Kennzeichen Erkennungsproblem - inkonsistente Ergebnisse ⇐ Python

[Anonymous]

Ich arbeite an einem Nummernschild -Erkennungssystem mit OpenCV in Python. Mein Skript zielt darauf ab, Nummernschilder in Bildern zu erkennen, eine Perspektivkorrektur anzuwenden und die erkannten Platten zu speichern. Es funktioniert jedoch nicht konsequent über alle Bilder. In einigen Fällen erkennt es keine Platten oder erkennt falsche Konturen.

Code: Select all

import cv2
import os
import numpy as np
from datetime import datetime

def redresser_perspective(roi, approx):
# Function to apply perspective transformation (not included here)
pass

def detect_license_plate(image_path, output_dir='detected_plates'):
"""
Detects license plates in an image and saves them.

Enhancements:
- Perspective correction for tilted plates.
- More robust contour filtering based on aspect ratio and area.

Args:
image_path (str): Path to the input image.
output_dir (str): Directory to save detected plates.

Returns:
list: List of saved plate image paths.
"""
if not os.path.exists(output_dir):
os.makedirs(output_dir)

img = cv2.imread(image_path)
if img is None:
print(f"Error: Cannot read image {image_path}")
return []

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
enhanced = clahe.apply(gray)

blur = cv2.GaussianBlur(enhanced, (5, 5), 0)
thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV, 19, 9)

rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
dilation = cv2.dilate(thresh, rect_kernel, iterations=1)

contours, _ = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

img_display = img.copy()
plates_saved = []
height, width, _ = img.shape

for contour in contours:
area = cv2.contourArea(contour)
peri = cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, 0.02 * peri, True)

if len(approx) >= 4 and len(approx)  min_width and min_height < h < max_height and
2 < aspect_ratio < 7 and 0.3 < area_ratio < 0.95):

roi = img[y:y+h, x:x+w]

if len(approx) == 4:
roi_corrected = redresser_perspective(roi, approx)
if roi_corrected is not None:
roi = roi_corrected

roi_gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
_, roi_thresh = cv2.threshold(roi_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
white_pixels = cv2.countNonZero(roi_thresh)
total_pixels = roi_thresh.size
white_ratio = white_pixels / total_pixels

if 0.3 < white_ratio < 0.95:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
output_path = os.path.join(output_dir, f"plate_{timestamp}.jpg")

cv2.imwrite(output_path, roi)
plates_saved.append(output_path)

cv2.rectangle(img_display, (x, y), (x+w, y+h), (0, 255, 0), 2)

timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
output_original = os.path.join(output_dir, f"original_with_plates_{timestamp}.jpg")
cv2.imwrite(output_original, img_display)

if plates_saved:
print(f"{len(plates_saved)} plate(s) detected and saved.")
else:
print("No license plate detected.")

return plates_saved

Beispielbilder:
Hier sind zwei Beispielbilder mit falschen Erkennungen:
Originalbild: