【深度学习】利用OpenCV+ConvNets检测几何图形

 #detect checkbox/squaredef extract_quads(self,image_arr,name_arr):
    if len(image_arr) > 0:
        for index,original_image in enumerate(image_arr):
            #to store extracted images            extracted_quad = []            image = original_image.copy()          
            #grayscale only if its not already            if len(image.shape) > 2:                gray = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2GRAY)              else:                gray = image.copy()
            #image preprocessing for quadrilaterals            img_dilate = self.do_quad_imageprocessing(gray,self.blocksize,self.thresh_const,self.kernelsize)   
            if len(img_dilate) > 0:
                try:                    #detect contours                    cnts = cv2.findContours(img_dilate.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)                    cnts = imutils.grab_contours(cnts)
                    #loop through detected contours                    for c in cnts:                        peri = cv2.arcLength(c, True)                        approx = cv2.approxPolyDP(c, (self.epsilon)* peri, True)
                        #bounding rec cordinates                        (x, y, w, h) = cv2.boundingRect(approx)
                        #get the aspect ratio                        aspectratio = float(w/h)


    
                        area = cv2.contourArea(c)                         if area < self.rec_max_area and area > self.rec_min_area and (aspectratio >= self.aspect_ratio[0] and aspectratio <= self.aspect_ratio[1]):
                            #check if there are 4 corners in the polygon                            if len(approx) == 4:                                                 cv2.drawContours(original_image,[c], 0, (0,255,0), 2)                                roi = original_image[y:y+h, x:x+w]                                extracted_quad.append(roi)
                except Exception as e:                    print('The following exception occured during quad shape detection: ',e)
            self.extracted_img_data.append([original_image,extracted_quad,name_arr[index]])
    else:                    print('No image is found during the extraction process')

def Img2Pdf(dirname):        images = []        #get the pdf file    for x in os.listdir(dirname):        if (dirname.split('.')[1]) == 'pdf':            pdf_filename = x            images_from_path = convert_from_path(os.path.join(dirname),dpi=300, poppler_path = r'C:\Program Files (x86)\poppler-0.68.0_x86\poppler-0.68.0\bin')for image in images_from_path:                images.append(np.array(image))                    return images

#keras thingsfrom keras.utils import to_categoricalfrom keras import layersfrom keras import modelsfrom keras.regularizers import l2

Y_test_orig = to_categorical(Y_test_orig, num_classes=2) Y_train_orig = to_categorical(Y_train_orig, num_classes=2)
# 3 layer ConvNetmodel = models.Sequential()model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(32,32,1)))model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))
#dense layermodel.add(layers.Flatten())
#add the regulizermodel.add(layers.Dense(128, activation='linear', activity_regularizer=l2(0.0003)))model.add(layers.Dense(128, activation='relu'))model.add(layers.Dense(2, activation='sigmoid'))  
model.summary()
from keras.optimizers import Adamopt = Adam(lr=0.001)model.compile(optimizer=opt, loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])
ntrain = len(X_train_orig)nval = len(X_test_orig)X_train_orig = X_train_orig.reshape((len(X_train_orig),32,32,1)) X_test_orig = X_test_orig.reshape((len(X_test_orig),32,32,1))
train_datagen = ImageDataGenerator(rescale = 1./255,rotation_range = 40, width_shift_range = .2,                                  height_shift_range = .2, shear_range = .2, zoom_range =  .2, horizontal_flip = True)
val_datagen = ImageDataGenerator(rescale = 1./255)
train_generator = train_datagen.flow(X_train_orig,Y_train_orig,batch_size=32)val_generator = val_datagen.flow(X_test_orig,Y_test_orig,batch_size = 32)



    

#X_train_orig, X_test_orig, Y_train_orig,Y_test_orighistory = model.fit_generator(train_generator,steps_per_epoch = ntrain/32, epochs = 64, validation_data = val_generator, validation_steps = nval/32  )