@@ -0,0 +1,11 @@ | |||
import cv2 | |||
import os | |||
import hashlib | |||
def verticalMappingToFolder(image): | |||
name = hashlib.md5(image.data).hexdigest()[:8] | |||
print(name) | |||
cv2.imwrite("./cache/finemapping/"+name+".png",image) | |||
@@ -0,0 +1,103 @@ | |||
# -- coding: UTF-8 | |||
import cv2 | |||
import matplotlib.pyplot as plt | |||
from sklearn.cluster import KMeans | |||
import os | |||
boundaries = [ | |||
([100,80,0],[240,220,110]), # yellow | |||
([0,40,50],[110,180,250]), # blue | |||
([0,60,0],[60,160,70]), # green | |||
] | |||
color_attr = ["黄牌","蓝牌",'绿牌','白牌','黑牌'] | |||
threhold_green = 13 | |||
threhold_blue = 13 | |||
threhold_yellow1 = 50 | |||
threhold_yellow2 = 70 | |||
# plt.figure() | |||
# plt.axis("off") | |||
# plt.imshow(image) | |||
# plt.show() | |||
import numpy as np | |||
def centroid_histogram(clt): | |||
numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1) | |||
(hist, _) = np.histogram(clt.labels_, bins=numLabels) | |||
# normalize the histogram, such that it sums to one | |||
hist = hist.astype("float") | |||
hist /= hist.sum() | |||
# return the histogram | |||
return hist | |||
def plot_colors(hist, centroids): | |||
bar = np.zeros((50, 300, 3), dtype="uint8") | |||
startX = 0 | |||
for (percent, color) in zip(hist, centroids): | |||
endX = startX + (percent * 300) | |||
cv2.rectangle(bar, (int(startX), 0), (int(endX), 50), | |||
color.astype("uint8").tolist(), -1) | |||
startX = endX | |||
# return the bar chart | |||
return bar | |||
def search_boundaries(color): | |||
for i,color_bound in enumerate(boundaries): | |||
if np.all(color >= color_bound[0]) and np.all(color <= color_bound[1]): | |||
return i | |||
return -1 | |||
def judge_color(color): | |||
r = color[0] | |||
g = color[1] | |||
b = color[2] | |||
if g - r >= threhold_green and g - b >= threhold_green: | |||
return 2 | |||
if b - r >= threhold_blue and b - g >= threhold_blue: | |||
return 1 | |||
if r- b > threhold_yellow2 and g - b > threhold_yellow2: | |||
return 0 | |||
if r > 200 and b > 200 and g > 200: | |||
return 3 | |||
if r < 50 and b < 50 and g < 50: | |||
return 4 | |||
return -1 | |||
def judge_plate_color(img): | |||
image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |||
image = image.reshape((image.shape[0] * image.shape[1], 3)) | |||
clt = KMeans(n_clusters=2) | |||
clt.fit(image) | |||
hist = centroid_histogram(clt) | |||
index = np.argmax(hist) | |||
#print clt.cluster_centers_[index] | |||
#color_index = search_boundaries(clt.cluster_centers_[index]) | |||
color_index = judge_color(clt.cluster_centers_[index]) | |||
if color_index == -1: | |||
if index == 0: | |||
secound_index = 1 | |||
else: | |||
secound_index = 0 | |||
color_index = judge_color(clt.cluster_centers_[secound_index]) | |||
if color_index == -1: | |||
print(clt.cluster_centers_) | |||
bar = plot_colors(hist, clt.cluster_centers_) | |||
# show our color bart | |||
plt.figure() | |||
plt.axis("off") | |||
plt.imshow(bar) | |||
plt.show() | |||
if color_index != -1: | |||
return color_attr[color_index],clt.cluster_centers_[index] | |||
else: | |||
return None,clt.cluster_centers_[index] |
@@ -0,0 +1,6 @@ | |||
import json | |||
with open("/Users/universe/ProgramUniverse/zeusees/HyperLPR/config.json") as f: | |||
configuration = json.load(f) |
@@ -0,0 +1,100 @@ | |||
#coding=utf-8 | |||
import numpy as np | |||
import cv2 | |||
import time | |||
from matplotlib import pyplot as plt | |||
import math | |||
from scipy.ndimage import filters | |||
# | |||
# def strokeFiter(): | |||
# pass; | |||
def angle(x,y): | |||
return int(math.atan2(float(y),float(x))*180.0/3.1415) | |||
def h_rot(src, angle, scale=1.0): | |||
w = src.shape[1] | |||
h = src.shape[0] | |||
rangle = np.deg2rad(angle) | |||
nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale | |||
nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale | |||
rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale) | |||
rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0])) | |||
rot_mat[0,2] += rot_move[0] | |||
rot_mat[1,2] += rot_move[1] | |||
return cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4) | |||
pass | |||
def v_rot(img, angel, shape, max_angel): | |||
size_o = [shape[1],shape[0]] | |||
size = (shape[1]+ int(shape[0]*np.cos((float(max_angel )/180) * 3.14)),shape[0]) | |||
interval = abs( int( np.sin((float(angel) /180) * 3.14)* shape[0])) | |||
pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]]) | |||
if(angel>0): | |||
pts2 = np.float32([[interval,0],[0,size[1] ],[size[0],0 ],[size[0]-interval,size_o[1]]]) | |||
else: | |||
pts2 = np.float32([[0,0],[interval,size[1] ],[size[0]-interval,0 ],[size[0],size_o[1]]]) | |||
M = cv2.getPerspectiveTransform(pts1,pts2) | |||
dst = cv2.warpPerspective(img,M,size) | |||
return dst,M | |||
def skew_detection(image_gray): | |||
h, w = image_gray.shape[:2] | |||
eigen = cv2.cornerEigenValsAndVecs(image_gray,12, 5) | |||
angle_sur = np.zeros(180,np.uint) | |||
eigen = eigen.reshape(h, w, 3, 2) | |||
flow = eigen[:,:,2] | |||
vis = image_gray.copy() | |||
vis[:] = (192 + np.uint32(vis)) / 2 | |||
d = 12 | |||
points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2) | |||
for x, y in points: | |||
vx, vy = np.int32(flow[int(y), int(x)]*d) | |||
