Added Python3 support
Json Shen
8 years ago
parent
83b5cf3f85
commit
715249db17
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import cv2
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import os
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import hashlib
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def verticalMappingToFolder(image):
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name = hashlib.md5(image.data).hexdigest()[:8]
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print(name)
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cv2.imwrite("./cache/finemapping/"+name+".png",image)
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# -- coding: UTF-8
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import cv2
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import matplotlib.pyplot as plt
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from sklearn.cluster import KMeans
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import os
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boundaries = [
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([100,80,0],[240,220,110]), # yellow
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([0,40,50],[110,180,250]), # blue
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([0,60,0],[60,160,70]), # green
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]
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color_attr = ["黄牌","蓝牌",'绿牌','白牌','黑牌']
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threhold_green = 13
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threhold_blue = 13
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threhold_yellow1 = 50
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threhold_yellow2 = 70
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# plt.figure()
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# plt.axis("off")
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# plt.imshow(image)
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# plt.show()
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import numpy as np
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def centroid_histogram(clt):
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numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
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(hist, _) = np.histogram(clt.labels_, bins=numLabels)
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# normalize the histogram, such that it sums to one
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hist = hist.astype("float")
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hist /= hist.sum()
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# return the histogram
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return hist
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def plot_colors(hist, centroids):
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bar = np.zeros((50, 300, 3), dtype="uint8")
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startX = 0
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for (percent, color) in zip(hist, centroids):
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endX = startX + (percent * 300)
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cv2.rectangle(bar, (int(startX), 0), (int(endX), 50),
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color.astype("uint8").tolist(), -1)
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startX = endX
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# return the bar chart
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return bar
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def search_boundaries(color):
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for i,color_bound in enumerate(boundaries):
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if np.all(color >= color_bound[0]) and np.all(color <= color_bound[1]):
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return i
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return -1
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def judge_color(color):
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r = color[0]
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g = color[1]
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b = color[2]
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if g - r >= threhold_green and g - b >= threhold_green:
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return 2
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if b - r >= threhold_blue and b - g >= threhold_blue:
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return 1
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if r- b > threhold_yellow2 and g - b > threhold_yellow2:
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return 0
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if r > 200 and b > 200 and g > 200:
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return 3
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if r < 50 and b < 50 and g < 50:
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return 4
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return -1
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def judge_plate_color(img):
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image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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image = image.reshape((image.shape[0] * image.shape[1], 3))
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clt = KMeans(n_clusters=2)
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clt.fit(image)
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hist = centroid_histogram(clt)
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index = np.argmax(hist)
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#print clt.cluster_centers_[index]
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#color_index = search_boundaries(clt.cluster_centers_[index])
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color_index = judge_color(clt.cluster_centers_[index])
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if color_index == -1:
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if index == 0:
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secound_index = 1
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else:
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secound_index = 0
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color_index = judge_color(clt.cluster_centers_[secound_index])
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if color_index == -1:
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print(clt.cluster_centers_)
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bar = plot_colors(hist, clt.cluster_centers_)
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# show our color bart
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plt.figure()
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plt.axis("off")
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plt.imshow(bar)
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plt.show()
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if color_index != -1:
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return color_attr[color_index],clt.cluster_centers_[index]
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else:
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return None,clt.cluster_centers_[index]
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import json
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with open("/Users/universe/ProgramUniverse/zeusees/HyperLPR/config.json") as f:
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configuration = json.load(f)
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#coding=utf-8
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import numpy as np
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import cv2
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import time
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from matplotlib import pyplot as plt
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import math
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from scipy.ndimage import filters
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#
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# def strokeFiter():
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# pass;
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def angle(x,y):
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return int(math.atan2(float(y),float(x))*180.0/3.1415)
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def h_rot(src, angle, scale=1.0):
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w = src.shape[1]
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h = src.shape[0]
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rangle = np.deg2rad(angle)
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nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale
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nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale
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rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale)
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rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0]))
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rot_mat[0,2] += rot_move[0]
