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File size: 1,719 Bytes
22388fc 1d828f9 22388fc 1d828f9 22388fc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import cv2
import numpy as np
# Lazy-loaded EasyOCR reader (only initialized when redact_names is called)
_reader = None
def _get_reader():
global _reader
if _reader is None:
import easyocr
_reader = easyocr.Reader(['en'], gpu=False)
return _reader
def redact_names(img: np.ndarray) -> np.ndarray:
"""
Scans the image for text. If it finds text, we apply cv2.inpaint
to seamlessly remove the detected text, mimicking "redaction" without a flat background block.
"""
# Create an initial mask of zeros
mask = np.zeros(img.shape[:2], dtype=np.uint8)
h, w = img.shape[:2]
# Run OCR on the image
results = _get_reader().readtext(img)
has_text = False
for (bbox, text, prob) in results:
# Bounding box points: top_left, top_right, bottom_right, bottom_left
(tl, tr, br, bl) = bbox
tl = (int(tl[0]), int(tl[1]))
br = (int(br[0]), int(br[1]))
tr = (int(tr[0]), int(tr[1]))
# Heuristic: Redact text only if it's in the typical name region
# e.g., Bottom 35% of the card, Left 65% of the card
if tl[1] > h * 0.65 and tr[0] < w * 0.65:
has_text = True
pad = 8 # Slightly wider pad for clean redaction
cv2.rectangle(mask, (max(0, tl[0]-pad), max(0, tl[1]-pad)), (min(w, br[0]+pad), min(h, br[1]+pad)), 255, -1)
if has_text:
# Inpaint to remove the text seamlessly using the nearby background
# Use cv2.INPAINT_TELEA or cv2.INPAINT_NS
result = cv2.inpaint(img, mask, inpaintRadius=7, flags=cv2.INPAINT_TELEA)
return result
return img
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