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| 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 | |
| theme = gr.themes.Base( | |
| primary_hue="violet", | |
| secondary_hue="green", | |
| ).set( | |
| body_background_fill_dark='*checkbox_label_background_fill' | |
| ) | |
| def save_to_file(df, file_path, folder): | |
| # Create the folder if it doesn't exist | |
| os.makedirs(folder, exist_ok=True) | |
| # Save DataFrame to Excel file | |
| with pd.ExcelWriter(os.path.join(folder, file_path)) as writer: | |
| df.to_excel(writer, index=False) | |
| print(f"File saved at {os.path.join(folder, file_path)}") | |
| HF_TOKEN = os.getenv('HF_TOKEN') | |
| hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "test") | |
| def image_processing(image,input_type,input_choice, save_path): | |
| #Initialize output image | |
| output_image = np.zeros((512,512)) | |
| 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' | |
| workbook.save(save_path) | |
| # Create a DataFrame from the branch points mask | |
| df = pd.DataFrame(output_image) | |
| save_to_file(df, os.path.splitext(save_path)[0] + "_mask.xlsx", "statistics") | |
| 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 | |
| #excel_tips = 'tip_pts_mask_indices.xlsx' | |
| workbook.save(save_path) | |
| # Create a DataFrame from the branch points mask | |
| df = pd.DataFrame(output_image) | |
| save_to_file(df, os.path.splitext(save_path)[0] + "_mask.xlsx", "statistics") | |
| 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] = 255 | |
| output_image[branches > 0] = 128 | |
| 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' | |
| workbook.save(save_path) | |
| # Create a DataFrame from the branch points mask | |
| df = pd.DataFrame(output_image) | |
| save_to_file(df, os.path.splitext(save_path)[0] + "_mask.xlsx", "statistics") | |
| 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(os.path.join("features", "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(os.path.join("features", "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(os.path.join("features", "hog_transform.npz"),hog=output_image) | |
| elif input_type == "compute all": | |
| img1 = pcv.morphology.skeletonize(mask=gray_img) | |
| if input_choice == "compute_tips": | |
| 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 | |
| #excel_tips = 'tip_pts_mask_indices.xlsx' | |
| workbook.save(save_path) | |
| # Create a DataFrame from the branch points mask | |
| df = pd.DataFrame(output_image) | |
| save_to_file(df, os.path.splitext(save_path)[0] + "_mask.xlsx", "statistics") | |
| elif input_choice == "compute_branch": | |
| 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' | |
| workbook.save(save_path) | |
| # Create a DataFrame from the branch points mask | |
| df = pd.DataFrame(output_image) | |
| save_to_file(df, os.path.splitext(save_path)[0] + "_mask.xlsx", "statistics") | |
| 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) | |
| np.savez(os.path.join("features", "sift_descriptors.npz"), descriptors=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) | |
| np.savez(os.path.join("features", "lbp_transform.npz"),lbp=output_image) | |
| 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) | |
| np.savez(os.path.join("features", "hog_transform.npz"),hog=output_image) | |
| # 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 | |
| def flagging_callback(example, label): | |
| hf_writer.add(example, label) | |
| 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>" | |
| ) | |
| examples = [["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","sort","sift transform","lbp transform","hog transform","compute all"], label="Choose the operation to be performed"), | |
| gr.components.Dropdown(["compute_tips","compute_branch","compute_both","compute_sift","compute_lbp","compute_hog"],label="choose from compute all"), | |
| gr.inputs.Textbox(default="results.xlsx", label="Enter file name to save (.xlsx extension will be added automatically)"), | |
| ], | |
| outputs=gr.outputs.Image(type="pil", label="Processed Image"), | |
| description=body, | |
| layout="vertical", | |
| allow_flagging=False, | |
| allow_screenshot=False, | |
| theme=theme, | |
| examples=examples, | |
| flagging_callback=flagging_callback | |
| ) | |
| iface.launch() |