Programmiererforum

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