Update ocr_engine.py

ocr_engine.py

@@ -3,8 +3,6 @@ import numpy as np
 import cv2
 import re
 import logging
-from datetime import datetime
-import os
 
 # Set up logging for debugging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -12,20 +10,6 @@ logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(
 # Initialize EasyOCR
 easyocr_reader = easyocr.Reader(['en'], gpu=False)
 
-# Directory for debug images
-DEBUG_DIR = "debug_images"
-os.makedirs(DEBUG_DIR, exist_ok=True)
-
-def save_debug_image(img, filename_suffix, prefix=""):
-    """Saves an image to the debug directory with a timestamp."""
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
-    filename = os.path.join(DEBUG_DIR, f"{prefix}{timestamp}_{filename_suffix}.png")
-    if len(img.shape) == 3:  # Color image
-        cv2.imwrite(filename, img)
-    else:  # Grayscale image
-        cv2.imwrite(filename, img)
-    logging.info(f"Saved debug image: {filename}")
-
 def estimate_brightness(img):
     """Estimate image brightness to detect illuminated displays"""
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
@@ -34,80 +18,57 @@ def estimate_brightness(img):
 def detect_roi(img):
     """Detect and crop the region of interest (likely the digital display)"""
     try:
-        save_debug_image(img, "01_original")
         gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        save_debug_image(gray, "02_grayscale")
-        
-        # Use adaptive thresholding for better robustness
-        thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
-                                       cv2.THRESH_BINARY, 11, 2)
-        save_debug_image(thresh, "03_roi_adaptive_threshold")
-        
-        kernel = np.ones((7, 7), np.uint8)  # Smaller kernel
-        dilated = cv2.dilate(thresh, kernel, iterations=3)  # Fewer iterations
-        save_debug_image(dilated, "04_roi_dilated")
-        
+        brightness = estimate_brightness(img)
+        thresh_value = 230 if brightness > 100 else 190
+        _, thresh = cv2.threshold(gray, thresh_value, 255, cv2.THRESH_BINARY)
+        kernel = np.ones((9, 9), np.uint8)
+        dilated = cv2.dilate(thresh, kernel, iterations=3)
         contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        
         if contours:
-            img_area = img.shape[0] * img.shape[1]
-            valid_contours = []
-            for c in contours:
-                area = cv2.contourArea(c)
-                # Relaxed area and aspect ratio filters
-                if 500 < area < (img_area * 0.95):
-                    x, y, w, h = cv2.boundingRect(c)
-                    aspect_ratio = w / h
-                    if 1.5 <= aspect_ratio <= 6.0 and w > 80 and h > 40:
-                        valid_contours.append(c)
-
+            valid_contours = [c for c in contours if cv2.contourArea(c) > 500]
             if valid_contours:
                 for contour in sorted(valid_contours, key=cv2.contourArea, reverse=True):
                     x, y, w, h = cv2.boundingRect(contour)
-                    padding = 60  # Increased padding
-                    x, y = max(0, x - padding), max(0, y - padding)
-                    w, h = min(w + 2 * padding, img.shape[1] - x), min(h + 2 * padding, img.shape[0] - y)
-                    roi_img = img[y:y+h, x:x+w]
-                    save_debug_image(roi_img, "05_detected_roi")
-                    logging.info(f"Detected ROI with dimensions: ({x}, {y}, {w}, {h})")
-                    return roi_img, (x, y, w, h)
-        
-        logging.info("No suitable ROI found, returning original image.")
-        save_debug_image(img, "05_no_roi_original_fallback")
+                    aspect_ratio = w / h
+                    if 1.5 <= aspect_ratio <= 4.0 and w > 50 and h > 30:
+                        x, y = max(0, x-40), max(0, y-40)
+                        w, h = min(w+80, img.shape[1]-x), min(h+80, img.shape[0]-y)
+                        return img[y:y+h, x:x+w], (x, y, w, h)
         return img, None
     except Exception as e:
         logging.error(f"ROI detection failed: {str(e)}")
-        save_debug_image(img, "05_roi_detection_error_fallback")
         return img, None
 
 def detect_segments(digit_img):
     """Detect seven-segment patterns in a digit image"""
     h, w = digit_img.shape
-    if h < 15 or w < 10:
+    if h < 10 or w < 10:
         return None
 
