Upload app.py

app.py

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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
+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()
+