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36e7618 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | import cv2
import numpy as np
from ultralytics import YOLO
import easyocr
import os
import uuid
# =========================
# Load YOLO model
# =========================
MODEL_PATH = "weights/best.pt"
if not os.path.exists(MODEL_PATH):
print(f"❌ ERROR: Model weights not found at {MODEL_PATH}")
model = YOLO(MODEL_PATH)
# =========================
# EasyOCR Init
# =========================
reader = easyocr.Reader(['en'], gpu=False) # set gpu=False if needed
# =========================
# Debug folder
# =========================
DEBUG_DIR = "debug_crops"
os.makedirs(DEBUG_DIR, exist_ok=True)
# =========================
# OCR FUNCTION (EasyOCR)
# =========================
def get_ocr_text(crop, crop_id):
try:
if crop is None or crop.size == 0:
return None, 0.0
crop = cv2.resize(crop, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
crop_rgb = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)
cv2.imwrite(f"{DEBUG_DIR}/{crop_id}.jpg", crop)
results = reader.readtext(crop_rgb)
if not results:
return None, 0.0
texts = []
confidences = []
for (bbox, text, conf) in results:
clean_text = "".join([c for c in text if c.isalnum()])
if len(clean_text) >= 2: # allow small parts like "L8"
texts.append(clean_text)
confidences.append(conf)
if not texts:
return None, 0.0
# 🔥 SORT by vertical position (top → bottom)
results_sorted = sorted(results, key=lambda x: min([p[1] for p in x[0]]))
final_text = ""
for (_, text, _) in results_sorted:
clean = "".join([c for c in text if c.isalnum()])
if len(clean) >= 2:
final_text += clean
avg_conf = sum(confidences) / len(confidences)
print(f"✅ OCR [{crop_id}] -> {final_text}")
return final_text, float(avg_conf)
except Exception as e:
print(f"OCR Error: {e}")
return None, 0.0
# =========================
# IMAGE PROCESSING
# =========================
def process_image(image):
print(f"\n[STEP 1] Running YOLO detection...")
results = model(image, imgsz=320, verbose=False)
plates = []
found_count = len(results[0].boxes)
print(f"[STEP 2] YOLO found {found_count} bounding boxes.")
for r in results:
if r.boxes:
for box in r.boxes.xyxy.cpu().numpy():
crop_id = f"plate_{uuid.uuid4().hex[:6]}"
x1, y1, x2, y2 = map(int, box)
# 🔥 IMPORTANT FIX: bigger padding
h, w, _ = image.shape
pad = 15
crop = image[
max(0, y1 - pad):min(h, y2 + pad),
max(0, x1 - pad):min(w, x2 + pad)
]
print(f"[STEP 3] Processing {crop_id}...")
text, conf = get_ocr_text(crop, crop_id)
if text:
plates.append({
"text": text,
"confidence": conf,
"debug_id": crop_id
})
else:
print(f" ⚠️ OCR failed for {crop_id}")
return plates
# =========================
# VIDEO PROCESSING
# =========================
def process_video_stream(video_path):
print(f"\n🎥 Processing video: {video_path}")
cap = cv2.VideoCapture(video_path)
tracked_plates = {}
final_results = []
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model.track(frame, persist=True, imgsz=320, verbose=False)
if results[0].boxes.id is not None:
ids = results[0].boxes.id.int().cpu().tolist()
boxes = results[0].boxes.xyxy.cpu().numpy()
for box, tid in zip(boxes, ids):
if tid not in tracked_plates:
x1, y1, x2, y2 = map(int, box)
crop_id = f"track_{tid}"
crop = frame[y1:y2, x1:x2]
text, conf = get_ocr_text(crop, crop_id)
if text and len(text) >= 5:
print(f"✅ Detected Plate: {text}")
tracked_plates[tid] = text
final_results.append(text)
cap.release()
return list(set(final_results)) |