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| import os | |
| import sys | |
| from env import config_env | |
| config_env() | |
| 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 | |
| from explanations import explain | |
| from inference_resnet import get_triplet_model | |
| from inference_beit import get_triplet_model_beit | |
| import pathlib | |
| import tensorflow as tf | |
| from closest_sample import get_images | |
| 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') | |
| if not os.path.exists('dataset'): | |
| REPO_ID='Serrelab/Fossils' | |
| token = os.environ.get('READ_TOKEN') | |
| print(f"Read token:{token}") | |
| if token is None: | |
| print("warning! A read token in env variables is needed for authentication.") | |
| snapshot_download(repo_id=REPO_ID, token=token,repo_type='dataset',local_dir='dataset') | |
| def get_model(model_name): | |
| if model_name=='Mummified 170': | |
| n_classes = 170 | |
| model = get_triplet_model(input_shape = (600, 600, 3), | |
| embedding_units = 256, | |
| embedding_depth = 2, | |
| backbone_class=tf.keras.applications.ResNet50V2, | |
| nb_classes = n_classes,load_weights=False,finer_model=True,backbone_name ='Resnet50v2') | |
| model.load_weights('model_classification/mummified-170.h5') | |
| elif model_name=='Rock 170': | |
| n_classes = 171 | |
| model = get_triplet_model(input_shape = (600, 600, 3), | |
| embedding_units = 256, | |
| embedding_depth = 2, | |
| backbone_class=tf.keras.applications.ResNet50V2, | |
| nb_classes = n_classes,load_weights=False,finer_model=True,backbone_name ='Resnet50v2') | |
| model.load_weights('model_classification/rock-170.h5') | |
| elif model_name == 'Fossils 142': | |
| n_classes = 142 | |
| model = get_triplet_model_beit(input_shape = (384, 384, 3), | |
| embedding_units = 256, | |
| embedding_depth = 2, | |
| n_classes = n_classes) | |
| model.load_weights('model_classification/fossil-142.h5') | |
| else: | |
| raise ValueError(f"Model name '{model_name}' is not recognized") | |
| return model,n_classes | |
| def segment_image(input_image): | |
| img = segmentation_sam(input_image) | |
| return img | |
| def classify_image(input_image, model_name): | |
| #segmented_image = segment_image(input_image) | |
| if 'Rock 170' ==model_name: | |
| from inference_resnet import inference_resnet_finer | |
| model,n_classes= get_model(model_name) | |
| result = inference_resnet_finer(input_image,model,size=600,n_classes=n_classes) | |
| return result | |
| elif 'Mummified 170' ==model_name: | |
| from inference_resnet import inference_resnet_finer | |
| model, n_classes= get_model(model_name) | |
| result = inference_resnet_finer(input_image,model,size=600,n_classes=n_classes) | |
| return result | |
| if 'Fossils 142' ==model_name: | |
| from inference_beit import inference_resnet_finer_beit | |
| model,n_classes = get_model(model_name) | |
| result = inference_resnet_finer_beit(input_image,model,size=384,n_classes=n_classes) | |
| return result | |
| return None | |
| def get_embeddings(input_image,model_name): | |
| if 'Rock 170' ==model_name: | |
| from inference_resnet import inference_resnet_embedding | |
| model,n_classes= get_model(model_name) | |
| result = inference_resnet_embedding(input_image,model,size=600,n_classes=n_classes) | |
| return result | |
| elif 'Mummified 170' ==model_name: | |
| from inference_resnet import inference_resnet_embedding | |
| model, n_classes= get_model(model_name) | |
| result = inference_resnet_embedding(input_image,model,size=600,n_classes=n_classes) | |
| return result | |
| if 'Fossils 142' ==model_name: | |
| from inference_beit import inference_resnet_embedding_beit | |
| model,n_classes = get_model(model_name) | |
| result = inference_resnet_embedding_beit(input_image,model,size=384,n_classes=n_classes) | |
| return result | |
| return None | |
| def find_closest(input_image,model_name): | |
| embedding = get_embeddings(input_image,model_name) | |
| classes, paths = get_images(embedding) | |
| #outputs = classes+paths | |
| return classes,paths | |
| def explain_image(input_image,model_name): | |
| model,n_classes= get_model(model_name) | |
| if model_name=='Fossils 142': | |
| size = 384 | |
| else: | |
| size = 600 | |
| #saliency, integrated, smoothgrad, | |
| exp_list = explain(model,input_image,size = size, n_classes=n_classes) | |
| #original = saliency + integrated + smoothgrad | |
| print('done') | |
| sobol1,sobol2,sobol3,sobol4,sobol5 = exp_list[0],exp_list[1],exp_list[2],exp_list[3],exp_list[4] | |
| rise1,rise2,rise3,rise4,rise5 = exp_list[5],exp_list[6],exp_list[7],exp_list[8],exp_list[9] | |
| hsic1,hsic2,hsic3,hsic4,hsic5 = exp_list[10],exp_list[11],exp_list[12],exp_list[13],exp_list[14] | |
| saliency1,saliency2,saliency3,saliency4,saliency5 = exp_list[15],exp_list[16],exp_list[17],exp_list[18],exp_list[19] | |