# cv2.line(rgb, (x-vx, y-vy), (x+vx, y+vy), (0, 355, 0), 1, cv2.LINE_AA) | |||
ang = angle(vx,vy) | |||
angle_sur[(ang+180)%180] +=1 | |||
# torr_bin = 30 | |||
angle_sur = angle_sur.astype(np.float) | |||
angle_sur = (angle_sur-angle_sur.min())/(angle_sur.max()-angle_sur.min()) | |||
angle_sur = filters.gaussian_filter1d(angle_sur,5) | |||
skew_v_val = angle_sur[20:180-20].max() | |||
skew_v = angle_sur[30:180-30].argmax() + 30 | |||
skew_h_A = angle_sur[0:30].max() | |||
skew_h_B = angle_sur[150:180].max() | |||
skew_h = 0 | |||
if (skew_h_A > skew_v_val*0.3 or skew_h_B > skew_v_val*0.3): | |||
if skew_h_A>=skew_h_B: | |||
skew_h = angle_sur[0:20].argmax() | |||
else: | |||
skew_h = - angle_sur[160:180].argmax() | |||
return skew_h,skew_v | |||
def fastDeskew(image): | |||
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) | |||
skew_h,skew_v = skew_detection(image_gray) | |||
print("校正角度 h ",skew_h,"v",skew_v) | |||
deskew,M = v_rot(image,int((90-skew_v)*1.5),image.shape,60) | |||
return deskew,M | |||
if __name__ == '__main__': | |||
fn = './dataset/0.jpg' | |||
img = cv2.imread(fn) | |||
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |||
skew_h,skew_v = skew_detection(gray) | |||
img = v_rot(img,(90-skew_v ),img.shape,60) | |||
# img = h_rot(img,skew_h) | |||
# if img.shape[0]>img.shape[1]: | |||
# img = h_rot(img, -90) | |||
plt.show() | |||
cv2.waitKey() |
@@ -0,0 +1,76 @@ | |||
import cv2 | |||
import numpy as np | |||
watch_cascade = cv2.CascadeClassifier('./model/cascade.xml') | |||
def computeSafeRegion(shape,bounding_rect): | |||
top = bounding_rect[1] # y | |||
bottom = bounding_rect[1] + bounding_rect[3] # y + h | |||
left = bounding_rect[0] # x | |||
right = bounding_rect[0] + bounding_rect[2] # x + w | |||
min_top = 0 | |||
max_bottom = shape[0] | |||
min_left = 0 | |||
max_right = shape[1] | |||
# print "computeSateRegion input shape",shape | |||
if top < min_top: | |||
top = min_top | |||
# print "tap top 0" | |||
if left < min_left: | |||
left = min_left | |||
# print "tap left 0" | |||
if bottom > max_bottom: | |||
bottom = max_bottom | |||
#print "tap max_bottom max" | |||
if right > max_right: | |||
right = max_right | |||
#print "tap max_right max" | |||
# print "corr",left,top,right,bottom | |||
return [left,top,right-left,bottom-top] | |||
def cropped_from_image(image,rect): | |||
x, y, w, h = computeSafeRegion(image.shape,rect) | |||
return image[y:y+h,x:x+w] | |||
def detectPlateRough(image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05): | |||
print(image_gray.shape) | |||
if top_bottom_padding_rate>0.2: | |||
print("error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate) | |||
exit(1) | |||
height = image_gray.shape[0] | |||
padding = int(height*top_bottom_padding_rate) | |||
scale = image_gray.shape[1]/float(image_gray.shape[0]) | |||
image = cv2.resize(image_gray, (int(scale*resize_h), resize_h)) | |||
image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]] | |||
image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY) | |||
watches = watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40)) | |||
cropped_images = [] | |||
for (x, y, w, h) in watches: | |||
cropped_origin = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h))) | |||
x -= w * 0.14 | |||
w += w * 0.28 | |||
y -= h * 0.6 | |||
h += h * 1.1; | |||
cropped = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h))) | |||
cropped_images.append([cropped,[x, y+padding, w, h],cropped_origin]) | |||
return cropped_images |
@@ -0,0 +1,63 @@ | |||
#coding=utf-8 | |||
from keras import backend as K | |||
from keras.models import load_model | |||
from keras.layers import * | |||
import numpy as np | |||
import random | |||
import string | |||
import cv2 | |||
from . import e2emodel as model | |||
chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", | |||
"琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", | |||
"B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X", | |||
"Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空" | |||
]; | |||
pred_model = model.construct_model("./model/ocr_plate_all_w_rnn_2.h5",) | |||
import time | |||
def fastdecode(y_pred): | |||
results = "" | |||
confidence = 0.0 | |||
table_pred = y_pred.reshape(-1, len(chars)+1) | |||
res = table_pred.argmax(axis=1) | |||
for i,one in enumerate(res): | |||
if one<len(chars) and (i==0 or (one!=res[i-1])): | |||
results+= chars[one] | |||
confidence+=table_pred[i][one] | |||
confidence/= len(results) | |||
return results,confidence | |||
def recognizeOne(src): | |||
# x_tempx= cv2.imread(src) | |||
x_tempx = src | |||
# x_tempx = cv2.bitwise_not(x_tempx) | |||
x_temp = cv2.resize(x_tempx,( 160,40)) | |||
x_temp = x_temp.transpose(1, 0, 2) | |||
t0 = time.time() | |||
y_pred = pred_model.predict(np.array([x_temp])) | |||
y_pred = y_pred[:,2:,:] | |||
# plt.imshow(y_pred.reshape(16,66)) | |||
# plt.show() | |||
# | |||
# cv2.imshow("x_temp",x_tempx) | |||
# cv2.waitKey(0) | |||
return fastdecode(y_pred) | |||
# | |||
# | |||
# import os | |||
# | |||
# path = "/Users/yujinke/PycharmProjects/HyperLPR_Python_web/cache/finemapping" | |||
# for filename in os.listdir(path): | |||
# if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".bmp"): | |||
# x = os.path.join(path,filename) | |||
# recognizeOne(x) | |||
# # print time.time() - t0 | |||
# | |||
# # cv2.imshow("x",x) | |||
# # cv2.waitKey() |
@@ -0,0 +1,34 @@ | |||
