app.py

import gradio as gr
import cv2
import numpy as np

def preprocess(img):
    # Convert to grayscale
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    cv2.imwrite("1_gray.png", img_gray)

    # Blur the image
    img_blur = cv2.GaussianBlur(img_gray, (5, 5), 1)
    cv2.imwrite("2_blur.png", img_blur)

    # Detect edges with Canny
    img_canny = cv2.Canny(img_blur, 50, 50)
    cv2.imwrite("3_canny.png", img_canny)

    # Dilate the edges
    kernel = np.ones((3, 3))
    img_dilate = cv2.dilate(img_canny, kernel, iterations=2)
    cv2.imwrite("4_dilate.png", img_dilate)

    # Erode the dilated edges
    img_erode = cv2.erode(img_dilate, kernel, iterations=1)
    cv2.imwrite("5_erode.png", img_erode)

    return img_erode

def find_tip(points, convex_hull):
    length = len(points)
    indices = np.setdiff1d(range(length), convex_hull)

    for i in range(2):
        j = indices[i] + 2
        if j > length - 1:
            j = length - j
        if np.all(points[j] == points[indices[i - 1] - 2]):
            return tuple(points[j])






def infer(image_in):
    img = cv2.imread(image_in)

    contours, hierarchy = cv2.findContours(preprocess(img), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

    for cnt in contours:
        peri = cv2.arcLength(cnt, True)
        approx = cv2.approxPolyDP(cnt, 0.025 * peri, True)
        hull = cv2.convexHull(approx, returnPoints=False)
        sides = len(hull)

        if 6 > sides > 3 and sides + 2 == len(approx):
            arrow_tip = find_tip(approx[:,0,:], hull.squeeze())
            if arrow_tip:
                cv2.drawContours(img, [cnt], -1, (0, 255, 0), 3)
                cv2.circle(img, arrow_tip, 3, (0, 0, 255), cv2.FILLED)

    cv2.imwrite("Image_result.png", img)

    return "Image_result.png"

gr.Interface(
    fn=infer,
    inputs=gr.Image(
        sources=["upload"],
        type="filepath"
    ),
    outputs=gr.Image()
).launch()