+    # Define segment regions (top, middle, bottom, left-top, left-bottom, right-top, right-bottom)
     segments = {
-        'top': (int(w*0.15), int(w*0.85), 0, int(h*0.2)),
-        'middle': (int(w*0.15), int(w*0.85), int(h*0.4), int(h*0.6)),
-        'bottom': (int(w*0.15), int(w*0.85), int(h*0.8), h),
-        'left_top': (0, int(w*0.25), int(h*0.05), int(h*0.5)),
-        'left_bottom': (0, int(w*0.25), int(h*0.5), int(h*0.95)),
-        'right_top': (int(w*0.75), w, int(h*0.05), int(h*0.5)),
-        'right_bottom': (int(w*0.75), w, int(h*0.5), int(h*0.95))
+        'top': (0, w, 0, h//5),
+        'middle': (0, w, 2*h//5, 3*h//5),
+        'bottom': (0, w, 4*h//5, h),
+        'left_top': (0, w//5, 0, h//2),
+        'left_bottom': (0, w//5, h//2, h),
+        'right_top': (4*w//5, w, 0, h//2),
+        'right_bottom': (4*w//5, w, h//2, h)
     }
 
     segment_presence = {}
     for name, (x1, x2, y1, y2) in segments.items():
-        x1, y1 = max(0, x1), max(0, y1)
-        x2, y2 = min(w, x2), min(h, y2)
         region = digit_img[y1:y2, x1:x2]
         if region.size == 0:
-            segment_presence[name] = False
-            continue
+            return None
+        # Count white pixels in the region
         pixel_count = np.sum(region == 255)
         total_pixels = region.size
-        segment_presence[name] = pixel_count / total_pixels > 0.45  # Lowered threshold
+        # Segment is present if more than 50% of the region is white
+        segment_presence[name] = pixel_count > total_pixels * 0.5
 
+    # Seven-segment digit patterns
     digit_patterns = {
         '0': ('top', 'bottom', 'left_top', 'left_bottom', 'right_top', 'right_bottom'),
         '1': ('right_top', 'right_bottom'),
@@ -122,228 +83,140 @@ def detect_segments(digit_img):
     }
 
     best_match = None
-    max_score = -1
+    max_matches = 0
     for digit, pattern in digit_patterns.items():
         matches = sum(1 for segment in pattern if segment_presence.get(segment, False))
-        non_matches_penalty = sum(1 for segment in segment_presence if segment not in pattern and segment_presence[segment])
-        current_score = matches - non_matches_penalty
-        if all(segment_presence.get(s, False) for s in pattern):
-            current_score += 0.5
-        if current_score > max_score:
-            max_score = current_score
+        non_matches = sum(1 for segment in segment_presence if segment not in pattern and segment_presence[segment])
+        score = matches - non_matches
+        if score > max_matches:
+            max_matches = score
             best_match = digit
-        elif current_score == max_score and best_match is not None:
-            current_digit_non_matches = sum(1 for segment in segment_presence if segment not in pattern and segment_presence[segment])
-            best_digit_pattern = digit_patterns[best_match]
-            best_digit_non_matches = sum(1 for segment in segment_presence if segment not in best_digit_pattern and segment_presence[segment])
-            if current_digit_non_matches < best_digit_non_matches:
-                best_match = digit
-    
-    logging.debug(f"Segment presence: {segment_presence}, Detected digit: {best_match}")
+
     return best_match
 
 def custom_seven_segment_ocr(img, roi_bbox):
     """Perform custom OCR for seven-segment displays"""
     try:
         gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        brightness = estimate_brightness(img)
-        if brightness > 150:
-            _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-        else:
-            _, thresh = cv2.threshold(gray, 80, 255, cv2.THRESH_BINARY)  # Lower threshold
-        save_debug_image(thresh, "06_roi_thresh_for_digits")
+        _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
 