from keras import backend as K | |||
from keras.models import * | |||
from keras.layers import * | |||
from . import e2e | |||
def ctc_lambda_func(args): | |||
y_pred, labels, input_length, label_length = args | |||
y_pred = y_pred[:, 2:, :] | |||
return K.ctc_batch_cost(labels, y_pred, input_length, label_length) | |||
def construct_model(model_path): | |||
input_tensor = Input((None, 40, 3)) | |||
x = input_tensor | |||
base_conv = 32 | |||
for i in range(3): | |||
x = Conv2D(base_conv * (2 ** (i)), (3, 3),padding="same")(x) | |||
x = BatchNormalization()(x) | |||
x = Activation('relu')(x) | |||
x = MaxPooling2D(pool_size=(2, 2))(x) | |||
x = Conv2D(256, (5, 5))(x) | |||
x = BatchNormalization()(x) | |||
x = Activation('relu')(x) | |||
x = Conv2D(1024, (1, 1))(x) | |||
x = BatchNormalization()(x) | |||
x = Activation('relu')(x) | |||
x = Conv2D(len(e2e.chars)+1, (1, 1))(x) | |||
x = Activation('softmax')(x) | |||
base_model = Model(inputs=input_tensor, outputs=x) | |||
base_model.load_weights(model_path) | |||
return base_model |
@@ -0,0 +1,130 @@ | |||
#coding=utf-8 | |||
import cv2 | |||
import numpy as np | |||
from . import niblack_thresholding as nt | |||
from . import deskew | |||
def fitLine_ransac(pts,zero_add = 0 ): | |||
if len(pts)>=2: | |||
[vx, vy, x, y] = cv2.fitLine(pts, cv2.DIST_HUBER, 0, 0.01, 0.01) | |||
lefty = int((-x * vy / vx) + y) | |||
righty = int(((136- x) * vy / vx) + y) | |||
return lefty+30+zero_add,righty+30+zero_add | |||
return 0,0 | |||
#精定位算法 | |||
def findContoursAndDrawBoundingBox(image_rgb): | |||
line_upper = []; | |||
line_lower = []; | |||
line_experiment = [] | |||
grouped_rects = [] | |||
gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY) | |||
# for k in np.linspace(-1.5, -0.2,10): | |||
for k in np.linspace(-50, 0, 15): | |||
# thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k) | |||
# binary_niblack = gray_image > thresh_niblack | |||
# binary_niblack = binary_niblack.astype(np.uint8) * 255 | |||
binary_niblack = cv2.adaptiveThreshold(gray_image,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,17,k) | |||
# cv2.imshow("image1",binary_niblack) | |||
# cv2.waitKey(0) | |||
imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) | |||
for contour in contours: | |||
bdbox = cv2.boundingRect(contour) | |||
if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1200) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100): | |||
# cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1) | |||
line_upper.append([bdbox[0],bdbox[1]]) | |||
line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]]) | |||
line_experiment.append([bdbox[0],bdbox[1]]) | |||
line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]]) | |||
# grouped_rects.append(bdbox) | |||
rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE) | |||
leftyA, rightyA = fitLine_ransac(np.array(line_lower),3) | |||
rows,cols = rgb.shape[:2] | |||
# rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA) | |||
leftyB, rightyB = fitLine_ransac(np.array(line_upper),-3) | |||
rows,cols = rgb.shape[:2] | |||
# rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA) | |||
pts_map1 = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]]) | |||
pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]]) | |||
mat = cv2.getPerspectiveTransform(pts_map1,pts_map2) | |||
image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC) | |||
image,M = deskew.fastDeskew(image) | |||
return image | |||
#多级 | |||
def findContoursAndDrawBoundingBox2(image_rgb): | |||
line_upper = []; | |||
line_lower = []; | |||
line_experiment = [] | |||
grouped_rects = [] | |||
gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY) | |||
for k in np.linspace(-1.6, -0.2,10): | |||
# for k in np.linspace(-15, 0, 15): | |||
# # | |||
# thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k) | |||
# binary_niblack = gray_image > thresh_niblack | |||
# binary_niblack = binary_niblack.astype(np.uint8) * 255 | |||
binary_niblack = nt.niBlackThreshold(gray_image,19,k) | |||
# cv2.imshow("binary_niblack_opencv",binary_niblack_) | |||
# cv2.imshow("binary_niblack_skimage", binary_niblack) | |||
# cv2.waitKey(0) | |||
imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) | |||
for contour in contours: | |||
bdbox = cv2.boundingRect(contour) | |||
if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1000) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100): | |||
# cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1) | |||
line_upper.append([bdbox[0],bdbox[1]]) | |||
line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]]) | |||
line_experiment.append([bdbox[0],bdbox[1]]) | |||
line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]]) | |||
# grouped_rects.append(bdbox) | |||
rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE) | |||
leftyA, rightyA = fitLine_ransac(np.array(line_lower),2) | |||
rows,cols = rgb.shape[:2] | |||
# rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA) | |||
leftyB, rightyB = fitLine_ransac(np.array(line_upper),-4) | |||
rows,cols = rgb.shape[:2] | |||
# rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA) | |||
pts_map1 = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]]) | |||
pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]]) | |||
mat = cv2.getPerspectiveTransform(pts_map1,pts_map2) | |||
image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC) | |||
image,M= deskew.fastDeskew(image) | |||
return image |
@@ -0,0 +1,92 @@ | |||
#coding=utf-8 | |||
from keras.layers import Conv2D, Input,MaxPool2D, Reshape,Activation,Flatten, Dense | |||
from keras.models import Model, Sequential | |||
from keras.layers.advanced_activations import PReLU | |||
from keras.optimizers import adam | |||
import numpy as np | |||
import cv2 | |||
def getModel(): | |||
input = Input(shape=[16, 66, 3]) # change this shape to [None,None,3] to enable arbitraty shape input | |||
x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input) | |||
x = Activation("relu", name='relu1')(x) | |||
x = MaxPool2D(pool_size=2)(x) | |||
x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x) | |||
x = Activation("relu", name='relu2')(x) | |||
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x) | |||
x = Activation("relu", name='relu3')(x) | |||
x = Flatten()(x) | |||
output = Dense(2,name = "dense")(x) | |||
output = Activation("relu", name='relu4')(output) | |||
model = Model([input], [output]) | |||
return model | |||
model = getModel() | |||
model.load_weights("./model/model12.h5") | |||
def getmodel(): | |||
return model | |||
def gettest_model(): | |||
input = Input(shape=[16, 66, 3]) # change this shape to [None,None,3] to enable arbitraty shape input | |||
A = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input) | |||
B = Activation("relu", name='relu1')(A) | |||
C = MaxPool2D(pool_size=2)(B) | |||
x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(C) | |||
x = Activation("relu", name='relu2')(x) | |||
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x) | |||
K = Activation("relu", name='relu3')(x) | |||
x = Flatten()(K) | |||
dense = Dense(2,name = "dense")(x) | |||
output = Activation("relu", name='relu4')(dense) | |||
x = Model([input], [output]) | |||
x.load_weights("./model/model12.h5") | |||
ok = Model([input], [dense]) | |||
for layer in ok.layers: | |||
print(layer) | |||
return ok | |||
def finemappingVertical(image): | |||
resized = cv2.resize(image,(66,16)) | |||
resized = resized.astype(np.float)/255 | |||
res= model.predict(np.array([resized]))[0] | |||
print("keras_predict",res) | |||
res =res*image.shape[1] | |||
res = res.astype(np.int) | |||
H,T = res | |||
H-=3 | |||
#3 79.86 | |||
#4 79.3 | |||
#5 79.5 | |||
#6 78.3 | |||
#T | |||
#T+1 80.9 | |||
#T+2 81.75 | |||
#T+3 81.75 | |||
if H<0: | |||
H=0 | |||
T+=2; | |||
if T>= image.shape[1]-1: | |||
T= image.shape[1]-1 | |||
image = image[0:35,H:T+2] | |||
image = cv2.resize(image, (int(136), int(36))) | |||
return image |
@@ -0,0 +1,18 @@ | |||
import cv2 | |||
import numpy as np | |||
def niBlackThreshold( src, blockSize, k, binarizationMethod= 0 ): | |||
mean = cv2.boxFilter(src,cv2.CV_32F,(blockSize, blockSize),borderType=cv2.BORDER_REPLICATE) | |||
sqmean = cv2.sqrBoxFilter(src, cv2.CV_32F, (blockSize, blockSize), borderType = cv2.BORDER_REPLICATE) | |||
variance = sqmean - (mean*mean) | |||
stddev = np.sqrt(variance) | |||
thresh = mean + stddev * float(-k) | |||
thresh = thresh.astype(src.dtype) | |||
k = (src>thresh)*255 | |||
k = k.astype(np.uint8) | |||
return k | |||
# cv2.imshow() |
@@ -0,0 +1,246 @@ | |||
#coding=utf-8 | |||
from . import detect | |||
from . import finemapping as fm | |||
from . import segmentation | |||
import cv2 | |||
import time | |||
import numpy as np | |||
from PIL import ImageFont | |||
from PIL import Image | |||
from PIL import ImageDraw | |||
import json | |||
import sys | |||
from . import typeDistinguish as td | |||
import imp | |||
imp.reload(sys) | |||
fontC = ImageFont.truetype("./Font/platech.ttf", 14, 0); | |||
from . import e2e | |||
#寻找车牌左右边界 | |||
def find_edge(image): | |||
sum_i = image.sum(axis=0) | |||
sum_i = sum_i.astype(np.float) | |||
sum_i/=image.shape[0]*255 | |||
# print sum_i | |||
start= 0 ; | |||
end = image.shape[1]-1 | |||
for i,one in enumerate(sum_i): | |||
if one>0.4: | |||
start = i; | |||
if start-3<0: | |||
start = 0 | |||
else: | |||
start -=3 | |||
break; | |||
for i,one in enumerate(sum_i[::-1]): | |||
if one>0.4: | |||
end = end - i; | |||
if end+4>image.shape[1]-1: | |||
end = image.shape[1]-1 | |||
else: | |||
end+=4 | |||
break | |||
return start,end | |||
#垂直边缘检测 | |||
def verticalEdgeDetection(image): | |||
image_sobel = cv2.Sobel(image.copy(),cv2.CV_8U,1,0) | |||
# image = auto_canny(image_sobel) | |||
# img_sobel, CV_8U, 1, 0, 3, 1, 0, BORDER_DEFAULT | |||
# canny_image = auto_canny(image) | |||
flag,thres = cv2.threshold(image_sobel,0,255,cv2.THRESH_OTSU|cv2.THRESH_BINARY) | |||
print(flag) | |||
flag,thres = cv2.threshold(image_sobel,int(flag*0.7),255,cv2.THRESH_BINARY) | |||
# thres = simpleThres(image_sobel) | |||
kernal = np.ones(shape=(3,15)) | |||
thres = cv2.morphologyEx(thres,cv2.MORPH_CLOSE,kernal) | |||
return thres | |||
#确定粗略的左右边界 | |||
def horizontalSegmentation(image): | |||
thres = verticalEdgeDetection(image) | |||
# thres = thres*image | |||
head,tail = find_edge(thres) | |||
# print head,tail | |||
# cv2.imshow("edge",thres) | |||
tail = tail+5 | |||
if tail>135: | |||
tail = 135 | |||
image = image[0:35,head:tail] | |||
image = cv2.resize(image, (int(136), int(36))) | |||
return image | |||
#打上boundingbox和标签 | |||
def drawRectBox(image,rect,addText): | |||
cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0,0, 255), 2, cv2.LINE_AA) | |||
cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1, cv2.LINE_AA) | |||
img = Image.fromarray(image) | |||
draw = ImageDraw.Draw(img) | |||
#draw.text((int(rect[0]+1), int(rect[1]-16)), addText.decode("utf-8"), (255, 255, 255), font=fontC) | |||
draw.text((int(rect[0]+1), int(rect[1]-16)), addText, (255, 255, 255), font=fontC) | |||
imagex = np.array(img) | |||
return imagex | |||
from . import cache | |||
from . import finemapping_vertical as fv | |||
def RecognizePlateJson(image): | |||
images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1) | |||
jsons = [] | |||
for j,plate in enumerate(images): | |||
plate,rect,origin_plate =plate | |||
res, confidence = e2e.recognizeOne(origin_plate) | |||
print("res",res) | |||
cv2.imwrite("./"+str(j)+"_rough.jpg",plate) | |||
# print "车牌类型:",ptype | |||
# plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY) | |||
plate =cv2.resize(plate,(136,int(36*2.5))) | |||
t1 = time.time() | |||
ptype = td.SimplePredict(plate) | |||
if ptype>0 and ptype<4: | |||
plate = cv2.bitwise_not(plate) | |||
# demo = verticalEdgeDetection(plate) | |||
image_rgb = fm.findContoursAndDrawBoundingBox(plate) | |||
image_rgb = fv.finemappingVertical(image_rgb) | |||
cache.verticalMappingToFolder(image_rgb) | |||
# print time.time() - t1,"校正" | |||
print("e2e:",e2e.recognizeOne(image_rgb)[0]) | |||
image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY) | |||
cv2.imwrite("./"+str(j)+".jpg",image_gray) | |||
# image_gray = horizontalSegmentation(image_gray) | |||
t2 = time.time() | |||
res, confidence = e2e.recognizeOne(image_rgb) | |||
res_json = {} | |||
if confidence > 0.6: | |||
res_json["Name"] = res | |||
res_json["Type"] = td.plateType[ptype] | |||
res_json["Confidence"] = confidence; | |||
res_json["x"] = int(rect[0]) | |||
res_json["y"] = int(rect[1]) | |||
res_json["w"] = int(rect[2]) | |||
res_json["h"] = int(rect[3]) | |||
jsons.append(res_json) | |||
print(json.dumps(jsons,ensure_ascii=False,encoding="gb2312")) | |||
return json.dumps(jsons,ensure_ascii=False,encoding="gb2312") | |||
def SimpleRecognizePlateByE2E(image): | |||
t0 = time.time() | |||
images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1) | |||
res_set = [] | |||
for j,plate in enumerate(images): | |||
plate, rect, origin_plate =plate | |||
# plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY) | |||
plate =cv2.resize(plate,(136,36*2)) | |||
res,confidence = e2e.recognizeOne(origin_plate) | |||
print("res",res) | |||
t1 = time.time() | |||
ptype = td.SimplePredict(plate) | |||
if ptype>0 and ptype<5: | |||
# pass | |||
plate = cv2.bitwise_not(plate) | |||
image_rgb = fm.findContoursAndDrawBoundingBox(plate) | |||
image_rgb = fv.finemappingVertical(image_rgb) | |||
image_rgb = fv.finemappingVertical(image_rgb) | |||
cache.verticalMappingToFolder(image_rgb) | |||
cv2.imwrite("./"+str(j)+".jpg",image_rgb) | |||
res,confidence = e2e.recognizeOne(image_rgb) | |||
print(res,confidence) | |||
res_set.append([[],res,confidence]) | |||
if confidence>0.7: | |||
image = drawRectBox(image, rect, res+" "+str(round(confidence,3))) | |||
return image,res_set | |||
def SimpleRecognizePlate(image): | |||
t0 = time.time() | |||
images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1) | |||
res_set = [] | |||
for j,plate in enumerate(images): | |||
plate, rect, origin_plate =plate | |||
# plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY) | |||
plate =cv2.resize(plate,(136,36*2)) | |||
t1 = time.time() | |||
ptype = td.SimplePredict(plate) | |||
if ptype>0 and ptype<5: | |||
plate = cv2.bitwise_not(plate) | |||
image_rgb = fm.findContoursAndDrawBoundingBox(plate) | |||
image_rgb = fv.finemappingVertical(image_rgb) | |||
cache.verticalMappingToFolder(image_rgb) | |||
print("e2e:", e2e.recognizeOne(image_rgb)) | |||
image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_RGB2GRAY) | |||
# image_gray = horizontalSegmentation(image_gray) | |||
cv2.imshow("image_gray",image_gray) | |||
# cv2.waitKey() | |||
cv2.imwrite("./"+str(j)+".jpg",image_gray) | |||
# cv2.imshow("image",image_gray) | |||
# cv2.waitKey(0) | |||
print("校正",time.time() - t1,"s") | |||
# cv2.imshow("image,",image_gray) | |||
# cv2.waitKey(0) | |||
t2 = time.time() | |||
val = segmentation.slidingWindowsEval(image_gray) | |||
# print val | |||
print("分割和识别",time.time() - t2,"s") | |||
if len(val)==3: | |||
blocks, res, confidence = val | |||
if confidence/7>0.7: | |||
image = drawRectBox(image,rect,res) | |||
res_set.append(res) | |||
for i,block in enumerate(blocks): | |||
block_ = cv2.resize(block,(25,25)) | |||
block_ = cv2.cvtColor(block_,cv2.COLOR_GRAY2BGR) | |||
image[j * 25:(j * 25) + 25, i * 25:(i * 25) + 25] = block_ | |||
if image[j*25:(j*25)+25,i*25:(i*25)+25].shape == block_.shape: | |||
pass | |||
if confidence>0: | |||
print("车牌:",res,"置信度:",confidence/7) | |||
else: | |||
pass | |||
# print "不确定的车牌:", res, "置信度:", confidence | |||
print(time.time() - t0,"s") | |||
return image,res_set | |||
@@ -0,0 +1,154 @@ | |||
#coding=utf-8 | |||
from keras.models import Sequential | |||
from keras.layers import Dense, Dropout, Activation, Flatten | |||
from keras.layers import Conv2D,MaxPool2D | |||
from keras.optimizers import SGD | |||
from keras import backend as K | |||
K.set_image_dim_ordering('tf') | |||
import cv2 | |||
import numpy as np | |||
index = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9, "苏": 10, "浙": 11, "皖": 12, | |||
"闽": 13, "赣": 14, "鲁": 15, "豫": 16, "鄂": 17, "湘": 18, "粤": 19, "桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24, | |||
"藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36, | |||
"6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48, | |||
"J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60, | |||
"W": 61, "X": 62, "Y": 63, "Z": 64,"港":65,"学":66 ,"O":67 ,"使":68,"警":69,"澳":70,"挂":71}; | |||
chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", | |||
"琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", | |||
"B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", | |||
"Q", "R", "S", "T", "U", "V", "W", "X", | |||
"Y", "Z","港","学","O","使","警","澳","挂" ]; | |||
def Getmodel_tensorflow(nb_classes): | |||
# nb_classes = len(charset) | |||
img_rows, img_cols = 23, 23 | |||
# number of convolutional filters to use | |||
nb_filters = 32 | |||
# size of pooling area for max pooling | |||
nb_pool = 2 | |||
# convolution kernel size | |||
nb_conv = 3 | |||
# x = np.load('x.npy') | |||
# y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes) | |||
# weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3 | |||
# weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重,高频字优先 | |||
model = Sequential() | |||
model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1))) | |||
model.add(Activation('relu')) | |||
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) | |||
model.add(Dropout(0.25)) | |||
model.add(Conv2D(32, (3, 3))) | |||
model.add(Activation('relu')) | |||
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) | |||
model.add(Dropout(0.25)) | |||
model.add(Conv2D(512, (3, 3))) | |||
# model.add(Activation('relu')) | |||
# model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) | |||
# model.add(Dropout(0.25)) | |||
model.add(Flatten()) | |||
model.add(Dense(512)) | |||
model.add(Activation('relu')) | |||
model.add(Dropout(0.5)) | |||
model.add(Dense(nb_classes)) | |||
model.add(Activation('softmax')) | |||
model.compile(loss='categorical_crossentropy', | |||
optimizer='adam', | |||
metrics=['accuracy']) | |||
return model | |||
def Getmodel_ch(nb_classes): | |||
# nb_classes = len(charset) | |||
img_rows, img_cols = 23, 23 | |||
# number of convolutional filters to use | |||
nb_filters = 32 | |||
# size of pooling area for max pooling | |||
nb_pool = 2 | |||
# convolution kernel size | |||
nb_conv = 3 | |||
# x = np.load('x.npy') | |||
# y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes) | |||
# weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3 | |||
# weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重,高频字优先 | |||
model = Sequential() | |||
model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1))) | |||
model.add(Activation('relu')) | |||
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) | |||
model.add(Dropout(0.25)) | |||
model.add(Conv2D(32, (3, 3))) | |||
model.add(Activation('relu')) | |||
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) | |||
model.add(Dropout(0.25)) | |||
model.add(Conv2D(512, (3, 3))) | |||
# model.add(Activation('relu')) | |||
# model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool))) | |||
# model.add(Dropout(0.25)) | |||
model.add(Flatten()) | |||
model.add(Dense(756)) | |||
model.add(Activation('relu')) | |||
model.add(Dropout(0.5)) | |||
model.add(Dense(nb_classes)) | |||
model.add(Activation('softmax')) | |||
model.compile(loss='categorical_crossentropy', | |||
optimizer='adam', | |||
metrics=['accuracy']) | |||
return model | |||
model = Getmodel_tensorflow(65) | |||
#构建网络 | |||
model_ch = Getmodel_ch(31) | |||
model_ch.load_weights("./model/char_chi_sim.h5") | |||
# model_ch.save_weights("./model/char_chi_sim.h5") | |||
model.load_weights("./model/char_rec.h5") | |||
# model.save("./model/char_rec.h5") | |||
def SimplePredict(image,pos): | |||
image = cv2.resize(image, (23, 23)) | |||
image = cv2.equalizeHist(image) | |||
image = image.astype(np.float) / 255 | |||
image -= image.mean() | |||
image = np.expand_dims(image, 3) | |||
if pos!=0: | |||
res = np.array(model.predict(np.array([image]))[0]) | |||
else: | |||
res = np.array(model_ch.predict(np.array([image]))[0]) | |||
zero_add = 0 ; | |||
if pos==0: | |||
res = res[:31] | |||
elif pos==1: | |||
res = res[31+10:65] | |||
zero_add = 31+10 | |||
else: | |||
res = res[31:] | |||
zero_add = 31 | |||
max_id = res.argmax() | |||
return res.max(),chars[max_id+zero_add],max_id+zero_add | |||
@@ -0,0 +1,307 @@ | |||
#coding=utf-8 | |||
import cv2 | |||
import numpy as np | |||
# from matplotlib import pyplot as plt | |||
import scipy.ndimage.filters as f | |||
import scipy | |||
import time | |||
import scipy.signal as l | |||
from keras.models import Sequential | |||
from keras.layers import Dense, Dropout, Activation, Flatten | |||
from keras.layers import Conv2D, MaxPool2D | |||
