def plot2d(data, cmap='gray_r', **kwargs): #if datax.ndim != datay.ndim: # print "data set sizes don't match!" # return 0 #num = np.arange(0, datax.ndim, 1) #f, axarr = plt.subplots(datax.ndim) #for n in num: # axarr[n].plot(datax[n], datay[n]) # axarr[n].set_title for name, value in kwargs.items(): print "%s = %f" %(name, value) plt.figure() plt.imgshow(kwargs['img'], cmap=kwargs['cmap'] )
from app import filtroFauna, filtroFlora from PIL import Image from matplotlib import pyplot as plt imgFlora = Image.open("img/floresta-amazonia.jpg") resfl = filtroFlora(imgFlora) plt.imshow(resfl) plt.show() imgFauna = Image.open("img/arara-azul-voando.jpg") resfa = filtroFauna(imgFauna) plt.imshow(resfa) plt.show() # Juntando imagens. plt.imgshow(resfa, resfl)
val_loss = autoencoder_train.history['val_loss'] epochs = range(epochs) plt.figure() plt.plot(epochs, loss, 'bo', label='Training loss') plt.plot(epochs, val_loss, 'b', label='Validation loss') plt.title('Training and validation loss') plt.legend() plt.show() pred = autoencoder.predict(test_data) print(pred.shape) plt.figure(figsize=(20, 4)) print("Test Images") for i in range(10): plt.subplot(2, 10, i + 1) plt.imshow(test_data[i, ..., 0], cmap='gray') curr_lbl = test_labels[i] plt.title("(Label: " + str(label_dict[curr_lbl]) + ")") plt.show() plt.figure(figsize=(20, 4)) print("reconstruction of test images") for i in range(10): plt.subplot(2, 10, i + 1) plt.imgshow(pred[i, ..., 0], cmap='gray') import pylab as p p.show()
def plot(self): plt.imgshow(self.image, cmap=plt.cm.gray) plt.axis('off') plt.show()
import pylab as pl digits = load_digits() #Analyzing one image. pl.gray() pl.matshow(digits.images[0]) pl.show() #Visualizing first 15 images with their labels. data = list(zip(digits.images, digits.target)) plt.figure(figsize=(5, 5)) for item, (img, label) in enumerate(data[:15]): plt.subplot(3, 5, item + 1) plt.axis('off') plt.imgshow(img, cmap=plt.cm.gray_r, interpolation='nearest') plt.tittle('%i' % label) import random from sklean import ensemble #Dividing our data in order to use it as a supervised learning. n = len(digits.images) x = digits.images.reshape((n, -1)) y = digits.target #Random indices. sample_index = random.sample(range(len(x)), len(x) / 5) valid_index = [i for i in range(len(x)) if i not in sample_index] #Images and targets to work.
Este é um arquivo de script temporário. """ #%% import cv2 import matplotlib.pyplot as plt import numpy as np #%% img = cv2.imread('onepiece.jpeg', cv2.IMREAD_GRAYSCALE) plt.imshow(img, cmap='gray') #%% img = cv2.imread('onepiece.jpeg', cv2.IMREAD_GRAYSCALE) img = cv2.cvtColor(img, cv2.IMREAD_COLOR) plt.imshow(img) #%% rgb = cv2.split(img) plt.subplot(221), plt.imgshow(img) plt.subplot(222), plt.title('R'), plt.imshow(rgb[0], cmap='gray') plt.subplot(223), plt.title('G'), plt.imshow(rgb[1], cmap='gray') plt.subplot(224), plt.title('B'), plt.imshow(rgb[2], cmap='gray') plt.show() #%% img = cv2.imread('download.jpeg', cv2.IMREAD_GRAYSCALE) plt.subplot(221), plt.title('Original'), plt.imshow(img, cmap='gray') plt.subplot(222), plt.title('Histogram'), plt.hist(img.ravel(), 256, [0, 256]) plt.show()
