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import subprocess
import os
if os.getenv('SYSTEM') == 'spaces':
subprocess.call('pip install tensorflow==2.9'.split())
subprocess.call('pip install keras==2.9'.split())
subprocess.call('pip install git+https://github.com/facebookresearch/segment-anything.git')
subprocess.call('pip install opencv-python-headless==4.5.5.64'.split())
subprocess.call('pip install git+https://github.com/cocodataset/panopticapi.git'.split())
import gradio as gr
from huggingface_hub import snapshot_download
import cv2
import dotenv
dotenv.load_dotenv()
import numpy as np
import gradio as gr
import glob
from inference_sam import segmentation_sam
import pathlib
if not os.path.exists('images'):
REPO_ID='Serrelab/image_examples_gradio'
snapshot_download(repo_id=REPO_ID, token=os.environ.get('READ_TOKEN'),repo_type='dataset',local_dir='images')
def segment_image(input_image):
img = segmentation_sam(input_image)
return img
def classify_image(input_image, model_name):
if 'Rock 170' ==model_name:
from inference_resnet import inference_resnet_finer
result = inference_resnet_finer(input_image,model_name,n_classes=171)
return result
elif 'Mummified 170' ==model_name:
from inference_resnet import inference_resnet_finer
result = inference_resnet_finer(input_image,model_name,n_classes=170)
return result
if 'Fossils 19' ==model_name:
from inference_beit import inference_dino
return inference_dino(input_image,model_name)
return None
def find_closest(input_image):
return None
with gr.Blocks(theme='sudeepshouche/minimalist') as demo:
with gr.Tab(" 19 Classes Support"):
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input")
classify_image_button = gr.Button("Classify Image")
with gr.Column():
segmented_image = gr.outputs.Image(label="SAM output",type='numpy')
segment_button = gr.Button("Segment Image")
#classify_segmented_button = gr.Button("Classify Segmented Image")
with gr.Column():
drop_2 = gr.Dropdown(
["Mummified 170", "Rock 170", "Fossils 19"],
multiselect=False,
value=["Rock 170"],
label="Model",
interactive=True,
)
class_predicted = gr.Label(label='Class Predicted',num_top_classes=10)
with gr.Row():
paths = sorted(pathlib.Path('images/').rglob('*.jpg'))
samples=[[path.as_posix()] for path in paths if 'fossils' in str(path) ][:19]
examples_fossils = gr.Examples(samples, inputs=input_image,examples_per_page=10,label='Fossils Examples from the dataset')
samples=[[path.as_posix()] for path in paths if 'leaves' in str(path) ][:19]
examples_leaves = gr.Examples(samples, inputs=input_image,examples_per_page=5,label='Leaves Examples from the dataset')
with gr.Accordion("Using Diffuser"):
with gr.Column():
prompt = gr.Textbox(lines=1, label="Prompt")
output_image = gr.Image(label="Output")
generate_button = gr.Button("Generate Leave")
with gr.Column():
class_predicted2 = gr.Label(label='Class Predicted from diffuser')
classify_button = gr.Button("Classify Image")
with gr.Accordion("Explanations "):
gr.Markdown("Computing Explanations from the model")
with gr.Row():
original_input = gr.Image(label="Original Frame")
saliency = gr.Image(label="saliency")
gradcam = gr.Image(label='gradcam')
guided_gradcam = gr.Image(label='guided gradcam')
guided_backprop = gr.Image(label='guided backprop')
generate_explanations = gr.Button("Generate Explanations")
with gr.Accordion('Closest Images'):
gr.Markdown("Finding the closest images in the dataset")
with gr.Row():
closest_image_0 = gr.Image(label='Closest Image')
closest_image_1 = gr.Image(label='Second Closest Image')
closest_image_2 = gr.Image(label='Third Closest Image')
closest_image_3 = gr.Image(label='Forth Closest Image')
closest_image_4 = gr.Image(label='Fifth Closest Image')
find_closest_btn = gr.Button("Find Closest Images")
segment_button.click(segment_image, inputs=input_image, outputs=segmented_image)
classify_image_button.click(classify_image, inputs=[input_image,drop_2], outputs=class_predicted)
#classify_segmented_button.click(classify_image, inputs=[segmented_image,drop_2], outputs=class_predicted)
demo.launch(debug=True)
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