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771af3a | 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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | import easyocr
import easyocr.easyocr
import sys
import json
import os
from PIL import Image
import io
# Fix EasyOCR bug
easyocr.easyocr.corrupt_msg = "Model error"
def process_image(reader, image_path):
try:
if not os.path.exists(image_path):
return {"error": f"Path not found: {image_path}"}
# Single-pass OCR is much faster and usually accurate enough for Deep Learning models
results = reader.readtext(image_path, detail=1)
if not results:
return {"error": "No text detected"}
symbols = []
for (bbox, text, prob) in results:
text = text.strip().upper()
clean_text = "".join([c for c in text if c.isalpha()])
if not clean_text: continue
x_center = (bbox[0][0] + bbox[2][0]) / 2
y_center = (bbox[0][1] + bbox[2][1]) / 2
if len(clean_text) > 1:
# Approximate positions for concatenated chars
w = bbox[2][0] - bbox[0][0]
char_w = w / len(clean_text)
for i, char in enumerate(clean_text):
symbols.append({
"text": char,
"x": bbox[0][0] + (i + 0.5) * char_w,
"y": y_center
})
else:
symbols.append({"text": clean_text, "x": x_center, "y": y_center})
if not symbols: return []
# Find 8 distinct lanes for X and Y
def get_lanes(coords, num_lanes=8):
coords.sort()
if not coords: return []
lanes = []
# Simple clustering: divide range into 8 buckets
mi, ma = min(coords), max(coords)
if ma == mi: return [mi]
bucket_size = (ma - mi) / (num_lanes - 1) if num_lanes > 1 else 1
for i in range(num_lanes):
center = mi + i * bucket_size
lanes.append(center)
return lanes
xs = [s["x"] for s in symbols]
ys = [s["y"] for s in symbols]
x_lanes = get_lanes(xs, 8)
y_lanes = get_lanes(ys, 8)
grid = [[" " for _ in range(8)] for _ in range(8)]
for s in symbols:
# Map to nearest lane
r = min(range(8), key=lambda i: abs(s["y"] - y_lanes[i]))
c = min(range(8), key=lambda i: abs(s["x"] - x_lanes[i]))
grid[r][c] = s["text"]
final_symbols = []
for r in range(8):
for c in range(8):
if grid[r][c] != " ":
final_symbols.append({"text": grid[r][c], "r": r, "c": c})
return final_symbols
except Exception as e:
return {"error": str(e)}
def main():
# Initialize reader ONCE
try:
reader = easyocr.Reader(['en'], gpu=False, verbose=False)
# Signal ready
print("READY", flush=True)
except Exception as e:
print(json.dumps({"error": f"Init failed: {str(e)}"}), flush=True)
return
# Listen for image paths on stdin
while True:
line = sys.stdin.readline()
if not line:
break
image_path = line.strip()
if not image_path:
continue
result = process_image(reader, image_path)
print(json.dumps(result), flush=True)
if __name__ == "__main__":
main()
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