import gradio as gr import cv2 import json import numpy as np import glob2 as glob from PIL import Image from pyAAMED import pyAAMED title = """

Arc Adjacency Matrix based Fast Ellipse Detection

Gitub """ def detect_ellipses(img_path): imgC = cv2.imread(img_path) imgG = cv2.cvtColor(imgC, cv2.COLOR_BGR2GRAY) ammed_size = 600 iheight, iwidth = imgG.shape imax = max(iheight, iwidth) iscale = (ammed_size - 1) / imax is_landscape = iwidth >= iheight if is_landscape: iw = imax * iscale ih = iheight * iscale else: iw = iwidth * iscale ih = imax * iscale imgG = cv2.resize(imgG, (int(iw), int(ih))) aamed = pyAAMED(ammed_size, ammed_size) aamed.setParameters(3.1415926/3, 3.4, 0.77) print(ammed_size, iw, ih, imgG.shape) result = aamed.run_AAMED(imgG) imgN = imgG.copy() if len(result) > 0: imgN = cv2.cvtColor(imgN, cv2.COLOR_GRAY2RGB) for e in result: x, y, w, h, a, _ = e imgN = cv2.ellipse(imgN, (y, x), (int(h / 2), int(w / 2)), -a, 0, 360, (0, 255, 0), 3) # image, center_coordinates, axesLength, angle, startAngle, endAngle, color, thickness # from CPP code: # temp.center.x = detEllipses[i].center.y; # temp.center.y = detEllipses[i].center.x; # temp.size.height = detEllipses[i].size.width; # temp.size.width = detEllipses[i].size.height; # temp.angle = -detEllipses[i].angle; result = result.tolist() else: result = [] return [Image.fromarray(imgN), json.dumps(result)] examples = [] test_files = glob.glob('./examples/*.jpg') + glob.glob('./examples/*.png') for f in test_files: examples = examples + [[f]] gr.Interface( fn=detect_ellipses, inputs=gr.Image(label="Upload image with ellipses", type="filepath"), outputs=[ gr.Image(type="pil", label="Detected ellipses"), gr.Textbox(label="Detected ellipses") ], title=title, examples=examples, allow_flagging='never' ).launch( debug=True, server_name="0.0.0.0", server_port=7860 )