Created
April 5, 2016 05:25
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import cv2 | |
import matplotlib.pyplot as plt | |
import numpy as np | |
im = cv2.imread(r'img\barcodes.jpg', cv2.IMREAD_GRAYSCALE) | |
im_out = cv2.imread(r'img\barcodes.jpg') | |
#riscalatura dell'immagine | |
scale = 800.0 / im.shape[1] | |
im = cv2.resize(im, (int(im.shape[1] * scale), int(im.shape[0] * scale))) | |
#blackhat | |
kernel = np.ones((1, 3), np.uint8) | |
im = cv2.morphologyEx(im, cv2.MORPH_BLACKHAT, kernel, anchor=(1, 0)) | |
#sogliatura | |
thresh, im = cv2.threshold(im, 10, 255, cv2.THRESH_BINARY) | |
#operazioni morfologiche | |
kernel = np.ones((1, 5), np.uint8) | |
im = cv2.morphologyEx(im, cv2.MORPH_DILATE, kernel, anchor=(2, 0), iterations=2) #dilatazione | |
im = cv2.morphologyEx(im, cv2.MORPH_CLOSE, kernel, anchor=(2, 0), iterations=2) #chiusura | |
kernel = np.ones((21, 35), np.uint8) | |
im = cv2.morphologyEx(im, cv2.MORPH_OPEN, kernel, iterations=1) | |
#estrazione dei componenti connessi | |
contours, hierarchy = cv2.findContours(im, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) | |
unscale = 1.0 / scale | |
if contours != None: | |
for contour in contours: | |
# se l'area non è grande a sufficienza la salto | |
if cv2.contourArea(contour) <= 2000: | |
continue | |
#estraggo il rettangolo di area minima (in formato (centro_x, centro_y), (width, height), angolo) | |
rect = cv2.minAreaRect(contour) | |
#l'effetto della riscalatura iniziale deve essere eliminato dalle coordinate rilevate | |
rect = \ | |
((int(rect[0][0] * unscale), int(rect[0][1] * unscale)), \ | |
(int(rect[1][0] * unscale), int(rect[1][1] * unscale)), \ | |
rect[2]) | |
#disegno il tutto sull'immagine originale | |
box = np.int0(cv2.cv.BoxPoints(rect)) | |
cv2.drawContours(im_out, [box], 0, (0, 255, 0), thickness = 2) | |
plt.imshow(im_out) | |
#scrittura dell' immagine finale | |
cv2.imwrite(r'img\out.png', im_out) |
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