Delete gradio_app.py

gradio_app.py

@@ -1,179 +0,0 @@
-import gradio as gr
-from PIL import Image
-import torch
-import numpy as np
-import cv2
-from plantcv import plantcv as pcv 
-from skimage.feature import local_binary_pattern
-from io import BytesIO
-from skimage.feature import hog
-import base64  # import the base64 module
-import openpyxl
-import pandas as pd
-from gradio import themes
-import gradio as gr
-import os
-from gradio import components
-import webbrowser
-
-#from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css
-
-theme = gr.themes.Base(
-    primary_hue="violet",
-    secondary_hue="green",
-).set(
-    body_background_fill_dark='*checkbox_label_background_fill'
-)
-
-def show_excel():
-    os.system("start excel tip_pts_mask.xlsx")
-                            
-def image_processing(image,input_type,input_choice):
-    array = np.array(image)
-    array = array.astype(np.float32)
-    gray_img = cv2.cvtColor(array, cv2.COLOR_RGB2GRAY)
-    if input_type == "Tips":
-        img1 = pcv.morphology.skeletonize(mask=gray_img)
-        output_image = pcv.morphology.find_tips(skel_img=img1, mask=None, label="default")
-        non_zero_indices = np.nonzero(output_image)
-
-        # Create a new Excel workbook and worksheet
-        workbook = openpyxl.Workbook()
-        worksheet = workbook.active
-
-        # Write the non-zero indices to the worksheet
-        for row, col in zip(*non_zero_indices):
-            worksheet.cell(row=row+1, column=1, value=row)
-            worksheet.cell(row=row+1, column=2, value=col)
-
-        # Save the workbook
-        #excel_tips = 'tip_pts_mask_indices.xlsx'
-        excel_tips= workbook.save('tip_pts_mask_indices.xlsx')
-        
-
-        # Create a DataFrame from the branch points mask
-        df = pd.DataFrame(output_image)
-        # Save the DataFrame to an excel file
-        df.to_excel('tip_pts_mask.xlsx', index=False)
-    
-
-    elif input_type == "Branches":
-        img1 = pcv.morphology.skeletonize(mask=gray_img)
-        output_image = pcv.morphology.find_branch_pts(skel_img=img1, mask=None, label="default")
-        non_zero_indices = np.nonzero(output_image)
-
-        # Create a new Excel workbook and worksheet
-        workbook = openpyxl.Workbook()
-        worksheet = workbook.active
-
-        # Write the non-zero indices to the worksheet
-        for row, col in zip(*non_zero_indices):
-            worksheet.cell(row=row+1, column=1, value=row)
-            worksheet.cell(row=row+1, column=2, value=col)
-
-        # Save the workbook
-        workbook.save('branch_pts_mask_indices.xlsx')
-
-        # Create a DataFrame from the branch points mask
-        df = pd.DataFrame(output_image)
-        # Save the DataFrame to an excel file
-        df.to_excel('branch_pts_mask.xlsx', index=False)
-
-    elif input_type == "Both":
-        img1 = pcv.morphology.skeletonize(mask=gray_img)
-        tips = pcv.morphology.find_tips(skel_img=img1, mask=None, label="default")
-        branches = pcv.morphology.find_branch_pts(skel_img=img1, mask=None, label="default")
-        output_image = np.zeros_like(img1)
-        output_image[tips > 0] = 254
-        output_image[branches > 0] = 128
-    elif input_type == "sort":
-        image = pcv.morphology.skeletonize(mask=gray_img)
-        img1,edge_objects = pcv.morphology.prune(skel_img=image, size=70, mask=None)
-        #output_image = leaf(skel_img=img1,objects=edge_objects, mask=None)    
-    elif input_type == "sift transform":
-        image = pcv.morphology.skeletonize(mask=gray_img)
-        sift = cv2.SIFT_create()
-        kp, des= sift.detectAndCompute(image, None)
-        output_image = cv2.drawKeypoints(image, kp, des)
-        np.savez('sift_descriptors.npz', descriptors=des)
-
-    elif input_type == "lbp transform":
-        radius = 1  # LBP feature radius
