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import gradio as gr # Gradio package for interface
import sys # System package for path dependencies
sys.path.append('Interface_Dependencies')
sys.path.append('Engineering-Clinic-Emerging-AI-Design-Interface/Interface_Dependencies')
sys.path.append('Engineering-Clinic-Emerging-AI-Design-Interface/yolov7-main')
sys.path.append('./') # to run '$ python *.py' files in subdirectories
from run_methods import run_all, correct_video
# Gradio Interface Code
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!
""")
# For for input & output settings
with gr.Row() as file_settings:
# 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)
# 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=True)
# 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=True)
# 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=True)
# 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=True)
# Default label output textbox: Visible
labels = gr.Textbox(label='Top Predictions', value = "")
# Default time output textbox: Visible
formatted_time = gr.Textbox(label = 'Time to Run in Seconds:', value = "")
# 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)
# List of components for clearing
clear_comp_list = [input_im, output_box_im, output_conv_im, output_grad_im, labels, formatted_time, input_vid, output_box_vid]
# For start & clear buttons
with gr.Row() as buttons:
start_but = gr.Button(label="Start")
clear_but = gr.ClearButton(value='Clear All',components=clear_comp_list,
interactive=True,visible=True)
# 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)
# Object confidence threshold
obj_conf_thr = gr.Number(label='Object Confidence Threshold',value=0.25)
# Intersection of union threshold
iou_thr = gr.Number(label='IOU threshold for NMS',value=0.45)
# Agnostic NMS boolean
agnostic_nms = gr.Checkbox(label='Agnostic NMS',value=True)
# Normailze gradient boolean
norm = gr.Checkbox(label='Normalize Gradient',value=False,visible=True)
def change_file_type(file, source, is_stream):
"""
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.
"""
if file == "Image":
if source == "Computer":
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),
output_box_im: gr.Image(visible=True),
output_conv_im: gr.Image(visible=True),
output_grad_im: gr.Image(visible=True),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=True),
labels: gr.Textbox(visible=True),
formatted_time: gr.Textbox(visible=True)
}
elif source == "Webcam":
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),
output_box_im: gr.Image(visible=True),
output_conv_im: gr.Image(visible=True),
output_grad_im: gr.Image(visible=True),
input_vid: gr.Video(visible=False),
output_box_vid: gr.Video(visible=False),
norm: gr.Checkbox(visible=True),
labels: gr.Textbox(visible=True),
formatted_time: gr.Textbox(visible=True)
}
elif file == "Video":
if source == "Computer":
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),
output_box_vid: gr.Video(label="Output Video",show_share_button=True,visible=True),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False),
formatted_time: gr.Textbox(visible=False)
}
elif source == "Webcam":
if is_stream:
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),
output_box_im: gr.Image(visible=True),
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),
formatted_time: gr.Textbox(visible=False)
}
elif not is_stream:
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),
output_box_vid: gr.Video(label="Output Video",show_share_button=True,visible=True),
norm: gr.Checkbox(visible=False),
labels: gr.Textbox(visible=False),
formatted_time: gr.Textbox(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):
return "outputs\\runs\\detect\\exp\\smoothGrad" + str(int(int(number) -1)) + '.jpg'
# 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, formatted_time]
# 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]
# 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, formatted_time, output_box_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], outputs=change_comp_list)
source_type.input(change_file_type, show_progress=True, inputs=[file_type, source_type, video_stream], outputs=change_comp_list)
video_stream.input(change_file_type, show_progress=True, inputs=[file_type, source_type, video_stream], 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 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], outputs=change_comp_list)
if __name__== "__main__" :
# If True, it launches Gradio interface
# If False, it runs without the interface
if True:
# demo.queue().launch(share=True)
demo.queue().launch()
else:
# run_image("inference\\images\\bus.jpg","Computer",640,0.45,0.25,1,True)
run_video("0", "Webcam", 640, 0.25, 0.45, True, True) |