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import os
import cv2
import numpy as np
from ultralytics import YOLO
import tensorflow as tf
import tempfile
# ----------------- Helper Functions -----------------
def order_points(pts: np.ndarray) -> np.ndarray:
"""
Orders 4 points in the order: top-left, top-right, bottom-right, bottom-left.
"""
rect = np.zeros((4, 2), dtype="float32")
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)] # top-left
rect[2] = pts[np.argmax(s)] # bottom-right
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)] # top-right
rect[3] = pts[np.argmax(diff)] # bottom-left
return rect
def crop_regions(image: np.ndarray, res, conf_threshold: float=0.6) -> list:
"""
Crops and deskews regions based on OBB detection, returning (crop, x_min).
Uses perspective transform to rotate the region upright.
"""
regions = []
if hasattr(res, 'obb') and res.obb is not None:
polys = res.obb.xyxyxyxy.cpu().numpy()
confs = res.obb.conf.cpu().numpy()
for poly, conf in zip(polys, confs):
if conf < conf_threshold:
continue
pts = poly.reshape(4, 2).astype(np.float32)
# Order points and compute destination rectangle
rect = order_points(pts)
(tl, tr, br, bl) = rect
widthA = np.linalg.norm(br - bl)
widthB = np.linalg.norm(tr - tl)
maxW = int(max(widthA, widthB))
heightA = np.linalg.norm(tr - br)
heightB = np.linalg.norm(tl - bl)
maxH = int(max(heightA, heightB))
dst = np.array([
[0, 0],
[maxW - 1, 0],
[maxW - 1, maxH - 1],
[0, maxH - 1]
], dtype="float32")
# Perspective transform
M = cv2.getPerspectiveTransform(rect, dst)
warp = cv2.warpPerspective(image, M, (maxW, maxH))
x_min = int(rect[:, 0].min())
regions.append((warp, x_min))
return regions
# ----------------- Main OCR Class -----------------
class TwoStageOCR:
def __init__(
self,
box_model_path: str,
yolo_model_path: str,
cnn_model_path: str,
image_size=(28, 28),
conf_threshold=0.25
):
# Stage 1: panel/region detector
self.box_detector = YOLO(box_model_path)
# Stage 2: digit detector for refined localization
self.digit_detector = YOLO(yolo_model_path)
# CNN for final classification
self.cnn = tf.keras.models.load_model(cnn_model_path, compile=False)
# Embedded class names for LeNet
class_names = ['0','1','2','3','4','5','6','7','8','9','C','dot']
self.inv_map = {i: label for i, label in enumerate(class_names)}
self.image_size = image_size
self.conf_threshold = conf_threshold
def preprocess_crop(self, crop: np.ndarray) -> np.ndarray:
gray = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
resized = cv2.resize(gray, self.image_size)
normed = resized.astype(np.float32) / 255.0
return normed.reshape(1, *self.image_size, 1)
def ocr_panel(self, panel: np.ndarray) -> str:
"""
Detect digits in a cropped panel and classify using CNN.
"""
res = self.digit_detector.predict(source=panel, verbose=False)[0]
boxes = res.boxes.xyxy.cpu().numpy()
confs = res.boxes.conf.cpu().numpy()
# Filter by confidence
mask = confs >= self.conf_threshold
boxes = boxes[mask]
if boxes.size == 0:
return ""
# Sort left-to-right
boxes = boxes[np.argsort(boxes[:, 0])]
digits = []
for x1, y1, x2, y2 in boxes:
c = panel[int(y1):int(y2), int(x1):int(x2)]
inp = self.preprocess_crop(c)
probs = self.cnn.predict(inp, verbose=False)
idx = int(np.argmax(probs, axis=1)[0])
label = self.inv_map[idx]
digits.append(label)
return ''.join(digits)
def ocr_image(self, image_path: str) -> str:
img = cv2.imread(image_path)
if img is None:
raise FileNotFoundError(f"Cannot read {image_path}")
# Stage 1: detect and crop panels
res_panels = self.box_detector.predict(source=img, verbose=False)[0]
panels = crop_regions(img, res_panels)
if not panels:
return ""
# Sort panels by x-coordinate
panels = sorted(panels, key=lambda x: x[1])
# Stage 2: OCR each panel
results = []
for panel_crop, _ in panels:
text = self.ocr_panel(panel_crop)
if text:
results.append(text)
return ' '.join(results)
# -------------------
# Example pipeline
# -------------------
if __name__ == '__main__':
box_model = 'Models/res_temp_box_v3.pt'
temp_yolo_model = 'Models/temp_detect_v3.pt'
temp_cnn_model = 'Models/lenet7seg.h5'
res_yolo_model = 'Models/res_detect_v4.pt'
res_cnn_model = 'Models/lenet_res_v4.h5'
# Initialize both OCRs
temp_ocr = TwoStageOCR(
box_model_path=box_model,
yolo_model_path=temp_yolo_model,
cnn_model_path=temp_cnn_model,
image_size=(28,28),
conf_threshold=0.3
)
from Lenet_res import YoloLeNetOCR
res_ocr = YoloLeNetOCR(
yolo_model_path=res_yolo_model,
lenet_model_path=res_cnn_model,
image_size=(28,28),
conf_threshold=0.5
)
input_dir = 'cr_test'
for fname in os.listdir(input_dir):
if not fname.lower().endswith(('.png','.jpg','.jpeg')):
continue
full = os.path.join(input_dir, fname)
img = cv2.imread(full)
if img is None:
print(f"Cannot read {full}")
continue
# Detect panels (res or temp)
box_detector = temp_ocr.box_detector
res_panels = box_detector.predict(source=img, verbose=False)[0]
if not hasattr(res_panels, 'obb') or res_panels.obb is None:
print(f"{fname} -> No panels detected")
continue
polys = res_panels.obb.xyxyxyxy.cpu().numpy()
confs = res_panels.obb.conf.cpu().numpy()
class_ids = res_panels.obb.cls.cpu().numpy().astype(int)
class_names = box_detector.model.names # Should be ['res', 'temp']
for poly, conf, cls_id in zip(polys, confs, class_ids):
if conf < 0.3:
continue
pts = poly.reshape(4, 2).astype(np.float32)
rect = order_points(pts) # <-- FIXED HERE
(tl, tr, br, bl) = rect
widthA = np.linalg.norm(br - bl)
widthB = np.linalg.norm(tr - tl)
maxW = int(max(widthA, widthB))
heightA = np.linalg.norm(tr - br)
heightB = np.linalg.norm(tl - bl)
maxH = int(max(heightA, heightB))
dst = np.array([
[0, 0],
[maxW - 1, 0],
[maxW - 1, maxH - 1],
[0, maxH - 1]
], dtype="float32")
M = cv2.getPerspectiveTransform(rect, dst)
crop = cv2.warpPerspective(img, M, (maxW, maxH))
class_name = class_names[cls_id]
if class_name == 'temp':
raw = temp_ocr.ocr_panel(crop)
# Remove 'C' if present
temp_digits = raw.replace('C', '')
# Only format if at least 2 digits
if len(temp_digits) > 1:
formatted = temp_digits[:-1] + '.' + temp_digits[-1] + '°C'
else:
formatted = temp_digits + '°C'
print(f"{fname} [TEMP] -> {formatted}")
elif class_name == 'res':
# Save crop to a temporary file
with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp:
cv2.imwrite(tmp.name, crop)
tmp_path = tmp.name
raw = res_ocr.ocr_image(tmp_path)
os.remove(tmp_path) # Clean up temp file
raw = raw.replace("dot", ".")
print(f"{fname} [RES] -> {raw}")
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