from keras.optimizers import SGD | |||
from keras import backend as K | |||
K.set_image_dim_ordering('tf') | |||
def Getmodel_tensorflow(nb_classes): | |||
# nb_classes = len(charset) | |||
img_rows, img_cols = 23, 23 | |||
# number of convolutional filters to use | |||
nb_filters = 16 | |||
# size of pooling area for max pooling | |||
nb_pool = 2 | |||
# convolution kernel size | |||
nb_conv = 3 | |||
# x = np.load('x.npy') | |||
# y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes) | |||
# weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3 | |||
# weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重,高频字优先 | |||
model = Sequential() | |||
model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols,1))) | |||
model.add(Activation('relu')) | |||
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) | |||
model.add(Conv2D(nb_filters, (nb_conv, nb_conv))) | |||
model.add(Activation('relu')) | |||
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) | |||
model.add(Flatten()) | |||
model.add(Dense(256)) | |||
model.add(Dropout(0.5)) | |||
model.add(Activation('relu')) | |||
model.add(Dense(nb_classes)) | |||
model.add(Activation('softmax')) | |||
model.compile(loss='categorical_crossentropy', | |||
optimizer='sgd', | |||
metrics=['accuracy']) | |||
return model | |||
def Getmodel_tensorflow_light(nb_classes): | |||
# nb_classes = len(charset) | |||
img_rows, img_cols = 23, 23 | |||
# number of convolutional filters to use | |||
nb_filters = 8 | |||
# size of pooling area for max pooling | |||
nb_pool = 2 | |||
# convolution kernel size | |||
nb_conv = 3 | |||
# x = np.load('x.npy') | |||
# y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes) | |||
# weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3 | |||
# weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重,高频字优先 | |||
model = Sequential() | |||
model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols, 1))) | |||
model.add(Activation('relu')) | |||
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) | |||
model.add(Conv2D(nb_filters, (nb_conv * 2, nb_conv * 2))) | |||
model.add(Activation('relu')) | |||
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) | |||
model.add(Flatten()) | |||
model.add(Dense(32)) | |||
# model.add(Dropout(0.25)) | |||
model.add(Activation('relu')) | |||
model.add(Dense(nb_classes)) | |||
model.add(Activation('softmax')) | |||
model.compile(loss='categorical_crossentropy', | |||
optimizer='adam', | |||
metrics=['accuracy']) | |||
return model | |||
model = Getmodel_tensorflow_light(3) | |||
model2 = Getmodel_tensorflow(3) | |||
import os | |||
model.load_weights("./model/char_judgement1.h5") | |||
# model.save("./model/char_judgement1.h5") | |||
model2.load_weights("./model/char_judgement.h5") | |||
# model2.save("./model/char_judgement.h5") | |||
model = model2 | |||
def get_median(data): | |||
data = sorted(data) | |||
size = len(data) | |||
# print size | |||
if size % 2 == 0: # 判断列表长度为偶数 | |||
median = (data[size//2]+data[size//2-1])/2 | |||
data[0] = median | |||
if size % 2 == 1: # 判断列表长度为奇数 | |||
median = data[(size-1)//2] | |||
data[0] = median | |||
return data[0] | |||
import time | |||
def searchOptimalCuttingPoint(rgb,res_map,start,width_boundingbox,interval_range): | |||
t0 = time.time() | |||
# | |||
# for x in xrange(10): | |||
# res_map = np.vstack((res_map,res_map[-1])) | |||
length = res_map.shape[0] | |||
refine_s = -2; | |||
if width_boundingbox>20: | |||
refine_s = -9 | |||
score_list = [] | |||
interval_big = int(width_boundingbox * 0.3) # | |||
p = 0 | |||
for zero_add in range(start,start+50,3): | |||
# for interval_small in xrange(-0,width_boundingbox/2): | |||
for i in range(-8,int(width_boundingbox/1)-8): | |||
for refine in range(refine_s, int(width_boundingbox/2+3)): | |||
p1 = zero_add# this point is province | |||
p2 = p1 + width_boundingbox +refine # | |||
p3 = p2 + width_boundingbox + interval_big+i+1 | |||
p4 = p3 + width_boundingbox +refine | |||
p5 = p4 + width_boundingbox +refine | |||
p6 = p5 + width_boundingbox +refine | |||
p7 = p6 + width_boundingbox +refine | |||
if p7>=length: | |||
continue | |||
score = res_map[p1][2]*3 -(res_map[p3][1]+res_map[p4][1]+res_map[p5][1]+res_map[p6][1]+res_map[p7][1])+7 | |||
# print score | |||
score_list.append([score,[p1,p2,p3,p4,p5,p6,p7]]) | |||
p+=1 | |||
print(p) | |||
score_list = sorted(score_list , key=lambda x:x[0]) | |||
# for one in score_list[-1][1]: | |||
# cv2.line(debug,(one,0),(one,36),(255,0,0),1) | |||
# # | |||
# cv2.imshow("one",debug) | |||
# cv2.waitKey(0) | |||
# | |||
print("寻找最佳点",time.time()-t0) | |||
return score_list[-1] | |||
import sys | |||
sys.path.append('../') | |||
from . import recognizer as cRP | |||
from . import niblack_thresholding as nt | |||
def refineCrop(sections,width=16): | |||
new_sections = [] | |||
for section in sections: | |||
# cv2.imshow("section¡",section) | |||
# cv2.blur(section,(3,3),3) | |||
sec_center = np.array([section.shape[1]/2,section.shape[0]/2]) | |||
binary_niblack = nt.niBlackThreshold(section,17,-0.255) | |||
imagex, contours, hierarchy = cv2.findContours(binary_niblack,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) | |||
boxs = [] | |||
for contour in contours: | |||
x,y,w,h = cv2.boundingRect(contour) | |||
ratio = w/float(h) | |||
