def fimage(x, y, file, type=1): if file[:3] == '/sd': utils.filecp(file, '/flash/fcache', blocksize=8192) lcd.image(x, y, '/flash/fcache', 0, type) os.remove('/flash/fcache') else: lcd.image(x, y, file, 0, type)
def main(): remote.synchronise() files = parser.parse_file_list(remote.get_content()) while len(files) == 0: print("No files found, retrying...") remote.synchronise() files = parser.parse_file_list(remote.get_content()) time.sleep(20) while True: for file in files: if file.type == parser.FileType.IMAGE: display.image(file) elif file.type == parser.FileType.VIDEO: display.video(file) else: print("Unknown file type of " + str(file.path))
def showPic(imgs, locs, win=0, name="Real"): imgs = [(x + 0.5) / 2 * 255 for x in imgs] imgs = [cv2.flip(x.transpose(1, 2, 0), 0) for x in imgs] # imgs = [x.transpose(1, 2, 0) for x in imgs] for i in range(4): for y in range(16): for x in range(16): if locs[i][y][x] > 0.3: cv2.rectangle(imgs[i], (x * 8, 127 - y * 8), (x * 8 + 8, 119 - y * 8), (0, 0, 255), 1) # cv2.rectangle(imgs[i], (x * 8, y * 8), (x * 8 + 8, y * 8 + 8), (0, 0, 255), 1) half = len(imgs) // 2 row1 = np.concatenate(imgs[:half], 1) row2 = np.concatenate(imgs[half:], 1) content = np.concatenate((row1, row2), 0) try: display.image(content, win=win, title=name) except Exception as e: print(e)
import random import time import display def generate_image(): X, Y = numpy.meshgrid(numpy.linspace(0, numpy.pi, 512), numpy.linspace(0, 2, 512)) z = (numpy.sin(X) + numpy.cos(Y))**2 + 0.5 return z i1 = generate_image() i2 = generate_image() display.image(i1, title='gradient') # display.images([i2, i2, i2, i2], width=200, title='super fabio', labels=['a', 'b', 'c', 'd']) data = [] for i in range(15): data.append([i, random.random(), random.random() * 2]) win = display.plot(data, labels=['position', 'a', 'b'], title='progress') for i in range(15, 25): time.sleep(0.2) data.append([i, random.random(), random.random() * 2]) display.plot(data, win=win)
out_folder_name = time_info.strftime("%Y-%m-%d") + '_' \ + input_name[:-4] \ + '_2D' + time_info.strftime("_%H%M") if not os.path.exists('./Trained_models/' + out_folder_name): os.mkdir('./Trained_models/' + out_folder_name) copyfile('./train_g2d_periodic.py', './Trained_models/' + out_folder_name + '/code.txt') # load images input_texture = Image.open('./Images/' + input_name) input_torch = Variable(prep(input_texture)).unsqueeze(0).cuda() # display images if disp: img_disp = numpy.asarray(input_texture, dtype="int32") display.image(img_disp, win='input', title='Input texture') #define layers, loss functions, weights and compute optimization target out_keys = ['r11', 'r21', 'r31', 'r41', 'r51'] loss_fns = [GramMSELoss()] * len(out_keys) loss_fns = [loss_fn.cuda() for loss_fn in loss_fns] # these are the weights settings recommended by Gatys # to use with Gatys' normalization: # w = [1e2/n**3 for n in [64,128,256,512,512]] w = [1, 1, 1, 1, 1] #compute optimization targets targets = [GramMatrix()(f).detach() for f in vgg_net(input_torch, out_keys)] # training parameters batch_size = 10
#!/usr/bin/env python import numpy import random import time import display def generate_image(): X, Y = numpy.meshgrid(numpy.linspace(0, numpy.pi, 512), numpy.linspace(0, 2, 512)) z = (numpy.sin(X) + numpy.cos(Y)) ** 2 + 0.5 return z i1 = generate_image() i2 = generate_image() display.image(i1, title='gradient') # display.images([i2, i2, i2, i2], width=200, title='super fabio', labels=['a', 'b', 'c', 'd']) data = [] for i in range(15): data.append([i, random.random(), random.random() * 2]) win = display.plot(data, labels=[ 'position', 'a', 'b' ], title='progress') for i in range(15, 25): time.sleep(0.2) data.append([i, random.random(), random.random() * 2]) display.plot(data, win=win)
import cv2 import display display.set_port(9000) def generate_image(): X, Y = np.meshgrid(np.linspace(0, np.pi, 512), np.linspace(0, 2, 512)) z = (np.sin(X) + np.cos(Y))**2 + 0.5 return z i1 = generate_image() i2 = generate_image() display.image(i1, title='gradient') # display.images([i2, i2, i2, i2], width=200, title='super fabio', labels=['a', 'b', 'c', 'd']) data = [] for i in range(15): data.append([i, random.random(), random.random() * 2]) win = display.plot(data, labels=['position', 'a', 'b'], title='progress') for i in range(15, 25): time.sleep(0.2) data.append([i, random.random(), random.random() * 2]) display.plot(data, win=win) im = cv2.imread('example.png')
+ inputs_names[0][:-4] \ + '_3D' + time_info.strftime("_%H%M") if not os.path.exists('./Trained/' + out_folder_name): os.mkdir('./Trained/' + out_folder_name) # load images input_textures = [Image.open('./Textures/' + name) for name in inputs_names] input_textures_torch = [ Variable(prep(img)).unsqueeze(0).cuda() for img in input_textures ] # display images if disp: for i, img in enumerate(input_textures): img_disp = numpy.asarray(img, dtype="int32") display.image(img_disp, win=['input' + str(i)], title=['Input texture d' + str(i)]) #define layers, loss functions, weights and compute optimization target loss_layers = ['r11', 'r21', 'r31', 'r41', 'r51'] loss_fns = [GramMSELoss()] * len(loss_layers) loss_fns = [loss_fn.cuda() for loss_fn in loss_fns] w = [1, 1, 1, 1, 1] #compute optimization targets targets = [] for img in input_textures_torch: targets.append([GramMatrix()(f).detach() for f in vgg(img, loss_layers)]) # training parameters slice_size = 128 # training slice resolution (best if same as examples)
print(f, " - ", len(eps), " len: ", len(eyes)) result.append(np.asarray(eps)) for ei in range(len(eps)): x, y, w, h = eps[ei] if w > 0 and h > 0: print("eye @ ", eps[ei]) eye_img = image[y:y + h, x:x + w] cv2.imwrite(f"{args.output}/eye_{ei}_{f}", eye_img) # cv2.imwrite(args.output + "/final.png", image) # print(type(image), image.shape) if args.display: destRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) destRGB = cv2.flip(destRGB, 0) display.image(destRGB, win='eye', title='eyes', width=800, height=450) # cv2.imshow('Lets wear Glasses', image) # cv2.waitKey() # cv2.destroyAllWindows() # net = BlurConv(3, 7, 1.5) # print(net) print(len(result)) result = np.asarray(result) print(result.shape) numlist = np.asarray(numlist)