def plot_images(folder, labels, targets, title): subs = { 0: 'Hands', 1: 'Objects-Scenes', 2: 'Humans', 3: 'Faces', 4: 'Animals', 5: 'Animal Faces', 6: 'Monkey Faces', 7: 'Fruits-Vegetables', } fig = plt.figure() images = list(glob.iglob(folder)) labels_correct = [i == j for i, j in zip(labels, targets)] labels = [subs.get(item, item) for item in labels] for i, file in enumerate(images): sub = fig.add_subplot(12, 10, i + 1) img = plt.imread(file) sub.axis('off') sub.set_title(labels[i], fontsize=8, y=-.28) if not labels_correct[i]: sub.title.set_color('red') sub.imshow(img) plt.suptitle(title) plt.subplot_tool() plt.show()
def plot_data(self, data): plt.clf() x = [i for i in range(4096)] y = [] for elt in x: y.append(data.value[elt]) N = 50 cumsum, y_mean = [0], [] for i, yy in enumerate(y, 1): cumsum.append(cumsum[i - 1] + yy) if i >= N: mean = (cumsum[i] - cumsum[i - N]) / N y_mean.append(mean) plt.scatter(x, y, s=0.1, c="r") plt.plot(x[int(N / 2):-(int(N / 2) - 1)], y_mean) plt.ylabel('Hits/h') plt.xlabel('Channel') plt.subplot_tool() plt.gcf() App.get_running_app().graphWidget.draw_idle()
def blackwhite_colorized_comparison(dir_color): images = os.listdir(dir_color) # dfine plot num_col = 6 num_rows = math.ceil(len(images) * 2 / num_col) plt.figure(figsize=(num_col * 2.5 + 1, (num_rows + 2) * 2.5 + 1)) gs1 = gridspec.GridSpec(num_rows + 2, num_col, width_ratios=[1] * num_col, wspace=0.03, hspace=0.03, top=1, bottom=0, left=0, right=1) # make plots for i, image in enumerate(images): image1 = load_images(dir_color, image) # plot image ax1 = plt.subplot(gs1[i * 2]) ax1.imshow(image1[:, :, 0], cmap='gray') ax2 = plt.subplot(gs1[i * 2 + 1]) ax2.imshow(color.lab2rgb(image1)) ax1.axis('off') ax2.axis('off') plt.subplot_tool() plt.savefig("../../../black-colored-comparison.jpg", bbox_inches='tight') plt.close()
def main(): parser = argparse.ArgumentParser() parser.add_argument('-i', '--image', type=str, help='input image path') args = parser.parse_args() image_name = args.image img = cv2.imread(image_name, 0) Kernel_K = np.array([[2, 4, 5, 4, 2], [4, 9, 12, 9, 4], [5, 12, 15, 12, 5], [4, 9, 12, 9, 4], [2, 4, 5, 4, 2]], np.float32) / 159 Kernel_Gx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], np.float32) Kernel_Gy = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], np.float32) img_smooth = filterKernel(img, Kernel_K) img_smooth = img_smooth.astype(np.uint8) img_x = filterKernel(img_smooth, Kernel_Gx) img_y = filterKernel(img_smooth, Kernel_Gy) img_cover = np.sqrt(np.square(img_x) + np.square(img_y)) img_cover = img_cover.astype(np.uint8) for i in np.arange(img_cover.shape[0]): for j in np.arange(img_cover.shape[1]): if img_cover[i, j] > 100 and img_cover[i, j] < 200: img_cover[i, j] = 255 else: img_cover[i, j] = 0 # curr = img_cover[i-1:i+2,j-1:j+2] # curr[curr < 100] = 0 # curr[curr > 200] = 0 # img_cover[i-1:i+2,j-1:j+2]=curr # cv2.imwrite("gr_img.jpg", img_cover) # cv2.imshow('Original', img) # cv2.imshow("Smooth", img_smooth) # cv2.imshow('Gradient', img_cover) plt.figure() plt.subplot(131) plt.imshow(img, cmap='gray') plt.subplot(132) plt.imshow(img_smooth, cmap='gray') plt.subplot(133) plt.imshow(img_cover, cmap='gray') plt.subplot_tool() plt.show()
def plot_latencies(ip_addresses, values): fig, ax = plt.subplots(nrows=1, ncols=1) plt.subplots_adjust(wspace=0.6, hspace=0.6, left=0.1, bottom=0.35, right=0.96, top=0.90) plt.xticks(range(len(ip_addresses)), ip_addresses, rotation=90) fig.suptitle('Latency by video IP address') plt.xlabel('Video IP address') plt.ylabel('Ping average (ms)') plt.plot(ip_addresses, values) plt.subplot_tool() fig.savefig('resources/plots/latency_pytomo.png') plt.close(fig)
