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Merged
angrave merged 1 commit into from
Mar 10, 2026
Merged

Add first frame #74

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26 changes: 11 additions & 15 deletions pkg/agent/tasks/lib/scenedetection/svm_poly3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -462,7 +462,7 @@ def find_match(curr, ref, width, height, index):

# If no features were found in either image, skip
if des1 is None or des2 is None or len(kp1) < 2 or len(kp2) < 2:
print(f"No descriptors found (des1={des1 is None}, des2={des2 is None}) at index {index}")
# print(f"No descriptors found (des1={des1 is None}, des2={des2 is None}) at index {index}")
return False

# FLANN parameters
Expand All @@ -483,15 +483,11 @@ def find_match(curr, ref, width, height, index):
if m.distance < DISTANCE_RATIO * n.distance:
good.append(m)

print("Matches: ", len(matches))
print("Good: ", len(good))
print("Good ratio: ", len(good)/len(matches))

# If fewer than MIN_GOOD found between frames, then it's unlikely
# they are scrolled versions of each other
MIN_GOOD_RATIO = 0.2 # Increase to require more matches for two frames to be considered related
if len(good)/len(matches) < MIN_GOOD_RATIO:
print("Not enough good matches to estimate motion")
# print("Not enough good matches to estimate motion")
return False

# Calculating distances for matched pairs
Expand All @@ -506,17 +502,13 @@ def find_match(curr, ref, width, height, index):
# If not enough agree, it's unlikely the frames are scrolled versions of each other
MIN_INLIER_RATIO = 0.3 # Increase to require more consistent motion between frames
if inlier_ratio < MIN_INLIER_RATIO:
print("Reject: not enough inliers")
# print("Not enough inliers")
return False

tx = M[0,2] # translation in x
ty = M[1,2] # translation in Y
# sx = np.sqrt(M[0,0]**2 + M[1,0]**2) # scale in x
sy = np.sqrt(M[0,1]**2 + M[1,1]**2) # scale in y

print("x-shift: ", tx)
print("y-shift: ", ty)
print("y-scale: ", sy)

if abs(sy - 1) > 0.01 or abs(ty) > 1 or abs(tx) > 1:
return True
Expand Down Expand Up @@ -548,12 +540,12 @@ def filter_scrolling(video_path, frame_cuts):
cap.set(cv2.CAP_PROP_POS_FRAMES, filtered_frame_cuts[0])
ret, frame = cap.read()
height, width, channels = frame.shape
reference_frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
reference_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

for i in range(1, len(frame_cuts)):
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_cuts[i])
ret, frame = cap.read()
curr_frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
curr_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# If reference frame is reasonably found within current frame, skip
if find_match(curr_frame, reference_frame, width, height, i):
Expand Down Expand Up @@ -837,7 +829,7 @@ def find_scenes(self, video_path):
start_time = perf_counter()

# 1. Enumerate candidates as subprocess and block until it completes
print(' >>>>> USING NEW SceneDetection Running Step 1/3 (subprocess): ' + video_path)
print(' >>>>> SceneDetection Running Step 1/3 (subprocess): ' + video_path)
(min_samples_between_cut, num_samples, num_frames, everyN, timestamps,
sim_structural, sim_structural_no_face, sim_ocr) = self.enumerate_scene_candidates_batch(video_path, start_time)

Expand Down Expand Up @@ -866,7 +858,7 @@ def find_scenes(self, video_path):
if samples_cut_candidates[i] >= samples_cut_candidates[i - 1] + min_samples_between_cut:
sample_cuts += [samples_cut_candidates[i]]

if num_samples > 1:
if num_samples > 1 and ((num_samples - 1) not in sample_cuts):
sample_cuts += [num_samples - 1]

# Now work in frames again. Make sure we are using regular ints (not numpy ints) other json serialization will fail
Expand All @@ -875,6 +867,10 @@ def find_scenes(self, video_path):
# Filter out frames differing only by scrolling
filtered_frame_cuts = filter_scrolling(video_path, frame_cuts)

# Readd the beginning of the video, if we've removed it
if 0 not in filtered_frame_cuts:
filtered_frame_cuts.insert(0, 0)

filtered_frame_cuts = [int(x) for x in filtered_frame_cuts]

# Finish up by calling helper method to cut scenes and run OCR
Expand Down