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from matplotlib.widgets import RectangleSelector
import matplotlib.pyplot as plt
import numpy as np
def patch_match(img, patch_size=3, iterations=1):
height, width, _ = img.shape
offsets = np.zeros((height, width, 2), dtype=np.int32)
def random_offsets():
return np.random.randint(-patch_size, patch_size + 1, size=(height, width, 2))
def distance(patch1, patch2):
return np.sum((patch1 - patch2) ** 2)
def get_patch(x, y):
return img[max(0, y):min(height, max(0, y) + patch_size), max(0, x):min(width, max(0, x) + patch_size)]
offsets = random_offsets()
for _ in range(iterations):
for y in range(patch_size,height-patch_size*2):
for x in range(patch_size,width-patch_size*2):
best_offset = offsets[y, x]
best_distance = distance(get_patch(x, y), get_patch(x + best_offset[0], y + best_offset[1]))
for dy in range(-1, 2):
for dx in range(-1, 2):
if dx == 0 and dy == 0:
continue
new_offset = best_offset + [dx, dy]
if (new_offset[0]+x>width-patch_size or new_offset[1]+y>height-patch_size):
continue
new_distance = distance(get_patch(x, y), get_patch(x + new_offset[0], y + new_offset[1]))
if new_distance < best_distance:
best_distance = new_distance
best_offset = new_offset
offsets[y, x] = best_offset
result = np.zeros_like(img)
for y in range(height):
for x in range(width):
offset = offsets[y, x]
result[y, x] = img[(y + offset[1]) % height, (x + offset[0]) % width]
return result
# Load the image using matplotlib
img = plt.imread('boat.png')
def onselect(eclick, erelease):
x1, y1 = eclick.xdata, eclick.ydata
x2 = x1 + 150
x2, y2 = erelease.xdata, erelease.ydata
avg_color = np.mean(img, axis=(0, 1))
img_copy = np.copy(img)
img_copy[int(y1):int(y2), int(x1):int(x2)] = avg_color
res = patch_match(img_copy)
ax.imshow(res)
plt.draw()
print("drawed")
#ax.imshow(img_copy)
#plt.draw()
fig, ax = plt.subplots()
ax.imshow(img)
toggle_selector = RectangleSelector(ax, onselect, useblit=True,
button=[1], minspanx=5, minspany=5, spancoords='pixels',
interactive=True)
plt.axis('off')
plt.show()