_grad_im: gr.Image(visible=False,scale=3),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False,scale=1),
plaus: gr.Textbox(visible=False,scale=1),
formatted_time: gr.Textbox(visible=False,scale=1),
help_box: gr.Textbox(visible=True, value=help_string,scale=10),
inf_size: gr.Number(visible=False),
obj_conf_thr: gr.Number(visible=False),
iou_thr: gr.Number(visible=False),
agnostic_nms: gr.Checkbox(visible=False),
weights: gr.File(visible=False)
}
elif file == "Video":
if source == "Computer":
if skill_type == "Advanced":
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(visible=False,streaming=False),
output_box_im: gr.Image(visible=False),
output_conv_im: gr.Image(visible=False),
output_grad_im: gr.Image(visible=False),
input_vid: gr.Video(source="upload",label="Input Video",
show_share_button=True,interactive=True,visible=True,scale=6),
output_box_vid: gr.Video(label="Output Video",show_share_button=True,visible=True,scale=6),
norm: gr.Checkbox(visible=True),
labels: gr.Textbox(visible=False),
plaus: gr.Textbox(visible=False),
formatted_time: gr.Textbox(visible=True,scale=1),
help_box: gr.Textbox(visible=False, value=help_string,scale=10),
inf_size: gr.Number(visible=True),
obj_conf_thr: gr.Number(visible=True),
iou_thr: gr.Number(visible=True),
agnostic_nms: gr.Checkbox(visible=True),
weights: gr.File(visible=True)
}
elif skill_type == "Simple":
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(visible=False,streaming=False),
output_box_im: gr.Image(visible=False),
output_conv_im: gr.Image(visible=False),
output_grad_im: gr.Image(visible=False),
input_vid: gr.Video(source="upload",label="Input Video",
show_share_button=True,interactive=True,visible=True,scale=6),
output_box_vid: gr.Video(label="Output Video",show_share_button=True,visible=True,scale=6),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False),
plaus: gr.Textbox(visible=False),
formatted_time: gr.Textbox(visible=False),
help_box: gr.Textbox(visible=False, value=help_string),
inf_size: gr.Number(visible=False),
obj_conf_thr: gr.Number(visible=False),
iou_thr: gr.Number(visible=False),
agnostic_nms: gr.Checkbox(visible=False),
weights: gr.File(visible=False)
}
else:
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(source="upload",type='filepath',label="Input Image",
show_download_button=True,show_share_button=True,interactive=True,visible=False,streaming=False,scale=3),
output_box_im: gr.Image(visible=False,scale=3),
output_conv_im: gr.Image(visible=False,scale=3),
output_grad_im: gr.Image(visible=False,scale=3),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False,scale=1),
plaus: gr.Textbox(visible=False,scale=1),
formatted_time: gr.Textbox(visible=False,scale=1),
help_box: gr.Textbox(visible=True, value=help_string,scale=10),
inf_size: gr.Number(visible=False),
obj_conf_thr: gr.Number(visible=False),
iou_thr: gr.Number(visible=False),
agnostic_nms: gr.Checkbox(visible=False),
weights: gr.File(visible=False)
}
#TODO-clinic: Fix possible streaming issues
elif source == "Webcam":
if is_stream:
if skill_type == "Advanced":
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=True),
output_map: gr.Slider(visible=False),
input_im: gr.Image(type='pil',source="webcam",label="Input Image",
streaming=True,visible=True,interactive=True,scale=6),
output_box_im: gr.Image(visible=True,scale=6),
output_conv_im: gr.Image(visible=False), #TODO-clinic: We may not want to hide all of these
output_grad_im: gr.Image(visible=False),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=True),
labels: gr.Textbox(visible=False),
plaus: gr.Textbox(visible=False),
formatted_time: gr.Textbox(visible=True,scale=1),
help_box: gr.Textbox(visible=False, value=help_string),
inf_size: gr.Number(visible=True),
obj_conf_thr: gr.Number(visible=True),
iou_thr: gr.Number(visible=True),
agnostic_nms: gr.Checkbox(visible=True),
weights: gr.File(visible=True)
}
elif skill_type == "Simple":
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=True),
output_map: gr.Slider(visible=False),
input_im: gr.Image(type='pil',source="webcam",label="Input Image",
streaming=True,visible=True,interactive=True,scale=6),
output_box_im: gr.Image(visible=True,scale=6),
output_conv_im: gr.Image(visible=False),
output_grad_im: gr.Image(visible=False),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False),
