def run(self):
self.results = np.array([])
# Let subscriber know the total
self.startProgress.emit(config.NISSL_COUNT)
# For updating the progress in the UI
self.total = 1
# Compute SIFT for region before starting
self.kp1, self.des1 = feature.extract_sift(self.im)
# Set multithreading if capable
if config.MULTITHREAD:
pool = ThreadPool(processes = cv2.getNumberOfCPUs())
else:
pool = ThreadPool(processes = 1)
# Total number of images to compare against
nissl = range(1, config.NISSL_COUNT + 1)
# Begin mapping process
pool.map(self.process_level, nissl)
pool.close()
pool.join()
# Tell UI the results
self.endProgress.emit(self.results)
def match(im):
atlas = feature.im_read('scripts_testing/plate-34.jpg')
if affine:
kp, des = feature.extract_sift(im)
kp2, des2 = feature.extract_sift(atlas)
else:
kp, des = sift.detectAndCompute(im, None)
kp2, des2 = sift.detectAndCompute(atlas, None)
match = feature.match(im, kp, des, atlas, kp2, des2)
print("Matches: ", len(match.matches))
print("Inliers: ", match.inlier_count)
plt.figure(figsize=(10,10))
plt.imshow(match.result)
plt.figure(figsize=(10,10))
plt.imshow(match.result2)
return match.H
def draw_kp(im):
if affine:
kp, des = feature.extract_sift(im)
else:
kp = sift.detect(im)
print("Keypoints: ", len(kp))
im_kp = cv2.drawKeypoints(im, kp, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
plt.figure(figsize=(10,10))
plt.imshow(im_kp)
def set_im_region(self, im_region):
logger.info("Image Region Shape: {0}", im_region.shape)
w, h, c = im_region.shape
# Check if the user would like to see the region keypoints
if config.UI_SHOW_KP:
kp, des = feature.extract_sift(im_region)
import cv2
im_region = cv2.drawKeypoints(
im_region,
kp,
None,
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
logger.info("Keypoints drawn for region")
# Check if we should save the region for testing
if config.UI_SAVE_REGION:
feature.im_write('region.png', im_region)
# Reduce the size of images to a reasonable range
import scipy.misc as misc
reduction_percent = int(config.RESIZE_WIDTH / w * 100)
im_region = misc.imresize(im_region, reduction_percent)
logger.info("Resized region to {0}", im_region.shape)
# Save the original selection for actual matching
self.region = im_region
self.canvas_region.imshow(im_region)
self.canvas_input.clear_corners()
self.btn_find_match.setEnabled(True)
self.slider_ratio_test.setEnabled(True)
if config.UI_WARP:
self.btn_warp.setEnabled(True)
self.btn_reset.setEnabled(True)
self.slider_warp_points.setEnabled(True)
self.slider_warp_disp.setEnabled(True)
if config.UI_ANGLE:
self.slider_angle.setEnabled(True)
s_label = s_data['labels']
pw_data = np.load('atlas_pw.npz')
pw_im = pw_data['images']
pw_label = pw_data['labels']
# Prepare SM
similarity_matrix = np.zeros((len(s_im), len(pw_im)))
print("Similarity Matrix Shape", similarity_matrix.shape)
# Precompute SIFT descriptors for atlas
kp = []
des = []
for j in range(len(pw_im)):
im2 = pw_im[j]
kp2, des2 = feature.extract_sift(im2)
kp.append(kp2)
des.append(des2)
# Begin matching
start_time = timer()
for i in range(similarity_matrix.shape[0]):
print("Matching S", s_label[i])
im1 = s_im[i]
kp1, des1 = feature.extract_sift(im1)
for j in range(similarity_matrix.shape[1]):
im2 = pw_im[j]
kp2 = kp[j]
des2 = des[j]