def get_plate_ver2(image, Dmax=608, Dmin = 250):
vehicle = preprocess_ver2(image)
ratio = float(max(vehicle.shape[:2])) / min(vehicle.shape[:2])
side = int(ratio * Dmin)
bound_dim = min(side, Dmax)
_ , LpImg, _, cor = detect_lp(wpod_net, vehicle, bound_dim, lp_threshold=0.5)
return vehicle, LpImg, cor
def get_plate(image_path):
Dmax = 608
Dmin = 288
vehicle = preprocess_image(image_path)
ratio = float(max(vehicle.shape[:2])) / min(vehicle.shape[:2])
side = int(ratio * Dmin)
bound_dim = min(side, Dmax)
_ , LpImg, _, cor, final_x, final_y = detect_lp(wpod_net, vehicle, bound_dim, lp_threshold=0.5)
return vehicle, LpImg, cor, final_x, final_y
def get_plate(image_path, dir_path):
Dmax = 608
Dmin = 288
vehicle = preprocess_image(image_path, dir_path)
# print("preprocessing ends-> !! ")
ratio = float(max(vehicle.shape[:2])) / min(vehicle.shape[:2]) # 350-197
side = int(ratio * Dmin)
bound_dim = min(side, Dmax)
_, LpImg, _, cor = detect_lp(wpod_net,
vehicle,
bound_dim,
lp_threshold=0.5)
return LpImg, cor
def get_plate(image_path):
Dmax = 608
Dmin = 288
# Loading model for plate detection
wpod_net_path = "wpod-net.json"
wpod_net = load_modell(wpod_net_path)
vehicle = preprocess_image(image_path)
ratio = float(max(vehicle.shape[:2])) / min(vehicle.shape[:2])
side = int(ratio * Dmin)
bound_dim = min(side, Dmax)
_, LpImg, _, cor = detect_lp(wpod_net,
vehicle,
bound_dim,
lp_threshold=0.5)
return vehicle, LpImg, cor
def get_plate(image_path):
Dmax = 608
Dmin = 288
vehicle = preprocess_image(image_path)
ratio = float(max(vehicle.shape[:2])) / min(vehicle.shape[:2])
side = int(ratio * Dmin)
bound_dim = min(side, Dmax)
_, LpImg, _, cor = detect_lp(wpod_net,
vehicle,
bound_dim,
lp_threshold=0.5)
count = 0
for img in LpImg:
plt.imsave("croppedImgs/cropped" + str(count) + ".jpg", img)
count += 1
return LpImg