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# coding=utf-8
# 画像を読み込んでセグメント(接続順に並んだ輪郭点)を出力するスクリプト
import numpy as np
import cv2
def get_hand_image_bg(imgfile):
img = cv2.imread(imgfile)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, img = cv2.threshold(img,
200,
255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
return img
def all_plus(img):
for i in range(len(img)):
for k in range(len(img[0])):
if img[i][k] <= 0:
img[i][k] = 0
else:
img[i][k] = 1
return img
def segmentation(edge):
ret = []
copy = edge
for i in range(len(edge)):
copy = sorted(copy, key=lambda vv: np.linalg.norm(vv - copy[0]))
ret.append(copy.pop(0))
return ret
def get_edge(img):
edge = []
for x in range(1, len(img) - 1):
for y in range(1, len(img[0]) - 1):
if img[x, y] == 1:
edge.append(np.array([x, y]))
return segmentation(edge)
def get_degree(v1, v2):
return (360 / (2 * np.pi)) * np.arccos(np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)))
def remove_isolated_point(img):
n8 = np.array([[1, 1, 1],
[1, 1, 1],
[1, 1, 1]],
np.uint8)
img = cv2.erode(img,
n8,
iterations=1)
return cv2.dilate(img,
n8,
iterations=1)
def get_segment(img_filename):
img = get_hand_image_bg(img_filename)
height = img.shape[0]
width = img.shape[1]
img = cv2.resize(img, (300, 300 * height / width))
img = remove_isolated_point(img)
img = cv2.Laplacian(img, cv2.CV_32F)
img = all_plus(img)
return get_edge(img)
if __name__ == '__main__':
for i in range(20):
#data = get_feature("./train_data/" + str(i) + ".png")
edge = get_segment("./test_data/" + str(i) + ".JPG")
#print ','.join(map(lambda v: str(v[0]) + ',' + str(v[1]), data))
print ','.join(map(lambda x: str(x[0]) +","+str(x[1]), edge))
#cv2.waitKey(0)
#cv2.destroyAllWindows()
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