+        # Use EasyOCR to get bounding boxes for digits
         results = easyocr_reader.readtext(thresh, detail=1, paragraph=False, 
-                                          contrast_ths=0.2, adjust_contrast=0.8, 
-                                          text_threshold=0.7, mag_ratio=2.0, 
-                                          allowlist='0123456789.', y_ths=0.3)
-        
-        logging.info(f"EasyOCR results: {results}")
+                                        contrast_ths=0.1, adjust_contrast=0.7, 
+                                        text_threshold=0.9, mag_ratio=1.5, 
+                                        allowlist='0123456789.')
+
         if not results:
-            logging.info("EasyOCR found no digits.")
             return None
 
-        digits_info = []
-        for (bbox, text, conf) in results:
+        # Sort bounding boxes left to right
+        digits = []
+        for (bbox, _, _) in results:
             (x1, y1), (x2, y2), (x3, y3), (x4, y4) = bbox
-            h_bbox = max(y1, y2, y3, y4) - min(y1, y2, y3, y4)
-            if len(text) == 1 and (text.isdigit() or text == '.') and h_bbox > 8:
-                x_min, x_max = int(min(x1, x4)), int(max(x2, x3))
-                y_min, y_max = int(min(y1, y2)), int(max(y3, y4))
-                digits_info.append((x_min, x_max, y_min, y_max, text, conf))
+            x_min, x_max = min(x1, x4), max(x2, x3)
+            y_min, y_max = min(y1, y2), max(y3, y4)
+            digits.append((x_min, x_max, y_min, y_max))
+
+        digits.sort(key=lambda x: x[0])  # Sort by x_min (left to right)
 
-        digits_info.sort(key=lambda x: x[0])
+        # Extract and recognize each digit
         recognized_text = ""
-        for idx, (x_min, x_max, y_min, y_max, easyocr_char, easyocr_conf) in enumerate(digits_info):
-            x_min, y_min = max(0, x_min), max(0, y_min)
-            x_max, y_max = min(thresh.shape[1], x_max), min(thresh.shape[0], y_max)
+        for x_min, x_max, y_min, y_max in digits:
+            x_min, y_min = max(0, int(x_min)), max(0, int(y_min))
+            x_max, y_max = min(thresh.shape[1], int(x_max)), min(thresh.shape[0], int(y_max))
             if x_max <= x_min or y_max <= y_min:
                 continue
-            digit_img_crop = thresh[y_min:y_max, x_min:x_max]
-            save_debug_image(digit_img_crop, f"07_digit_crop_{idx}_{easyocr_char}")
-            if easyocr_conf > 0.9 or easyocr_char == '.' or digit_img_crop.shape[0] < 15 or digit_img_crop.shape[1] < 10:
-                recognized_text += easyocr_char
-            else:
-                digit_from_segments = detect_segments(digit_img_crop)
-                if digit_from_segments:
-                    recognized_text += digit_from_segments
-                else:
-                    recognized_text += easyocr_char
-        
-        logging.info(f"Before validation, recognized_text: {recognized_text}")
-        text = re.sub(r"[^\d\.]", "", recognized_text)
-        if text.count('.') > 1:
-            text = text.replace('.', '', text.count('.') - 1)
-        if text and re.fullmatch(r"^\d*\.?\d*$", text) and len(text) > 0:
-            if text.startswith('.'):
-                text = "0" + text
-            if text.endswith('.'):
-                text = text.rstrip('.')
-            if text == '.' or text == '':
-                return None
+            digit_img = thresh[y_min:y_max, x_min:x_max]
+            digit = detect_segments(digit_img)
+            if digit:
+                recognized_text += digit
+
+        # Validate the recognized text
+        text = recognized_text
+        text = re.sub(r"[^\d\.]", "", text)
+        if re.fullmatch(r"\d{1,4}(\.\d{0,3})?", text):
             return text
-        logging.info(f"Custom OCR text '{recognized_text}' failed validation.")
         return None
     except Exception as e:
         logging.error(f"Custom seven-segment OCR failed: {str(e)}")
         return None
 
 def extract_weight_from_image(pil_img):
-    """Extract weight from a PIL image of a digital scale display"""
     try:
         img = np.array(pil_img)
         img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-        save_debug_image(img, "00_input_image")  # Log input image
 
         brightness = estimate_brightness(img)
-        conf_threshold = 0.6 if brightness > 150 else (0.5 if brightness > 80 else 0.4)
+        conf_threshold = 0.9 if brightness > 100 else 0.7
 
