Benjamin_Boenisch/breakpilot-lehrer
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix(ocr-pipeline): clamp gap detection to img_h to avoid dewarp padding
Some checks failed
CI / go-lint (push) Has been skipped
Details
CI / python-lint (push) Has been skipped
Details
CI / nodejs-lint (push) Has been skipped
Details
CI / test-go-school (push) Successful in 26s
Details
CI / test-go-edu-search (push) Successful in 27s
Details
CI / test-python-klausur (push) Failing after 1m46s
Details
CI / test-python-agent-core (push) Successful in 17s
Details
CI / test-nodejs-website (push) Successful in 16s
Details

Browse Source 
The inverted image can be taller than img_h after dewarp shear
correction, causing footer_y to be detected outside the visible page.
Now clamps the horizontal projection to actual_h = min(inv.shape[0], img_h).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
2026-03-02 17:06:58 +01:00
parent c8981423d4
commit f1fcc67357
1 changed files with 10 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

17
klausur-service/backend/cv_vocab_pipeline.py
View File

@@ -2545,12 +2545,15 @@ def _detect_header_footer_gaps(
HEADER_FOOTER_ZONE = 0.20 HEADER_FOOTER_ZONE = 0.20
GAP_MULTIPLIER = 2.0 GAP_MULTIPLIER = 2.0
# Step 1: Horizontal projection over full image width # Step 1: Horizontal projection — clamp to img_h to avoid dewarp padding
h_proj = np.sum(inv, axis=1).astype(float) actual_h = min(inv.shape[0], img_h)
h_proj_norm = h_proj / (img_w * 255) if img_w > 0 else h_proj roi = inv[:actual_h, :]
h_proj = np.sum(roi, axis=1).astype(float)
proj_w = roi.shape[1]
h_proj_norm = h_proj / (proj_w * 255) if proj_w > 0 else h_proj
# Step 2: Smoothing # Step 2: Smoothing
kernel_size = max(3, img_h // 200) kernel_size = max(3, actual_h // 200)
if kernel_size % 2 == 0: if kernel_size % 2 == 0:
kernel_size += 1 kernel_size += 1
h_smooth = np.convolve(h_proj_norm, np.ones(kernel_size) / kernel_size, mode='same') h_smooth = np.convolve(h_proj_norm, np.ones(kernel_size) / kernel_size, mode='same')
@@ -2561,7 +2564,7 @@ def _detect_header_footer_gaps(
gap_threshold = max(median_density * 0.15, 0.003) gap_threshold = max(median_density * 0.15, 0.003)
in_gap = h_smooth < gap_threshold in_gap = h_smooth < gap_threshold
MIN_GAP_HEIGHT = max(3, img_h // 500) MIN_GAP_HEIGHT = max(3, actual_h // 500)
# Step 4: Collect contiguous gaps # Step 4: Collect contiguous gaps
raw_gaps: List[Tuple[int, int]] = [] raw_gaps: List[Tuple[int, int]] = []
@@ -2590,8 +2593,8 @@ def _detect_header_footer_gaps(
large_gap_threshold = median_gap * GAP_MULTIPLIER large_gap_threshold = median_gap * GAP_MULTIPLIER
# Step 6: Find largest qualifying gap in header / footer zones # Step 6: Find largest qualifying gap in header / footer zones
header_zone_limit = int(img_h * HEADER_FOOTER_ZONE) header_zone_limit = int(actual_h * HEADER_FOOTER_ZONE)
footer_zone_start = int(img_h * (1.0 - HEADER_FOOTER_ZONE)) footer_zone_start = int(actual_h * (1.0 - HEADER_FOOTER_ZONE))
header_y: Optional[int] = None header_y: Optional[int] = None
footer_y: Optional[int] = None footer_y: Optional[int] = None