image = tf.convert_image_dtype(image, dtype=tf.float32) bbox_begin,bbox_size,_ = tf.image.sample_distorted_bounding_box(tf.shape(image),bounding_boxes=bbox)#随机截取图像 distort_image = tf.slice(image, bbox_begin, bbox_size) distorted_image = tf.image.resize_images(distort_image,[height,width],method=np.randint(4))#调整图像大小为神经网络的输入大小 distort_image = tf.image.random_flip_left_right(distort_image)#随机左右翻转图像 distort_image = distort_color(distort_image,np.random.randint(2))#随机调整图像颜色 return distort_image image_raw_data = tf.gfile.FastGFile(path,'rb').read() with tf.Session() as sess: image_data = tf.image.decode(image_raw_data) boxes = tf.constant([[[0.05,0.05,0.9,0.7],[0.35,0.47,0.5,0.56]]]) for i in range(6) result = preprocess_for_train(image_data,299,299,boxes) plt.imgshow(result.eval()) plt.show() 多线程处理数据输入 队列,处理输入数据的框架 import tensorflow as tf q = tf.FIFOQueue(2,'int32')#指定一个先进先出队列,可以保存两个元素 #RandomShufffleQueue是随机进出队列 init = q.enqueue_many(([0,10],))#使用函数初始化队列中的元素,元素的值为0和10 x = q.dequeue()#出队列 y = x + 1 q_inc = q.enqueue([y])#加入队列 with tf.Session() as tf: init.run()#初始化队列
#plt.plot(resnet18.TEST_LOSS, label="Testing Loss ResNet18") #plt.legend() #plt.savefig(os.path.join("plots", "final_loss.png")) #plt.show() #plt.figure(figsize=(12, 8)) #plt.title("Accuracy") #plt.plot(model21.VALIDATION_ACC, label="Validation Accuracy Model 21") #plt.plot(model21.TRAIN_ACC, label="Training Accuracy Model 21") #plt.plot(model21.TEST_ACC, label="Testing Accuracy Model 21") #plt.plot(resnet18.VALIDATION_ACC, label="Validation Accuracy ResNet18") #plt.plot(resnet18.TRAIN_ACC, label="Training Accuracy ResNet18") #plt.plot(resnet18.TEST_ACC, label="Testing Accuracy ResNet18") #plt.legend() #plt.savefig(os.path.join("plots", "final_accuracy.png")) #plt.show() #Visualizing filters to_vis = resnet18.visualize_first_filter_resnet18() plt.imgshow(to_vis) plt.savefig(os.path.join("plots", "first_layer_filters.png")) plt.show() #print("Final test accuracy:", trainer.TEST_ACC[-trainer.early_stop_count]) #print("Final test loss:", trainer.TEST_LOSS[-trainer.early_stop_count]) #print("Final validation accuracy:", trainer.VALIDATION_ACC[-trainer.early_stop_count]) #print("Final validation loss:", trainer.VALIDATION_LOSS[-trainer.early_stop_count]) #print("Final ttaining accuracy:", trainer.TRAIN_ACC[-trainer.early_stop_count]) #print("Final training loss:", trainer.TRAIN_LOSS[-trainer.early_stop_count]) #print("Total number of epochs " + str(trainer.number_of_epochs))
def plot_digit(X,y,idx): img = X[idx].reshape(28,28) plt.imgshow(img,cmap="Greys") plt.show()
import pandas as pd import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import fetch_mldata dataset = fetch_mldata('MNIST original') X = dataset.data y = dataset.target some_digit = X[62302] some_digit_image = some_digit_reshape(28, 28) plt.imgshow(some_digit_image) plt.show() from sklearn.tree import DecisionTreeClassifier dtf = DecisionTreeClassifier() dtf.fit(X, y) dtf.score(X, y) dtf.predict(X[[17,2703, 13413, 56404, 62302], ]) from sklearn.tree import export_graphviz export_graphviz(dtf, out_file="tree.dot") import graphviz with open("tree.dot") as f: dot_graph = f.read()
# Resimdeki ana hatların ortaya çıkarılması sağlanır. import cv2 import matplotlib.pyplot as plt # Resmi içeri aktarma img = cv2.imread("image1.jpg") img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #GrayScale convert # Thresh ile maxval arasındaki değerler beyaz olur. # cv2.THRESH_BINARY_INV uygulanırsa tam tersi olur. Yani beyaz yerine siyah olur. _, thresh_img = cv2.threshold(img, thresh=60, maxval=255, type=cv2.THRESH_BINARY) # Uyarlamalı eşik değeri # Bir dağ resminde bazı bölgerine ışık az düşer ise o bölge koyu renk olur. # Bu algoritmada dağı bir bütün olarak algılar ve renk dağılımını eşit uygular. thresh_img2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 8) #Görselleştirme plt.figure() plt.imgshow(thresh_img2, cmap="gray ") plt.axis("off") #eksenler kapanır. plt.show()