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rot_mat[1,2] += rot_move[1]
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return cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)
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pass
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def v_rot(img, angel, shape, max_angel):
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size_o = [shape[1],shape[0]]
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size = (shape[1]+ int(shape[0]*np.cos((float(max_angel )/180) * 3.14)),shape[0])
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interval = abs( int( np.sin((float(angel) /180) * 3.14)* shape[0]))
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pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
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if(angel>0):
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pts2 = np.float32([[interval,0],[0,size[1] ],[size[0],0 ],[size[0]-interval,size_o[1]]])
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else:
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pts2 = np.float32([[0,0],[interval,size[1] ],[size[0]-interval,0 ],[size[0],size_o[1]]])
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M = cv2.getPerspectiveTransform(pts1,pts2)
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dst = cv2.warpPerspective(img,M,size)
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return dst,M
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def skew_detection(image_gray):
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h, w = image_gray.shape[:2]
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eigen = cv2.cornerEigenValsAndVecs(image_gray,12, 5)
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angle_sur = np.zeros(180,np.uint)
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eigen = eigen.reshape(h, w, 3, 2)
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flow = eigen[:,:,2]
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vis = image_gray.copy()
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vis[:] = (192 + np.uint32(vis)) / 2
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d = 12
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points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
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for x, y in points:
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vx, vy = np.int32(flow[int(y), int(x)]*d)
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# cv2.line(rgb, (x-vx, y-vy), (x+vx, y+vy), (0, 355, 0), 1, cv2.LINE_AA)
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ang = angle(vx,vy)
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angle_sur[(ang+180)%180] +=1
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# torr_bin = 30
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angle_sur = angle_sur.astype(np.float)
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angle_sur = (angle_sur-angle_sur.min())/(angle_sur.max()-angle_sur.min())
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angle_sur = filters.gaussian_filter1d(angle_sur,5)
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skew_v_val = angle_sur[20:180-20].max()
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skew_v = angle_sur[30:180-30].argmax() + 30
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skew_h_A = angle_sur[0:30].max()
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skew_h_B = angle_sur[150:180].max()
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skew_h = 0
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if (skew_h_A > skew_v_val*0.3 or skew_h_B > skew_v_val*0.3):
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if skew_h_A>=skew_h_B:
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skew_h = angle_sur[0:20].argmax()
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else:
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skew_h = - angle_sur[160:180].argmax()
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return skew_h,skew_v
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def fastDeskew(image):
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image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
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skew_h,skew_v = skew_detection(image_gray)
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print("校正角度 h ",skew_h,"v",skew_v)
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deskew,M = v_rot(image,int((90-skew_v)*1.5),image.shape,60)
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return deskew,M
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if __name__ == '__main__':
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fn = './dataset/0.jpg'
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img = cv2.imread(fn)
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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skew_h,skew_v = skew_detection(gray)
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img = v_rot(img,(90-skew_v ),img.shape,60)
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# img = h_rot(img,skew_h)
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# if img.shape[0]>img.shape[1]:
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# img = h_rot(img, -90)
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plt.show()
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cv2.waitKey()
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import cv2
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import numpy as np
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watch_cascade = cv2.CascadeClassifier('./model/cascade.xml')
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def computeSafeRegion(shape,bounding_rect):
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top = bounding_rect[1] # y
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bottom = bounding_rect[1] + bounding_rect[3] # y + h
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left = bounding_rect[0] # x
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right = bounding_rect[0] + bounding_rect[2] # x + w
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min_top = 0
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max_bottom = shape[0]
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min_left = 0
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max_right = shape[1]
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# print "computeSateRegion input shape",shape
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if top < min_top:
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top = min_top
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# print "tap top 0"
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if left < min_left:
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left = min_left
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# print "tap left 0"
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if bottom > max_bottom:
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bottom = max_bottom
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#print "tap max_bottom max"
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if right > max_right:
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right = max_right
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#print "tap max_right max"
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# print "corr",left,top,right,bottom
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return [left,top,right-left,bottom-top]
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def cropped_from_image(image,rect):
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x, y, w, h = computeSafeRegion(image.shape,rect)
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return image[y:y+h,x:x+w]
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def detectPlateRough(image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05):
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print(image_gray.shape)
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if top_bottom_padding_rate>0.2:
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print("error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate)
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exit(1)
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height = image_gray.shape[0]
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padding = int(height*top_bottom_padding_rate)
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scale = image_gray.shape[1]/float(image_gray.shape[0])
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image = cv2.resize(image_gray, (int(scale*resize_h), resize_h))
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image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]]
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image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY)
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watches = watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40))
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cropped_images = []
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for (x, y, w, h) in watches:
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cropped_origin = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
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x -= w * 0.14