plaus: gr.Textbox(visible=False),
formatted_time: gr.Textbox(visible=False),
help_box: gr.Textbox(visible=False, value=help_string),
inf_size: gr.Number(visible=False),
obj_conf_thr: gr.Number(visible=False),
iou_thr: gr.Number(visible=False),
agnostic_nms: gr.Checkbox(visible=False),
weights: gr.File(visible=False)
}
else:
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(source="upload",type='filepath',label="Input Image",
show_download_button=True,show_share_button=True,interactive=True,visible=False,streaming=False,scale=3),
output_box_im: gr.Image(visible=False,scale=3),
output_conv_im: gr.Image(visible=False,scale=3),
output_grad_im: gr.Image(visible=False,scale=3),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False,scale=1),
plaus: gr.Textbox(visible=False,scale=1),
formatted_time: gr.Textbox(visible=False,scale=1),
help_box: gr.Textbox(visible=True, value=help_string,scale=10),
inf_size: gr.Number(visible=False),
obj_conf_thr: gr.Number(visible=False),
iou_thr: gr.Number(visible=False),
agnostic_nms: gr.Checkbox(visible=False),
weights: gr.File(visible=False)
}
elif not is_stream:
if skill_type == "Advanced":
return {
#TODO-clinic: We may not want to hide all of these
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=True, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(visible=False,streaming=False),
output_box_im: gr.Image(visible=False),
output_conv_im: gr.Image(visible=False),
output_grad_im: gr.Image(visible=False),
input_vid: gr.Video(label="Input Video",source="webcam",
show_share_button=True,interactive=True,visible=True,scale=6),
output_box_vid: gr.Video(label="Output Video",show_share_button=True,visible=True,scale=6),
norm: gr.Checkbox(visible=True),
labels: gr.Textbox(visible=False),
plaus: gr.Textbox(visible=False),
formatted_time: gr.Textbox(visible=True,scale=1),
help_box: gr.Textbox(visible=False, value=help_string),
inf_size: gr.Number(visible=True),
obj_conf_thr: gr.Number(visible=True),
iou_thr: gr.Number(visible=True),
agnostic_nms: gr.Checkbox(visible=True),
weights: gr.File(visible=True)
}
elif skill_type == "Simple":
#TODO-clinic: Update for simple
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=True, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(visible=False,streaming=False),
output_box_im: gr.Image(visible=False),
output_conv_im: gr.Image(visible=False),
output_grad_im: gr.Image(visible=False),
input_vid: gr.Video(label="Input Video",source="webcam",
show_share_button=True,interactive=True,visible=True,scale=6),
output_box_vid: gr.Video(label="Output Video",show_share_button=True,visible=True,scale=6),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False),
plaus: gr.Textbox(visible=False),
formatted_time: gr.Textbox(visible=False),
help_box: gr.Textbox(visible=False, value=help_string),
inf_size: gr.Number(visible=False),
obj_conf_thr: gr.Number(visible=False),
iou_thr: gr.Number(visible=False),
agnostic_nms: gr.Checkbox(visible=False),
weights: gr.File(visible=False)
}
else:
return {
conv_layer: gr.Slider(visible=False),
video_stream: gr.Checkbox(visible=False, value=False),
output_map: gr.Slider(visible=False),
input_im: gr.Image(source="upload",type='filepath',label="Input Image",
show_download_button=True,show_share_button=True,interactive=True,visible=False,streaming=False,scale=3),
output_box_im: gr.Image(visible=False,scale=3),
output_conv_im: gr.Image(visible=False,scale=3),
output_grad_im: gr.Image(visible=False,scale=3),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False,scale=1),
plaus: gr.Textbox(visible=False,scale=1),
formatted_time: gr.Textbox(visible=False,scale=1),
help_box: gr.Textbox(visible=True, value=help_string,scale=10),
inf_size: gr.Number(visible=False),
obj_conf_thr: gr.Number(visible=False),
iou_thr: gr.Number(visible=False),
agnostic_nms: gr.Checkbox(visible=False),
weights: gr.File(visible=False)
}
def change_conv_layer(layer):
"""
Changes the shown convolutional output layer based on gradio slider
Args:
layer (int): The layer to show
Returns:
str: The file path of the output image
"""
return "outputs/runs/detect/exp/layers/layer" + str(int(int(layer) - 1)) + '.jpg'
def change_output_num(number):
"""
Changes the shown gradient map based on gradio slider