def 工具栏(): fig, axs = plt.subplots(2, 2) axs[0, 0].imshow(np.random.random((100, 100))) axs[0, 1].imshow(np.random.random((100, 100))) axs[1, 0].imshow(np.random.random((100, 100))) axs[1, 1].imshow(np.random.random((100, 100))) plt.subplot_tool() plt.show()
def compute_and_diplay_saliency_map(layer_unit_1): try: fig, axs = mplt.subplots(2, 2) axs[0, 0].imshow(layer_unit_1[0, :, :, 0], cmap="hot") axs[0, 1].imshow(layer_unit_1[1, :, :, 0], cmap="hot") axs[1, 0].imshow(layer_unit_1[2, :, :, 0], cmap="hot") axs[1, 1].imshow(layer_unit_1[3, :, :, 0], cmap="hot") mplt.subplot_tool() mplt.show() except Exception as e: print(e)
def render_chars(chars): fig, axs = plt.subplots(1,len(chars)) for ix, char in enumerate(chars): axs[ix].imshow(char) plt.subplot_tool() plt.show()
except: #fig.savefig('mbotEXCEPTION.tiff') #print("an exception occurred",exception) #fig.savefig('mbot.tiff') #fig2.savefig('mbottt.tiff') ''' def animate(i): x = np.linspace(0, 2, 1000) y = np.sin(2 * np.pi * (x - 0.01 * i)) line.set_data(x, y) return line, try: # call the animator. blit=True means only re-draw the parts that have changed. anim = animation.FuncAnimation(fig, animate, init_func=init, frames=20, interval=20, blit=True) # save the animation as an mp4. This requires ffmpeg or mencoder to be # installed. The extra_args ensure that the x264 codec is used, so that # the video can be embedded in html5. You may need to adjust this for # your system: for more information, see # http://matplotlib.sourceforge.net/api/animation_api.html anim.save('basic_animation.mp4', fps=10, extra_args=['-vcodec', 'libx264']) except: print("an exception ocurred in animate().") ''' plt.subplot_tool(targetfig=fig) print("Done.") #plt.show()
return fname imgx = 20 imgy = 0 def pltshow(plt, dpi=150): global imgx, imgy temppath = tempimage() plt.savefig(temppath, dpi=dpi) dx,dy = imagesize(temppath) w = min(W,dx) image(temppath,imgx,imgy,width=w) imgy = imgy + dy + 20 os.remove(temppath) size(W, HEIGHT+dy+40) else: def pltshow(mplpyplot): mplpyplot.show() # nodebox section end fig, axs = plt.subplots(2, 2) axs[0, 0].imshow(np.random.random((100, 100))) axs[0, 1].imshow(np.random.random((100, 100))) axs[1, 0].imshow(np.random.random((100, 100))) axs[1, 1].imshow(np.random.random((100, 100))) plt.subplot_tool() pltshow(plt)
""" =============== Subplot Toolbar =============== Matplotlib has a toolbar available for adjusting subplot spacing. """ import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed(19680801) fig, axs = plt.subplots(2, 2) axs[0, 0].imshow(np.random.random((100, 100))) axs[0, 1].imshow(np.random.random((100, 100))) axs[1, 0].imshow(np.random.random((100, 100))) axs[1, 1].imshow(np.random.random((100, 100))) plt.subplot_tool() plt.show()
def subplot_tool(*args,**kwargs): return plt.subplot_tool(*args,**kwargs)
lista_alpha0 = [1] sangre(lista_alpha0, 0) plt.subplot(2, 3, 4) plt.title("Pregunta 2.A.2(alpha = 1/2)") plt.stem(n, lista_alpha0) lista_alpha1 = [1] sangre(lista_alpha1, 1) plt.subplot(2, 3, 5) plt.title("Pregunta 2.A.2(alpha = 1/3)") plt.stem(n, lista_alpha1) N = np.arange(1, 50, 1) n = np.arange(0, 50, 1) lista_alpha2 = [1] sangre(lista_alpha2, 2) plt.subplot(2, 3, 6) plt.title("Pregunta 2.A.2(alpha = 7/8)") plt.stem(n, lista_alpha2) ####################################################################################### #A.3 # PARA alpha = 1/2 ----> lim n->00 y[n] = 2 # PARA alpha = 1/3 ----> lim n->00 y[n] = 1.5 # PARA alpha = 7/8 ----> lim n->00 y[n] = 8 plt.subplot_tool( ) # Esto abre una ventana para poder visualizar mejor los graficos, puedes ajustarlos como quieras. plt.show()