| return sobol1,sobol2,sobol3,sobol4,sobol5,rise1,rise2,rise3,rise4,rise5,hsic1,hsic2,hsic3,hsic4,hsic5,saliency1,saliency2,saliency3,saliency4,saliency5 | |
| #minimalist theme | |
| with gr.Blocks(theme='sudeepshouche/minimalist') as demo: | |
| with gr.Tab(" Florrissant Fossils"): | |
| 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') | |
| # segmented_image=gr.Image(label="Segmented Image", type='numpy') | |
| # segment_button = gr.Button("Segment Image") | |
| # #classify_segmented_button = gr.Button("Classify Segmented Image") | |
| with gr.Column(): | |
| model_name = gr.Dropdown( | |
| ["Mummified 170", "Rock 170","Fossils 142"], | |
| multiselect=False, | |
| value="Fossils 142", # default option | |
| 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.Column(): | |
| with gr.Row(): | |
| #original_input = gr.Image(label="Original Frame") | |
| #saliency = gr.Image(label="saliency") | |
| #gradcam = gr.Image(label='integraged gradients') | |
| #guided_gradcam = gr.Image(label='gradcam') | |
| #guided_backprop = gr.Image(label='guided backprop') | |
| sobol1 = gr.Image(label = 'Sobol1') | |
| sobol2= gr.Image(label = 'Sobol2') | |
| sobol3= gr.Image(label = 'Sobol3') | |
| sobol4= gr.Image(label = 'Sobol4') | |
| sobol5= gr.Image(label = 'Sobol5') | |
| with gr.Row(): | |
| rise1 = gr.Image(label = 'Rise1') | |
| rise2 = gr.Image(label = 'Rise2') | |
| rise3 = gr.Image(label = 'Rise3') | |
| rise4 = gr.Image(label = 'Rise4') | |
| rise5 = gr.Image(label = 'Rise5') | |
| with gr.Row(): | |
| hsic1 = gr.Image(label = 'HSIC1') | |
| hsic2 = gr.Image(label = 'HSIC2') | |
| hsic3 = gr.Image(label = 'HSIC3') | |
| hsic4 = gr.Image(label = 'HSIC4') | |
| hsic5 = gr.Image(label = 'HSIC5') | |
| with gr.Row(): | |
| saliency1 = gr.Image(label = 'Saliency1') | |
| saliency2 = gr.Image(label = 'Saliency2') | |
| saliency3 = gr.Image(label = 'Saliency3') | |
| saliency4 = gr.Image(label = 'Saliency4') | |
| saliency5 = gr.Image(label = 'Saliency5') | |
| generate_explanations = gr.Button("Generate Explanations") | |
| # with gr.Accordion('Closest Images'): | |
| # gr.Markdown("Finding the closest images in the dataset") | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # label_closest_image_0 = gr.Markdown('') | |
| # closest_image_0 = gr.Image(label='Closest Image',image_mode='contain',width=200, height=200) | |
| # with gr.Column(): | |
| # label_closest_image_1 = gr.Markdown('') | |
| # closest_image_1 = gr.Image(label='Second Closest Image',image_mode='contain',width=200, height=200) | |
| # with gr.Column(): | |
| # label_closest_image_2 = gr.Markdown('') | |
| # closest_image_2 = gr.Image(label='Third Closest Image',image_mode='contain',width=200, height=200) | |
| # with gr.Column(): | |
| # label_closest_image_3 = gr.Markdown('') | |
| # closest_image_3 = gr.Image(label='Forth Closest Image',image_mode='contain', width=200, height=200) | |
| # with gr.Column(): | |
| # label_closest_image_4 = gr.Markdown('') | |
| # closest_image_4 = gr.Image(label='Fifth Closest Image',image_mode='contain',width=200, height=200) | |
| # find_closest_btn = gr.Button("Find Closest Images") | |
| with gr.Accordion('Closest Images'): | |
| gr.Markdown("Finding the closest images in the dataset") | |
| with gr.Row(): | |
| gallery = gr.Gallery(label="Closest Images", show_label=False,elem_id="gallery",columns=[5], rows=[1],height='auto', allow_preview=True, preview=None) | |
| #.style(grid=[1, 5], height=200, width=200) | |
| 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,model_name], outputs=class_predicted) | |
| generate_explanations.click(explain_image, inputs=[input_image,model_name], outputs=[sobol1,sobol2,sobol3,sobol4,sobol5,rise1,rise2,rise3,rise4,rise5,hsic1,hsic2,hsic3,hsic4,hsic5,saliency1,saliency2,saliency3,saliency4,saliency5]) # | |
| #find_closest_btn.click(find_closest, inputs=[input_image,model_name], outputs=[label_closest_image_0,label_closest_image_1,label_closest_image_2,label_closest_image_3,label_closest_image_4,closest_image_0,closest_image_1,closest_image_2,closest_image_3,closest_image_4]) | |
| def update_outputs(input_image,model_name): | |
| labels, images = find_closest(input_image,model_name) | |
| #labels_html = "".join([f'<div style="display: inline-block; text-align: center; width: 18%;">{label}</div>' for label in labels]) | |
| #labels_markdown = f"<div style='width: 100%; text-align: center;'>{labels_html}</div>" | |
| image_caption=[] | |
| for i in range(5): | |
| image_caption.append((images[i],labels[i])) | |
| return image_caption | |
| find_closest_btn.click(fn=update_outputs, inputs=[input_image,model_name], outputs=[gallery]) | |
| #classify_segmented_button.click(classify_image, inputs=[segmented_image,model_name], outputs=class_predicted) | |
| demo.queue() # manage multiple incoming requests | |
| if os.getenv('SYSTEM') == 'spaces': | |
| demo.launch(width='40%',auth=(os.environ.get('USERNAME'), os.environ.get('PASSWORD'))) | |
| else: | |
| demo.launch() | |