+        # Detect ROI
         roi_img, roi_bbox = detect_roi(img)
+
+        # Try custom seven-segment OCR first
         custom_result = custom_seven_segment_ocr(roi_img, roi_bbox)
         if custom_result:
+            # Format the custom result
             if "." in custom_result:
                 int_part, dec_part = custom_result.split(".")
                 int_part = int_part.lstrip("0") or "0"
-                dec_part = dec_part.rstrip('0')
-                if not dec_part and int_part != "0":
-                    custom_result = int_part
-                elif not dec_part and int_part == "0":
-                    custom_result = "0"
-                else:
-                    custom_result = f"{int_part}.{dec_part}"
+                custom_result = f"{int_part}.{dec_part.rstrip('0')}"
             else:
                 custom_result = custom_result.lstrip('0') or "0"
-            try:
-                float(custom_result)
-                logging.info(f"Custom OCR result: {custom_result}, Confidence: 100.0%")
-                return custom_result, 100.0
-            except ValueError:
-                logging.warning(f"Custom OCR result '{custom_result}' is not a valid number, falling back.")
-                custom_result = None
+            return custom_result, 100.0  # High confidence for custom OCR
 
-        logging.info("Custom OCR failed or invalid, falling back to general EasyOCR.")
-        processed_roi_img_gray = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
-        kernel_sharpening = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
-        sharpened_roi = cv2.filter2D(processed_roi_img_gray, -1, kernel_sharpening)
-        save_debug_image(sharpened_roi, "08_fallback_sharpened")
-        processed_roi_img_final = cv2.adaptiveThreshold(sharpened_roi, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
-                                                       cv2.THRESH_BINARY, 21, 5)
-        save_debug_image(processed_roi_img_final, "09_fallback_adaptive_thresh")
-
-        results = easyocr_reader.readtext(processed_roi_img_final, detail=1, paragraph=False, 
-                                          contrast_ths=0.3, adjust_contrast=0.9, 
-                                          text_threshold=0.5, mag_ratio=2.0, 
-                                          allowlist='0123456789.', batch_size=4, y_ths=0.3)
+        # Fallback to EasyOCR if custom OCR fails
+        images_to_process = [
+            ("raw", roi_img, {'contrast_ths': 0.1, 'adjust_contrast': 0.7, 'text_threshold': 0.9, 'mag_ratio': 1.5, 'allowlist': '0123456789.'}),
+        ]
 