if ratio<1 and h>36*0.4 and y<16\ | |||
: | |||
box = [x,y,w,h] | |||
boxs.append([box,np.array([x+w/2,y+h/2])]) | |||
# cv2.rectangle(section,(x,y),(x+w,y+h),255,1) | |||
# print boxs | |||
dis_ = np.array([ ((one[1]-sec_center)**2).sum() for one in boxs]) | |||
if len(dis_)==0: | |||
kernal = [0, 0, section.shape[1], section.shape[0]] | |||
else: | |||
kernal = boxs[dis_.argmin()][0] | |||
center_c = (kernal[0]+kernal[2]/2,kernal[1]+kernal[3]/2) | |||
w_2 = int(width/2) | |||
h_2 = kernal[3]/2 | |||
if center_c[0] - w_2< 0: | |||
w_2 = center_c[0] | |||
new_box = [center_c[0] - w_2,kernal[1],width,kernal[3]] | |||
# print new_box[2]/float(new_box[3]) | |||
if new_box[2]/float(new_box[3])>0.5: | |||
# print "异常" | |||
h = int((new_box[2]/0.35 )/2) | |||
if h>35: | |||
h = 35 | |||
new_box[1] = center_c[1]- h | |||
if new_box[1]<0: | |||
new_box[1] = 1 | |||
new_box[3] = h*2 | |||
section = section[int(new_box[1]):int(new_box[1]+new_box[3]), int(new_box[0]):int(new_box[0]+new_box[2])] | |||
# cv2.imshow("section",section) | |||
# cv2.waitKey(0) | |||
new_sections.append(section) | |||
# print new_box | |||
return new_sections | |||
def slidingWindowsEval(image): | |||
windows_size = 16; | |||
stride = 1 | |||
height= image.shape[0] | |||
t0 = time.time() | |||
data_sets = [] | |||
for i in range(0,image.shape[1]-windows_size+1,stride): | |||
data = image[0:height,i:i+windows_size] | |||
data = cv2.resize(data,(23,23)) | |||
# cv2.imshow("image",data) | |||
data = cv2.equalizeHist(data) | |||
data = data.astype(np.float)/255 | |||
data= np.expand_dims(data,3) | |||
data_sets.append(data) | |||
res = model2.predict(np.array(data_sets)) | |||
print("分割",time.time() - t0) | |||
pin = res | |||
p = 1 - (res.T)[1] | |||
p = f.gaussian_filter1d(np.array(p,dtype=np.float),3) | |||
lmin = l.argrelmax(np.array(p),order = 3)[0] | |||
interval = [] | |||
for i in range(len(lmin)-1): | |||
interval.append(lmin[i+1]-lmin[i]) | |||
if(len(interval)>3): | |||
mid = get_median(interval) | |||
else: | |||
return [] | |||
pin = np.array(pin) | |||
res = searchOptimalCuttingPoint(image,pin,0,mid,3) | |||
cutting_pts = res[1] | |||
last = cutting_pts[-1] + mid | |||
if last < image.shape[1]: | |||
cutting_pts.append(last) | |||
else: | |||
cutting_pts.append(image.shape[1]-1) | |||
name = "" | |||
confidence =0.00 | |||
seg_block = [] | |||
for x in range(1,len(cutting_pts)): | |||
if x != len(cutting_pts)-1 and x!=1: | |||
section = image[0:36,cutting_pts[x-1]-2:cutting_pts[x]+2] | |||
elif x==1: | |||
c_head = cutting_pts[x - 1]- 2 | |||
if c_head<0: | |||
c_head=0 | |||
c_tail = cutting_pts[x] + 2 | |||
section = image[0:36, c_head:c_tail] | |||
elif x==len(cutting_pts)-1: | |||
end = cutting_pts[x] | |||
diff = image.shape[1]-end | |||
c_head = cutting_pts[x - 1] | |||
c_tail = cutting_pts[x] | |||
if diff<7 : | |||
section = image[0:36, c_head-5:c_tail+5] | |||
else: | |||
diff-=1 | |||
section = image[0:36, c_head - diff:c_tail + diff] | |||
elif x==2: | |||
section = image[0:36, cutting_pts[x - 1] - 3:cutting_pts[x-1]+ mid] | |||
else: | |||
section = image[0:36,cutting_pts[x-1]:cutting_pts[x]] | |||
seg_block.append(section) | |||
refined = refineCrop(seg_block,mid-1) | |||
t0 = time.time() | |||
for i,one in enumerate(refined): | |||
res_pre = cRP.SimplePredict(one, i ) | |||
# cv2.imshow(str(i),one) | |||
# cv2.waitKey(0) | |||
confidence+=res_pre[0] | |||
name+= res_pre[1] | |||
print("字符识别",time.time() - t0) | |||
return refined,name,confidence |
@@ -0,0 +1,56 @@ | |||
#coding=utf-8 | |||
from keras.models import Sequential | |||
from keras.layers import Dense, Dropout, Activation, Flatten | |||
from keras.layers import Conv2D, MaxPool2D | |||
from keras.optimizers import SGD | |||
from keras import backend as K | |||
K.set_image_dim_ordering('tf') | |||
import cv2 | |||
import numpy as np | |||
plateType = ["蓝牌","单层黄牌","新能源车牌","白色","黑色-港澳"] | |||
def Getmodel_tensorflow(nb_classes): | |||
# nb_classes = len(charset) | |||
img_rows, img_cols = 9, 34 | |||
# number of convolutional filters to use | |||
nb_filters = 32 | |||
# size of pooling area for max pooling | |||
nb_pool = 2 | |||
# convolution kernel size | |||
nb_conv = 3 | |||
# x = np.load('x.npy') | |||
# y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes) | |||
# weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3 | |||
# weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重,高频字优先 | |||
model = Sequential() | |||
model.add(Conv2D(16, (5, 5),input_shape=(img_rows, img_cols,3))) | |||
model.add(Activation('relu')) | |||
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool))) | |||
model.add(Flatten()) | |||
model.add(Dense(64)) | |||
model.add(Activation('relu')) | |||
model.add(Dropout(0.5)) | |||
model.add(Dense(nb_classes)) | |||
model.add(Activation('softmax')) | |||
model.compile(loss='categorical_crossentropy', | |||
optimizer='adam', | |||
metrics=['accuracy']) | |||
return model | |||
model = Getmodel_tensorflow(5) | |||
model.load_weights("./model/plate_type.h5") | |||
model.save("./model/plate_type.h5") | |||
def SimplePredict(image): | |||
image = cv2.resize(image, (34, 9)) | |||
image = image.astype(np.float) / 255 | |||
res = np.array(model.predict(np.array([image]))[0]) | |||
return res.argmax() | |||