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w += w * 0.28
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y -= h * 0.6
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h += h * 1.1;
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cropped = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
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cropped_images.append([cropped,[x, y+padding, w, h],cropped_origin])
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return cropped_images
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@ -0,0 +1,63 @@
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#coding=utf-8
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from keras import backend as K
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from keras.models import load_model
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from keras.layers import *
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import numpy as np
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import random
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import string
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import cv2
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from . import e2emodel as model
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chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
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"琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
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"B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
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"Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空"
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];
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pred_model = model.construct_model("./model/ocr_plate_all_w_rnn_2.h5",)
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import time
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def fastdecode(y_pred):
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results = ""
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confidence = 0.0
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table_pred = y_pred.reshape(-1, len(chars)+1)
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res = table_pred.argmax(axis=1)
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for i,one in enumerate(res):
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if one<len(chars) and (i==0 or (one!=res[i-1])):
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results+= chars[one]
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confidence+=table_pred[i][one]
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confidence/= len(results)
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return results,confidence
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def recognizeOne(src):
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# x_tempx= cv2.imread(src)
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x_tempx = src
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# x_tempx = cv2.bitwise_not(x_tempx)
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x_temp = cv2.resize(x_tempx,( 160,40))
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x_temp = x_temp.transpose(1, 0, 2)
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t0 = time.time()
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y_pred = pred_model.predict(np.array([x_temp]))
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y_pred = y_pred[:,2:,:]
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# plt.imshow(y_pred.reshape(16,66))
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# plt.show()
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#
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# cv2.imshow("x_temp",x_tempx)
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# cv2.waitKey(0)
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return fastdecode(y_pred)
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#
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#
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# import os
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#
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# path = "/Users/yujinke/PycharmProjects/HyperLPR_Python_web/cache/finemapping"
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# for filename in os.listdir(path):
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# if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".bmp"):
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# x = os.path.join(path,filename)
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# recognizeOne(x)
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# # print time.time() - t0
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#
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# # cv2.imshow("x",x)
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# # cv2.waitKey()
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@ -0,0 +1,34 @@
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from keras import backend as K
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from keras.models import *
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from keras.layers import *
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from . import e2e
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def ctc_lambda_func(args):
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y_pred, labels, input_length, label_length = args
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y_pred = y_pred[:, 2:, :]
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return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
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def construct_model(model_path):
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input_tensor = Input((None, 40, 3))
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x = input_tensor
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base_conv = 32
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for i in range(3):
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x = Conv2D(base_conv * (2 ** (i)), (3, 3),padding="same")(x)
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x = BatchNormalization()(x)
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x = Activation('relu')(x)
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x = MaxPooling2D(pool_size=(2, 2))(x)
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x = Conv2D(256, (5, 5))(x)
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x = BatchNormalization()(x)
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x = Activation('relu')(x)
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x = Conv2D(1024, (1, 1))(x)
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x = BatchNormalization()(x)
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x = Activation('relu')(x)
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x = Conv2D(len(e2e.chars)+1, (1, 1))(x)
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x = Activation('softmax')(x)
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base_model = Model(inputs=input_tensor, outputs=x)
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base_model.load_weights(model_path)
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return base_model
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@ -0,0 +1,130 @@
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#coding=utf-8
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import cv2
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import numpy as np
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from . import niblack_thresholding as nt
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from . import deskew
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def fitLine_ransac(pts,zero_add = 0 ):
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if len(pts)>=2:
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[vx, vy, x, y] = cv2.fitLine(pts, cv2.DIST_HUBER, 0, 0.01, 0.01)
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lefty = int((-x * vy / vx) + y)
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righty = int(((136- x) * vy / vx) + y)
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return lefty+30+zero_add,righty+30+zero_add
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return 0,0
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#精定位算法
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def findContoursAndDrawBoundingBox(image_rgb):
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line_upper = [];
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line_lower = [];
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line_experiment = []
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grouped_rects = []
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gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
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# for k in np.linspace(-1.5, -0.2,10):
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for k in np.linspace(-50, 0, 15):
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# thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
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# 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()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue