import pickle
import numpy as np
import cv2
import CVAnalysis
if __name__ == "__main__":
#=====[ Step 1: switch to directory ]=====
os.chdir ('../data/toy_images')
filenames = [
'random1.jpg',
'random2.jpg',
'random3.jpg',
'random4.jpg',
'random5.jpg',
'random6.jpg',
'random7.jpg',
]
images = [cv2.imread (f) for f in filenames]
sift_desc = []
for image in images:
hc = CVAnalysis.get_harris_corners (image)
desc = CVAnalysis.get_sift_descriptors (image, hc)
sift_desc.append (desc)
features = np.concatenate (sift_desc, 0)
#==========[ Step 4: draw image ]==========
cv2.namedWindow('DISPLAY')
cv.SetMouseCallback('DISPLAY', on_mouse, param=harris_corners)
#==========[ Step 4: have user mark keypoints ]==========
while True:
disp_image = cv2.drawKeypoints(image,
harris_corners,
color=(0, 0, 255))
disp_image = cv2.drawKeypoints(disp_image,
corner_keypoints,
color=(255, 0, 0))
cv2.imshow('DISPLAY', disp_image)
key = cv2.waitKey(30)
if key == 27:
break
#==========[ Step 5: get descriptors for each corner point ]==========
print_status('MarkImage', 'getting SIFT descriptors for clicked corners')
desc = CVAnalysis.get_sift_descriptors(image, corner_keypoints)
corner_sift_desc = desc
#==========[ Step 6: construct BoardImage ]==========
print_status('MarkImage', 'constructing BoardImage object')
board = Board(image=image,
name=image_name,
board_points=corner_board_points,
image_points=corner_image_points,
sift_desc=corner_sift_desc)
for i in range(len(points_grid)):
points_grid[i].sort(key=lambda x: x[0])
return points_grid
if __name__ == '__main__':
#=====[ Step 1: read in image ]=====
image = cv2.imread('../data/p2/1.jpg')
#=====[ Step 2: get harris corners ]=====
hc = CVAnalysis.get_harris_corners(image)
#=====[ Step 3: classify them ]=====
sd = CVAnalysis.get_sift_descriptors(image, hc)
clf = pickle.load(open('../data/classifiers/corner_classifier.clf', 'r'))
predictions = clf.predict(sd)
idx = (predictions == 1)
corners = [c for c, i in zip(hc, idx) if i]
corners = avg_close_points(corners)
#=====[ Step 3: get black image, fill in corners ]=====
corners_img = np.zeros(image.shape[:2], dtype=np.uint8)
for corner in corners:
corners_img[int(corner[1])][int(corner[0])] = 255
#=====[ Step 4: apply hough transform to get lines ]=====
lines = cv2.HoughLines(corners_img, 3, np.pi / 180, 6)
lines = avg_close_lines(lines)
lines = filter_by_slope(lines)
cv2.namedWindow ('DISPLAY')
cv.SetMouseCallback ('DISPLAY', on_mouse, param=harris_corners)
#==========[ Step 4: have user mark keypoints ]==========
while True:
disp_image = cv2.drawKeypoints (image, harris_corners, color=(0, 0, 255))
disp_image = cv2.drawKeypoints (disp_image, corner_keypoints, color=(255, 0, 0))
cv2.imshow ('DISPLAY', disp_image)
key = cv2.waitKey(30)
if key == 27:
break
#==========[ Step 5: get descriptors for each corner point ]==========
print_status ('MarkImage', 'getting SIFT descriptors for clicked corners')
desc = CVAnalysis.get_sift_descriptors (image, corner_keypoints)
corner_sift_desc = desc
#==========[ Step 6: construct BoardImage ]==========
print_status ('MarkImage', 'constructing BoardImage object')
board = Board (
image=image,
name=image_name,
board_points=corner_board_points,
image_points=corner_image_points,
sift_desc = corner_sift_desc
)
