def run(self):
print("Processing No.", self.index, "image")
time1 = time.time()
sigma = 1.6
num_intervals = 3
assumed_blur = 0.5
image_border_width = 5
image = self.image.astype('float32')
base_image = SIFT.generateBaseImage(image, sigma, assumed_blur)
# print("Processing No.", self.index, "step1.")
num_octaves = SIFT.computeNumberOfOctaves(base_image.shape)
# print("Processing No.", self.index, "step2.")
gaussian_kernels = SIFT.generateGaussianKernels(sigma, num_intervals)
# print("Processing No.", self.index, "step3.")
gaussian_images = SIFT.generateGaussianImages(base_image, num_octaves,
gaussian_kernels)
# print("Processing No.", self.index, "step4.")
dog_images = SIFT.generateDoGImages(gaussian_images)
# print("Processing No.", self.index, "step5.")
keypoints = SIFT.findScaleSpaceExtrema(gaussian_images, dog_images,
num_intervals, sigma,
image_border_width)
# print("Processing No.", self.index, "step6.")
keypoints = SIFT.removeDuplicateKeypoints(keypoints)
# print("Processing No.", self.index, "step7.")
keypoints = SIFT.convertKeypointsToInputImageSize(keypoints)
# print("Processing No.", self.index, "step8.")
descriptors = SIFT.generateDescriptors(keypoints, gaussian_images)
# print("Processing No.", self.index, "finished.")
threadLock.acquire()
# 释放锁，开启下一个线程
self.list.append([self.index, keypoints, descriptors])
threadLock.release()
time2 = time.time()
c = time2 - time1
print("Processing No.", self.index, " costs ", c)
