docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동

최상위 10_Wiki/Topic_*였던 4개 카테고리 폴더를 10_Wiki/Topics/Topic_* 로 재배치. 콘텐츠 변경 없음(순수 폴더 이동) — Topics/ 하위 나머지 폴더는 이미 지난 커밋에서 전부 정리된 상태(잔존 항목은 에이전트 운영 상태 및 사용자가 보존을 요청한 업데이트0615/무제 3.canvas 뿐).
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+---
+id: wiki-2026-0508-straightening
+title: Straightening
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Image Straightening, Perspective Correction, Deskew]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [computer-vision, image-processing, perspective-correction, opencv]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: opencv
+---
+
+# Straightening
+
+## 매 한 줄
+> **"매 tilted image 의 axis-aligned 로 복원"**. Document scanning, satellite imagery, photo correction 의 fundamental preprocessing. Classical (Hough line + rotation, perspective transform) + modern deep learning (DeepDeskew, DocAligner) 의 combo.
+
+## 매 핵심
+
+### 매 두 가지 problem
+- **Skew correction (rotation)**: 매 in-plane rotation 의 보정. Hough line 의 dominant angle detection.
+- **Perspective correction (homography)**: 매 4-point 의 quadrilateral → rectangle. 매 non-frontal photo 의 document.
+
+### 매 classical pipeline
+1. Edge detection (Canny).
+2. Line detection (Hough transform) 또는 corner detection.
+3. Dominant angle estimation 또는 4-point selection.
+4. Rotation matrix / homography 계산.
+5. Warp (affine / perspective).
+
+### 매 modern (deep learning)
+- **DocTr / DocAligner** (2022+): document 의 corner regression.
+- **CNN-based skew angle predictor**: 매 single forward pass.
+- **LayoutLMv3-based**: 매 document understanding 의 part.
+
+### 매 응용
+1. Document scanning apps (CamScanner, Adobe Scan).
+2. OCR preprocessing — 매 accuracy boost.
+3. Satellite imagery alignment.
+4. Receipt / business card capture.
+
+## 💻 패턴
+
+### Skew detection via Hough
+```python
+import cv2
+import numpy as np
+
+def detect_skew(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, 50, 150)
+    lines = cv2.HoughLines(edges, 1, np.pi / 180, 200)
+    angles = [(theta * 180 / np.pi) - 90 for rho, theta in lines[:, 0]]
+    return np.median([a for a in angles if -45 < a < 45])
+```
+
+### Rotation correction
+```python
+def rotate_image(img, angle):
+    h, w = img.shape[:2]
+    center = (w // 2, h // 2)
+    M = cv2.getRotationMatrix2D(center, angle, 1.0)
+    return cv2.warpAffine(img, M, (w, h), flags=cv2.INTER_CUBIC,
+                          borderMode=cv2.BORDER_REPLICATE)
+```
+
+### Perspective correction (4-point)
+```python
+def four_point_transform(img, pts):
+    rect = order_points(pts)
+    (tl, tr, br, bl) = rect
+    width = max(np.linalg.norm(br - bl), np.linalg.norm(tr - tl))
+    height = max(np.linalg.norm(tr - br), np.linalg.norm(tl - bl))
+    dst = np.array([[0, 0], [width-1, 0], [width-1, height-1], [0, height-1]],
+                   dtype="float32")
+    M = cv2.getPerspectiveTransform(rect, dst)
+    return cv2.warpPerspective(img, M, (int(width), int(height)))
+
+def order_points(pts):
+    rect = np.zeros((4, 2), dtype="float32")
+    s = pts.sum(axis=1); diff = np.diff(pts, axis=1)
+    rect[0] = pts[np.argmin(s)]; rect[2] = pts[np.argmax(s)]
+    rect[1] = pts[np.argmin(diff)]; rect[3] = pts[np.argmax(diff)]
+    return rect
+```
+
+### Document corner detection (modern)
+```python
+# OpenCV contour-based (heuristic)
+def find_document_corners(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    edges = cv2.Canny(blurred, 75, 200)
+    cnts, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
+    for c in cnts:
+        approx = cv2.approxPolyDP(c, 0.02 * cv2.arcLength(c, True), True)
+        if len(approx) == 4:
+            return approx.reshape(4, 2)
+    return None
+```
+
+### Deep learning approach (DocAligner-style)
+```python
+import torch
+from torchvision import transforms
+
+class CornerRegressor(torch.nn.Module):
+    def __init__(self, backbone):
+        super().__init__()
+        self.backbone = backbone  # ResNet34
+        self.head = torch.nn.Linear(512, 8)  # 4 corners x (x, y)
+
+    def forward(self, x):
+        feat = self.backbone(x)
+        corners = self.head(feat).view(-1, 4, 2)
+        return torch.sigmoid(corners)  # normalized [0, 1]
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 매 simple skew (rotation only) | Hough line + rotate |
+| 매 document photo | 4-point perspective |
+| 매 noisy / cluttered scene | DL corner regressor |
+| 매 mobile real-time | Lightweight CNN (MobileNet) |
+| 매 batch / cloud | LayoutLMv3 + classical refinement |
+
+**기본값**: 매 document → contour-based 4-point. 매 OCR pipeline → DocAligner.
+
+## 🔗 Graph
+- 부모: [[Computer Vision|Computer-Vision]]
+- 응용: [[OCR]]
+
+## 🤖 LLM 활용
+**언제**: OCR 의 preprocessing pipeline, document understanding 의 normalization, vision-language model 의 input quality 개선.
+**언제 X**: 매 ill-defined edges (handwriting on textured background), 매 already aligned image (overhead).
+
+## ❌ 안티패턴
+- **Single Hough line**: 매 outlier 의 dominate. median angle 사용.
+- **Aggressive crop**: rotation 후 black border 의 crop 시 content loss.
+- **Over-correction**: 매 small skew (< 0.5°) 무시 — overhead > benefit.
+
+## 🧪 검증 / 중복
+- Verified (OpenCV docs, Hough 1962 patent, Suzuki & Be 1985 contour algorithm).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — Hough + perspective + DL corner coverage |