-        n_points = 8 * radius  # number of LBP feature points
-        output_image = local_binary_pattern(gray_img, n_points, radius)
-        # Save the LBP transformed image as a NumPy array in .npz format
-        np.savez('lbp_transform.npz', lbp=output_image)
-    
-    elif input_type == "hog transform":
-        image = pcv.morphology.skeletonize(mask=array)
-        fd,output_image = hog(gray_img, orientations=10, pixels_per_cell=(16, 16),
-                    cells_per_block=(1, 1), visualize=True, multichannel=False, channel_axis=-1)
-        np.savez('hog_transform.npz', hog=output_image)     
-    elif input_type == "compute all":
-        img1 = pcv.morphology.skeletonize(mask=gray_img)
-        if input_choice == "compute_branches":
-            output_image = pcv.morphology.find_branch_pts(skel_img=img1, mask=None, label="default")
-        elif input_choice == "compute_tips":
-            output_image = pcv.morphology.find_tips(skel_img=img1, mask=None, label="default")
-        elif input_choice == "compute_both":
-            img1 = pcv.morphology.skeletonize(mask=gray_img)
-            tips = pcv.morphology.find_tips(skel_img=img1, mask=None, label="default")
-            branches = pcv.morphology.find_branch_pts(skel_img=img1, mask=None, label="default")
-            output_image = np.zeros_like(img1)
-            output_image[tips > 0] = 255
-            output_image[branches > 0] = 128
-        elif input_choice == "compute_sift":
-            image = pcv.morphology.skeletonize(mask=gray_img)
-            sift = cv2.SIFT_create()
-            kp, des= sift.detectAndCompute(image, None)
-            output_image = cv2.drawKeypoints(image, kp, des) 
-        elif input_choice == "compute_lbp":
-            radius = 1  # LBP feature radius
-            n_points = 8 * radius  # number of LBP feature points
-            output_image = local_binary_pattern(gray_img, n_points, radius) 
-        elif input_choice == "compute_hog":
-            image = pcv.morphology.skeletonize(mask=array)
-            fd,output_image = hog(gray_img, orientations=10, pixels_per_cell=(16, 16),
-                    cells_per_block=(1, 1), visualize=True, multichannel=False, channel_axis=-1)          
-
-        
-
-    # Convert the resulting NumPy array back to a PIL image object for Gradio output
-    img2 = Image.fromarray(output_image)
-    if img2.mode == 'F':
-        img2 = img2.convert('RGB')
-
-    # Return the processed image as a Gradio output
-    return img2 
-
-body = (
-    "<center>"
-    "<a href='https://precisiongreenhouse.tamu.edu/'><img src='https://peepleslab.engr.tamu.edu/wp-content/uploads/sites/268/2023/04/AgriLife_Logo-e1681857158121.png' width=1650></a>"
-    "<br>"
-    "This demo extracts the plant statistics and the image features and also stores them. "
-    "<br>"
-    "<a href ='https://precisiongreenhouse.tamu.edu/'>The Texas A&M Plant Growth and Phenotyping Facility Data Analysis Pipeline</a>"
-    "</center>"
-)
-
-
-#@xamples = [["img1.png"],["img2.png"],["img3.png"]]
-iface = gr.Interface(
-    fn=image_processing,
-    inputs=[gr.inputs.Image(label="Input Image"),
-            gr.components.Dropdown(["Tips", "Branches","Both","SIFT Transform","LBP Transform","HOG Transform","Compute ALL"], label="Choose the operation to be performed"), 
-            gr.components.Dropdown(["Compute_Branches","Compute_Tips","Compute_Both","Compute_SIFT","Compute_LBP","Compute_HOG"],label="choose from compute all")],
-    outputs=gr.outputs.Image(type="pil", label="Processed Image"),
-             #gr.components.Button("Show Excel", type="button", onclick=show_excel)],
-    #title="TAMU AgriLife Plant Phenotyping Data Analysis Tool",
-    description= body,
-    layout="vertical",
-    allow_flagging=False,
-    allow_screenshot=False,
-    theme=theme
-    #examples=examples
-)
-
-#iface.launch()
-iface.launch()
-