Args:
number (int): The gradient map to show
Returns:
str: The file path of the output image
"""
return "outputs/runs/detect/exp/smoothGrad" + str(int(int(number) -1)) + '.jpg'
def demo1():
"""
Run a test image file as a demo
"""
#TODO-clinic - There may be a better way to do this (move to another file)
file_type = gr.Radio(label="File Type",info="Choose 'Image' if you are uploading an image, Choose 'Video' if you are uploading a video",
choices=['Image','Video'],value='Image',show_label=True,interactive=True,visible=False)
input_im = gr.Image(type='filepath',label="Input Image",value= "references/inference/images/bus.jpg",
show_download_button=True,show_share_button=True,interactive=True,visible=False)
input_vid = gr.Video(source="upload",label="Input Video",
show_share_button=True,interactive=True,visible=False)
source_type = gr.Radio(label="Source Type",info="Choose 'Computer' if you are uploading from your computer, Choose 'Webcam' if you would like to use your webcam",
choices=['Computer','Webcam'],value='Computer',show_label=True,interactive=True,visible=False)
inf_size = gr.Number(label='Inference Size (pixels)',value=640,precision=0,visible=False)
obj_conf_thr = gr.Number(label='Object Confidence Threshold',value=0.25,visible=False)
iou_thr = gr.Number(label='IOU threshold for NMS',value=0.45,visible=False)
conv_layer = gr.Slider(label="Convolution Layer",info="Choose a whole number from 1 to 17 to see the corresponding convolutional layer",
minimum=1,maximum=17,value=1,interactive=True,step=1,show_label=True,visible=False)
agnostic_nms = gr.Checkbox(label='Agnostic NMS',value=True,visible=False)
output_map = gr.Slider(label="Map Output Number",info="Choose a whole number from 1 to 3 to see the corresponding attribution map",
minimum=1,maximum=3,value=1,interactive=True,step=1,show_label=True,visible=False)
video_stream = gr.Checkbox(label="Stream from webcam?",info="Check this box if you would like to stream from your webcam",value=False,show_label=True,interactive=True,visible=False)
norm = gr.Checkbox(label='Normalize Gradient',value=False,visible=False)
weights = gr.File(label='Weights File',type='file',file_count='single',file_types=["pt"],value="weights/yolov7.pt",visible=False)
skill_type = gr.Radio(label="Skill Type",info="Choose 'Advanced' if you would like to use the advanced interface, 'Simple' if you would like that version, or help for more info",
choices=['Advanced','Simple'],value='Advanced',show_label=True,interactive=True,visible=False)
demo1_inputs = [file_type, input_im, input_vid, source_type,
inf_size, obj_conf_thr, iou_thr, conv_layer,
agnostic_nms, output_map, video_stream, norm, weights,skill_type]
return demo1_inputs
def demo2():
"""
Run a test video file as a demo
"""
#TODO-clinic - There may be a better way to do this (move to another file)
file_type = gr.Radio(label="File Type",info="Choose 'Image' if you are uploading an image, Choose 'Video' if you are uploading a video",
choices=['Image','Video'],value='Video',show_label=True,interactive=True,visible=False)
input_im = gr.Image(type='filepath',label="Input Image",
show_download_button=True,show_share_button=True,interactive=True,visible=False)
input_vid = gr.Video(source="upload",label="Input Video",
show_share_button=True,interactive=True,visible=False,value= "references/inference/videos/ducks.mp4")
source_type = gr.Radio(label="Source Type",info="Choose 'Computer' if you are uploading from your computer, Choose 'Webcam' if you would like to use your webcam",
choices=['Computer','Webcam'],value='Computer',show_label=True,interactive=True,visible=False)
inf_size = gr.Number(label='Inference Size (pixels)',value=640,precision=0,visible=False)
obj_conf_thr = gr.Number(label='Object Confidence Threshold',value=0.25,visible=False)
iou_thr = gr.Number(label='IOU threshold for NMS',value=0.45,visible=False)
conv_layer = gr.Slider(label="Convolution Layer",info="Choose a whole number from 1 to 17 to see the corresponding convolutional layer",
minimum=1,maximum=17,value=1,interactive=True,step=1,show_label=True,visible=False)
agnostic_nms = gr.Checkbox(label='Agnostic NMS',value=True,visible=False)
output_map = gr.Slider(label="Map Output Number",info="Choose a whole number from 1 to 3 to see the corresponding attribution map",
