def evaluting(wpod_net, model, labels, test_image_path="Plate_examples/mul2.jpg"):
vehicle, LpImg,cor = CarHelpers.get_plate(test_image_path, wpod_net)
if (len(LpImg)): #check if there is at least one license image
# Scales, calculates absolute values, and converts the result to 8-bit.
plate_image = cv2.convertScaleAbs(LpImg[0], alpha=(255.0))
# convert to grayscale and blur the image
gray = cv2.cvtColor(plate_image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(7,7),0)
# Applied inversed thresh_binary
binary = cv2.threshold(blur, 140, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
kernel3 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
thre_mor = cv2.morphologyEx(binary, cv2.MORPH_DILATE, kernel3)
# visualize results
fig = plt.figure(figsize=(12,7))
plt.rcParams.update({"font.size":18})
grid = gridspec.GridSpec(ncols=2,nrows=3,figure = fig)
plot_image = [plate_image, gray, blur, binary,thre_mor]
plot_name = ["plate_image","gray","blur","binary","dilation"]
for i in range(len(plot_image)):
fig.add_subplot(grid[i])
plt.axis(False)
plt.title(plot_name[i])
if i ==0:
plt.imshow(plot_image[i])
else:
plt.imshow(plot_image[i],cmap="gray")
plt.savefig('img/plot_image.jpg', dpi=300)
# Create sort_contours() function to grab the contour of each digit from left to right
def sort_contours(cnts,reverse = False):
i = 0
boundingBoxes = [cv2.boundingRect(c) for c in cnts]
(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
key=lambda b: b[1][i], reverse=reverse))
return cnts
cont, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# creat a copy version "test_roi" of plat_image to draw bounding box
test_roi = plate_image.copy()
# Initialize a list which will be used to append charater image
crop_characters = []
# define standard width and height of character
digit_w, digit_h = 30, 60
for c in sort_contours(cont):
(x, y, w, h) = cv2.boundingRect(c)
ratio = h/w
if 1<=ratio<=6.5: # Only select contour with defined ratio
if h/plate_image.shape[0]>=0.3: # Select contour which has the height larger than 50% of the plate
# Draw bounding box arroung digit number
cv2.rectangle(test_roi, (x, y), (x + w, y + h), (0, 255,0), 2)
# Sperate number and give prediction
curr_num = thre_mor[y:y+h,x:x+w]
curr_num = cv2.resize(curr_num, dsize=(digit_w, digit_h))
_, curr_num = cv2.threshold(curr_num, 140, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # 220 - 225
crop_characters.append(curr_num)
print("Detect {} letters...".format(len(crop_characters)))
fig = plt.figure(figsize=(10,6))
plt.axis(False)
plt.imshow(test_roi)
plt.savefig('img/test_roi.jpg', dpi=300,bbox_inches='tight')
#plt.savefig('grab_digit_contour.png',dpi=300)
fig = plt.figure(figsize=(14,4))
grid = gridspec.GridSpec(ncols=len(crop_characters),nrows=1,figure=fig)
for i in range(len(crop_characters)):
fig.add_subplot(grid[i])
plt.axis(False)
plt.imshow(crop_characters[i],cmap="gray")
plt.savefig('img/crop_characters.jpg')
#plt.savefig("segmented_leter.png",dpi=300,bbox_inches='tight')
fig = plt.figure(figsize=(15,3))
cols = len(crop_characters)
grid = gridspec.GridSpec(ncols=cols,nrows=1,figure=fig)
final_string = ''
for i,character in enumerate(crop_characters):
fig.add_subplot(grid[i])
title = np.array2string(CarModel.predict_from_model(character,model,labels))
plt.title('{}'.format(title.strip("'[]"),fontsize=20))
final_string+=title.strip("'[]")
plt.axis(False)
plt.imshow(character,cmap='gray')
plt.savefig('img/character.jpg', dpi=300,bbox_inches='tight')
print(final_string)
plt.savefig('img/final_result.jpg', dpi=300,bbox_inches='tight')
return final_string
plt.axis(False)
plt.imshow(crop_characters[i],cmap="gray")
#plt.savefig("segmented_leter.png",dpi=300)
# Load model architecture, weight and labels
json_file = open('model/MobileNets_character_recognition.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
model.load_weights("model/License_character_recognition_weight.h5")
print("[INFO] Model loaded successfully...")
labels = LabelEncoder()
labels.classes_ = np.load('model/license_character_classes.npy')
print("[INFO] Labels loaded successfully...")
fig = plt.figure(figsize=(15,3))
cols = len(crop_characters)
grid = gridspec.GridSpec(ncols=cols,nrows=1,figure=fig)
final_string = ''
for i,character in enumerate(crop_characters):
fig.add_subplot(grid[i])
title = np.array2string(CarModel.predict_from_model(character,model,labels))
plt.title('{}'.format(title.strip("'[]"),fontsize=20))
final_string+=title.strip("'[]")
plt.axis(False)
plt.imshow(character,cmap='gray')
print(final_string)
#plt.savefig('final_result.png', dpi=300)
