def find_scene_boundary(self, path, cur_video, start, end, k):
starttime = datetime.now()
pre_img = imagetool.fast_readrgbfile(path + cur_video[start])
pre_score = -1
scene_list = []
scene_start = 0
scene_end = 0
for i in range(start + k, end, k):
cur_img = imagetool.fast_readrgbfile(path + cur_video[i])
cur_score = imagetool.get_img_diff(pre_img, cur_img)
print("Similarity score (img{}, img{}): {}".format(
i - k, i, cur_score))
if (k == 1):
if (cur_score < 0.5):
scene_list.append([scene_start, i - 1])
scene_start = i
elif (pre_score != -1 and cur_score < 0.6
and abs(cur_score - pre_score) >= 0.2):
scene_end = self.find_conner(path, cur_video, i - k, i)
scene_list.append([scene_start, scene_end])
scene_start = scene_end + 1
i = scene_start
pre_img = imagetool.fast_readrgbfile(path + cur_video[i])
pre_score = -1
continue
pre_img = cur_img
pre_score = cur_score
if (k > 1 and scene_end < end - 1):
scene_list.append([scene_start, end - 1])
print("Time: {}".format(datetime.now() - starttime))
return scene_list
def test_get_similar_scores(path1, path2):
img1 = imagetool.readrgbfile(path1)
img2 = imagetool.readrgbfile(path2)
score = imagetool.get_img_diff(img2, img1)
cv2.imshow("img1", cv2.cvtColor(img1, cv2.COLOR_RGB2BGR))
cv2.imshow("img2", cv2.cvtColor(img2, cv2.COLOR_RGB2BGR))
print("Similar score: {}".format(score))
cv2.waitKey(0)
cv2.destroyAllWindows()
def video_postprocessing(self, videos_scene, key_frames):
print("---- Begin post-processing ----")
final_videos_scene = []
final_key_frames = []
# case 1: compare(1, 3) if similar merge(1...3) otherwise compare(1, 2)
offset = 0
for video in videos_scene:
i = 0
cur_video_scene = []
while (i < len(video)):
# each video frames scan neighbor and merge similar scene
inc = 0
if (i + 2 < len(video)):
cur_img = key_frames[i + offset]
next_img = key_frames[i + 1 + offset]
next2_img = key_frames[i + 2 + offset]
similar2 = imagetool.get_img_diff(cur_img, next2_img)
similar1 = imagetool.get_img_diff(cur_img, next_img)
print("similar1 {} similar2 {}".format(similar1, similar2))
if similar2 >= 0.5:
cur_video_scene.append([video[i][0], video[i + 2][1]])
print("merge {} with {}".format(
i + offset, i + 2 + offset))
inc = 3
elif similar1 >= 0.5:
cur_video_scene.append([video[i][0], video[i + 1][1]])
print("merge {} with {}".format(
i + offset, i + 1 + offset))
inc = 2
else:
cur_video_scene.append([video[i][0], video[i][1]])
inc = 1
else:
cur_video_scene.append([video[i][0], video[i][1]])
inc = 1
final_key_frames.append(key_frames[i + offset])
i += inc
final_videos_scene.append(cur_video_scene)
offset += len(video)
print(final_videos_scene)
return final_videos_scene, final_key_frames
def find_conner(self, path, cur_video, start, end):
pre_img = imagetool.fast_readrgbfile(path + cur_video[start])
pre_score = 1
min_score = 1
min_index = end
for i in range(start + 1, end + 1):
cur_img = imagetool.fast_readrgbfile(path + cur_video[i])
cur_score = imagetool.get_img_diff(pre_img, cur_img)
# print("Post-processing (img{}, img{}): {}".format(i-1, i, cur_score))
if (min_score > cur_score):
min_score = cur_score
min_index = i - 1
pre_img = cur_img
pre_score = cur_score
return min_index