RB1SAMURAI
commented
1 year ago import cv2 import turtle import numpy as np from matplotlib import pyplot as plt import time
def find_closest(p): if len(positions) > 0: nodes = np.array(positions) distances = np.sum((nodes - p) ** 2, axis=1) i_min = np.argmin(distances) return positions[i_min] else: return None
def outline(): src_image = cv2.imread(image, 0) blurred = cv2.GaussianBlur(src_image, (7, 7), 0) th3 = cv2.adaptiveThreshold(blurred, maxValue=255, adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C, thresholdType=cv2.THRESH_BINARY, blockSize=9, C=2) return th3
image = 'mahadev1.jpeg' im = cv2.imread(image, 0) th3 = outline()
plt.imshow(th3) plt.axis('off') plt.tight_layout()
plt.show()
WIDTH = im.shape[1] HEIGHT = im.shape[0] print(WIDTH, HEIGHT)
CUTOFF_LEN = ((WIDTH + HEIGHT) / 2) / 60 # 60 threshold value iH, iW = np.where(th3 == [0]) iW = iW - WIDTH / 2 iH = -1 * (iH - HEIGHT / 2) positions = [list(iwh) for iwh in zip(iW, iH)]
win = turtle.Screen()
win.bgcolor('black')
t = turtle.Turtle() t.color("brown") t.shapesize(1) t.pencolor("gray30")
t.speed(0) turtle.tracer(0, 0) t.penup() t.goto(positions[0]) t.pendown()
time.sleep(3)
p = positions[0] while (p): p = find_closest(p) if p: current_pos = np.asarray(t.pos()) new_pos = np.asarray(p) length = np.linalg.norm(new_pos - current_pos) if length < CUTOFF_LEN: t.goto(p) turtle.update() else: t.penup() t.goto(p) t.pendown() positions.remove(p) else: p = None
turtle.done()
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