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9b4f4f7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import functools
import numpy as np
import cv2
import math
arr = np.array
def image_scale(pts, scale):
"""scale to original image size"""
def __loop(x, y): return [x[0] * y, x[1] * y]
return list(map(functools.partial(__loop, y=1/scale), pts))
def image_resize(img, height=500):
"""resize image to same normalized area (height**2)"""
pixels = height * height
shape = list(np.shape(img))
scale = math.sqrt(float(pixels)/float(shape[0]*shape[1]))
shape[0] *= scale
shape[1] *= scale
img = cv2.resize(img, (int(shape[1]), int(shape[0])))
img_shape = np.shape(img)
return img, img_shape, scale
def image_transform(img, points, square_length=150):
"""crop original image using perspective warp"""
board_length = square_length * 8
def __dis(a, b): return np.linalg.norm(arr(a)-arr(b))
def __shi(seq, n=0): return seq[-(n % len(seq)):] + seq[:-(n % len(seq))]
best_idx, best_val = 0, 10**6
for idx, val in enumerate(points):
val = __dis(val, [0, 0])
if val < best_val:
best_idx, best_val = idx, val
pts1 = np.float32(__shi(points, 4 - best_idx))
pts2 = np.float32([[0, 0], [board_length, 0],
[board_length, board_length], [0, board_length]])
M = cv2.getPerspectiveTransform(pts1, pts2)
W = cv2.warpPerspective(img, M, (board_length, board_length))
return W
def crop(img, pts, scale):
"""crop using 4 points transform"""
pts_orig = image_scale(pts, scale)
img_crop = image_transform(img, pts_orig)
return img_crop
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