minimum=1,maximum=3,value=1,interactive=True,step=1,show_label=True,visible=False)
video_stream = gr.Checkbox(label="Stream from webcam?",info="Check this box if you would like to stream from your webcam",value=False,show_label=True,interactive=True,visible=False)
norm = gr.Checkbox(label='Normalize Gradient',value=False,visible=False)
weights = gr.File(label='Weights File',type='file',file_count='single',file_types=["pt"],value="weights/yolov7.pt",visible=False)
skill_type = gr.Radio(label="Skill Type",info="Choose 'Advanced' if you would like to use the advanced interface, Choose 'Simple' if you would like that version",
choices=['Advanced','Simple'],value='Advanced',show_label=True,interactive=True,visible=False)
demo2_inputs = [file_type, input_im, input_vid, source_type,
inf_size, obj_conf_thr, iou_thr, conv_layer,
agnostic_nms, output_map, video_stream, norm, weights,skill_type]
return demo2_inputs
# List of gradio components that change during method "change_file_type"
change_comp_list = [conv_layer, video_stream, output_map,
input_im, output_box_im, output_conv_im, output_grad_im,
input_vid, output_box_vid, norm, labels, plaus, formatted_time,
help_box, inf_size, obj_conf_thr, iou_thr, agnostic_nms, weights]
# List of gradio components that are input into the run_all method (when start button is clicked)
run_inputs = [file_type, input_im, input_vid, source_type,
inf_size, obj_conf_thr, iou_thr, conv_layer,
agnostic_nms, output_map, video_stream, norm, weights, skill_type] #TODO-clinic: Skill_type might be able to be removed here
# List of gradio components that are output from the run_all method (when start button is clicked)
run_outputs = [output_box_im, output_conv_im, output_grad_im, labels, plaus, formatted_time, output_box_vid, input_im, input_vid]
# When these settings are changed, the change_file_type method is called
file_type.input(change_file_type, show_progress=True, inputs=[file_type, source_type, video_stream, skill_type], outputs=change_comp_list)
source_type.input(change_file_type, show_progress=True, inputs=[file_type, source_type, video_stream, skill_type], outputs=change_comp_list)
video_stream.input(change_file_type, show_progress=True, inputs=[file_type, source_type, video_stream, skill_type], outputs=change_comp_list)
skill_type.input(change_file_type, show_progress=True, inputs=[file_type, source_type, video_stream, skill_type], outputs=change_comp_list)
# When start button is clicked, the run_all method is called
start_but.click(run_all, inputs=run_inputs, outputs=run_outputs)
# When demo1 button is clicked, run the function with stored params
demo1_but.click(run_all, inputs=demo1(), outputs=run_outputs)
# # When demo2 button is clicked, run the function with stored params
demo2_but.click(run_all, inputs=demo2(), outputs=run_outputs)
# When video is uploaded, the correct_video method is called
input_vid.upload(correct_video, inputs=[input_vid], outputs=[input_vid])
# When the convolutional layer setting is changed, the change_conv_layer method is called
conv_layer.input(change_conv_layer, conv_layer, output_conv_im)
# When the stream setting is true, run the stream
input_im.stream(run_all, inputs=run_inputs, outputs=run_outputs)
# When the gradient number is changed, the change_output_num method is called
output_map.input(change_output_num, output_map, output_grad_im)
# When the demo is first started, run the change_file_type method to ensure default settings
demo.load(change_file_type, show_progress=True, inputs=[file_type, source_type, video_stream, skill_type], outputs=change_comp_list)
return demo
# #Used for debugging
# if __name__== "__main__" :
# # If True, it launches Gradio interface
# # If False, it runs without the interface (for better debugging)
# if True:
# # demo.queue().launch(share=True)
# demo.queue().launch()
# else:
# #Test with yolo (weights and normal photo)
# run_all('Image','references\\inference\\images\\bus.jpg', 'None', 'Computer', 640,0.25,0.45,1,True,1,False,False,"yolov7.pt", "Advanced")
# #Test drone (weights and drone photo) TODO: Weight set may not be included in the repo
# #run_all('Image','references\\inference\\images\\V_DRONE_113_34.jpg', 'None', 'Computer', 640,0.25,0.45,1,True,1,False,False,"drone_63.pt", "Advanced") |