'gyrox': 10, 'gyroy': 11, 'gyroz': 12, } if __name__ == "__main__": pickle_gauss = open("results_Gaussian.pickle", "rb") results_Gaussian = pickle.load(pickle_gauss) pickle_gauss.close() pickle_gmm = open("results_GMM.pickle", "rb") results_GMM = pickle.load(pickle_gmm) pickle_gmm.close() fig1 = plt.figure(1) plt.subplot_tool(targetfig=fig1) for i, result in enumerate(results_GMM): plt.subplot(4, 3, i + 1) plt.imshow(result['img']) plt.title(result['title']) plt.subplot(4, 3, i + 4) colors = ["blue"] * 25 + ["green"] * 20 + ["red"] * 20 x = np.arange(len(colors)) y = result['scores'][9] #log prob of user 9 plt.scatter(x, y, c=colors) plt.title(result['title']) for i, result in enumerate(results_Gaussian): plt.subplot(4, 3, i + 7) plt.imshow(result['img']) plt.title(result['title']) plt.subplot(4, 3, i + 10)
def subplot_tool(*args, **kwargs): return plt.subplot_tool(*args, **kwargs)
def main(): # path_test = '/home/sushobhan/caffe/data/ptychography/databases/Test42_Set91_img512_patch48/test_images/' # path_test = "/home/sushobhan/Documents/data/ptychography/Test42_Set91_img512_patch48/test_images/" # path_test = "/home/sushobhan/Documents/data/ptychography/" path_test = "/home/sushobhan/Documents/data/ptychography/Test40_Set91_img512_patch48/test_images/" home = "/home/sushobhan/Documents/research/ptychography/" model_name = sys.argv[1] crop_size = 4 border_mode = 'valid' # file_name = 'lena_1.h5' # file_name = 'resChart.h5' file_name = 'set_1.h5' file = h5py.File(path_test + file_name, 'r') ks = file.keys() print ks data = file['data'] label = file['label'] # data = np.max(data) - data # label = np.max(label) - label if file_name == "lena.h5" or file_name == "resChart.h5" or file_name == "lena_1.h5": data = np.expand_dims(file['data'], axis=0) label = np.expand_dims(np.expand_dims(file['label'], axis=0), axis=0) # label = np.transpose(label,(0,1,3,2)) # im.imsave('label.png',label[0,0,],cmap=plt.cm.gray) # im.imsave('data.png',data[0,24,],cmap=plt.cm.gray) model = load_model(home + 'models/' + model_name + '.h5') y_output = np.array(model.predict(data)) if border_mode == 'valid': data = crop(data, crop_size) label = crop(label, crop_size) print np.max(data), np.max(label) print y_output.shape, np.max(y_output) print data.shape, label.shape im.imsave('label.png', label[0, 0, ], cmap=plt.cm.gray) im.imsave('data.png', data[0, 24, ], cmap=plt.cm.gray) im.imsave('output.png', y_output[0, 0, ], cmap=plt.cm.gray) fig = plt.figure(0) m, n = 2, 2 for i in range(0, 1): # print i j, k = i // n, i % n # print j,k plt.subplot2grid((m, n), (j, k)) plt.imshow(label[i, 0, ], cmap=plt.cm.gray) # print j+2, k plt.subplot2grid((m, n), (j + 1, k)) plt.imshow(y_output[i, 0, ], cmap=plt.cm.gray) plt.subplot2grid((m, n), (j, k + 1)) plt.imshow(data[i, 24, ], cmap=plt.cm.gray) print compare_psnr(label[i, 0, ], y_output[i, 0, ]) print compare_psnr(label[i, 0, ], data[i, 24, ]) plt.subplot_tool() plt.savefig(model_name + '.jpg') psnr_center = [] psnr_output = [] for i in range(data.shape[0]): psnr_center.append(compare_psnr(label[i, 0, ], data[i, 24, ])) psnr_output.append(compare_psnr(label[i, 0, ], y_output[i, 0, ])) print np.mean(psnr_output) print np.mean(psnr_center)