         best_weight = None
         best_conf = 0.0
         best_score = 0.0
-        for (bbox, text, conf) in results:
-            text = text.lower().strip()
-            text = text.replace(",", ".").replace(";", ".").replace(":", ".").replace(" ", "")
-            text = text.replace("o", "0").replace("O", "0").replace("q", "0").replace("Q", "0")
-            text = text.replace("s", "5").replace("S", "5")
-            text = text.replace("g", "9").replace("G", "6")
-            text = text.replace("l", "1").replace("I", "1").replace("|", "1")
-            text = text.replace("b", "8").replace("B", "8")
-            text = text.replace("z", "2").replace("Z", "2")
-            text = text.replace("a", "4").replace("A", "4")
-            text = text.replace("e", "3")
-            text = text.replace("t", "7")
-            text = text.replace("~", "").replace("`", "")
-            text = re.sub(r"(kgs|kg|k|lb|g|gr|pounds|lbs)\b", "", text)
-            text = re.sub(r"[^\d\.]", "", text)
-            if text.count('.') > 1:
-                parts = text.split('.')
-                text = parts[0] + '.' + ''.join(parts[1:])
-            text = text.strip('.')
-            if re.fullmatch(r"^\d*\.?\d{0,3}$", text) and len(text.replace('.', '')) > 0:
-                try:
-                    weight = float(text)
-                    range_score = 1.0
-                    if 0.1 <= weight <= 250:
-                        range_score = 1.5
-                    elif weight > 250 and weight <= 500:
-                        range_score = 1.2
-                    elif weight > 500 and weight <= 1000:
-                        range_score = 1.0
-                    else:
-                        range_score = 0.5
-                    digit_count = len(text.replace('.', ''))
-                    digit_score = 1.0
-                    if digit_count >= 2 and digit_count <= 5:
-                        digit_score = 1.3
-                    elif digit_count == 1:
-                        digit_score = 0.8
-                    score = conf * range_score * digit_score
-                    if roi_bbox:
-                        (x_roi, y_roi, w_roi, h_roi) = roi_bbox
-                        roi_area = w_roi * h_roi
-                        x_min, y_min = int(min(b[0] for b in bbox)), int(min(b[1] for b in bbox))
-                        x_max, y_max = int(max(b[0] for b in bbox)), int(max(b[1] for b in bbox))
-                        bbox_area = (x_max - x_min) * (y_max - y_min)
-                        if roi_area > 0 and bbox_area / roi_area < 0.03:
-                            score *= 0.5
-                        bbox_aspect_ratio = (x_max - x_min) / (y_max - y_min) if (y_max - y_min) > 0 else 0
-                        if bbox_aspect_ratio < 0.2:
-                            score *= 0.7
-                    if score > best_score and conf > conf_threshold:
-                        best_weight = text
-                        best_conf = conf
-                        best_score = score
-                        logging.info(f"Candidate EasyOCR weight: '{text}', Conf: {conf}, Score: {score}")
-                except ValueError:
-                    logging.warning(f"Could not convert '{text}' to float during EasyOCR fallback.")
-                    continue
+
+        for mode, proc_img, ocr_params in images_to_process:
+            if mode == "raw":
+                proc_img = cv2.cvtColor(proc_img, cv2.COLOR_BGR2GRAY)
+            results = easyocr_reader.readtext(proc_img, detail=1, paragraph=False, **ocr_params)
+            
+            for (bbox, text, conf) in results:
+                text = text.lower().strip()
+                text = text.replace(",", ".").replace(";", ".")
+                text = text.replace("o", "0").replace("O", "0")
+                text = text.replace("s", "5").replace("S", "5")
+                text = text.replace("g", "9").replace("G", "6")
+                text = text.replace("l", "1").replace("I", "1")
+                text = text.replace("b", "8").replace("B", "8")
+                text = text.replace("z", "2").replace("Z", "2")
+                text = text.replace("q", "9").replace("Q", "9")
+                text = text.replace("kgs", "").replace("kg", "").replace("k", "")
+                text = re.sub(r"[^\d\.]", "", text)
+
+                if re.fullmatch(r"\d{1,4}(\.\d{0,3})?", text):
+                    try:
+                        weight = float(text)
+                        range_score = 1.0 if 0.1 <= weight <= 500 else 0.3
+                        digit_score = 1.5 if 10 <= weight < 100 else 1.0
+                        score = conf * range_score * digit_score
+                        if score > best_score and conf > conf_threshold:
+                            best_weight = text
+                            best_conf = conf
+                            best_score = score
+                    except ValueError:
+                        continue
 
         if not best_weight:
-            logging.info("No valid weight detected after all attempts.")
+            logging.info("No valid weight detected")
             return "Not detected", 0.0
 
         if "." in best_weight:
             int_part, dec_part = best_weight.split(".")
             int_part = int_part.lstrip("0") or "0"
-            dec_part = dec_part.rstrip('0')
-            if not dec_part and int_part != "0":
-                best_weight = int_part
-            elif not dec_part and int_part == "0":
-                best_weight = "0"
-            else:
-                best_weight = f"{int_part}.{dec_part}"
+            best_weight = f"{int_part}.{dec_part.rstrip('0')}"
         else:
             best_weight = best_weight.lstrip('0') or "0"
 
-        try:
-            final_float_weight = float(best_weight)
-            if final_float_weight < 0.01 or final_float_weight > 1000:
-                logging.warning(f"Detected weight {final_float_weight} is outside typical range, reducing confidence.")
-                best_conf *= 0.5
-        except ValueError:
-            pass
-
-        logging.info(f"Final detected weight: {best_weight}, Confidence: {round(best_conf * 100, 2)}%")
         return best_weight, round(best_conf * 100, 2)
 
     except Exception as e:
-        logging.error(f"Weight extraction failed unexpectedly: {str(e)}")
+        logging.error(f"Weight extraction failed: {str(e)}")
         return "Not detected", 0.0