我们从Python开源项目中，提取了以下48个代码示例，用于说明如何使用scipy.misc.imsave()。
def validate_model(self, val_iter, epoch, step): labels, codes, images = next(val_iter) fake_imgs, real_imgs, d_loss, g_loss, l1_loss = self.generate_fake_samples(images, labels) print("Sample: d_loss: %.5f, g_loss: %.5f, l1_loss: %.5f" % (d_loss, g_loss, l1_loss)) merged_fake_images = merge(scale_back(fake_imgs), [self.batch_size, 1]) merged_real_images = merge(scale_back(real_imgs), [self.batch_size, 1]) merged_pair = np.concatenate([merged_real_images, merged_fake_images], axis=1) model_id, _ = self.get_model_id_and_dir() model_sample_dir = os.path.join(self.sample_dir, model_id) if not os.path.exists(model_sample_dir): os.makedirs(model_sample_dir) sample_img_path = os.path.join(model_sample_dir, "sample_%02d_%04d.png" % (epoch, step)) misc.imsave(sample_img_path, merged_pair)
def generate(p, ics, gcs, *args): from scipy import misc import subprocess import datetime inits = p.generate(np.array(ics),*args) goals = p.generate(np.array(gcs),*args) for noise_fn,output_dir in zip(noise_fns,output_dirs): inits = noise_fn(inits) goals = noise_fn(goals) for i,init in enumerate(inits): for j,goal in enumerate(goals): d = "{}/{}/{:03d}-{:03d}-{:03d}".format(output_dir,p.__name__,steps,i,j) try: subprocess.call(["mv",d,d+"_old_"+datetime.datetime.today().isoformat()]) except: pass os.makedirs(d) print(d) misc.imsave(os.path.join(d,"init.png"),init) misc.imsave(os.path.join(d,"goal.png"),goal) ################################################################
def resize_images(prms): seqNum = range(11) rawStr = ['rawLeftImFile', 'rawRightImFile'] imStr = ['leftImFile', 'rightImFile'] num = ku.get_num_images() for raw, new in zip(rawStr, imStr): for seq in seqNum: N = num[seq] print seq, N, raw, new rawNames = [prms['paths'][raw] % (seq,i) for i in range(N)] newNames = [prms['paths'][new] % (seq,i) for i in range(N)] dirName = os.path.dirname(newNames[0]) if not os.path.exists(dirName): os.makedirs(dirName) for rawIm, newIm in zip(rawNames, newNames): im = scm.imread(rawIm) im = scm.imresize(im, [256, 256]) scm.imsave(newIm, im) ## # Save images as jpgs.
def save_as_jpg(prms): seqNum = range(11) rawStr = ['rawLeftImFile', 'rawRightImFile'] imStr = ['leftImFile', 'rightImFile'] num = ku.get_num_images() for raw, new in zip(rawStr, imStr): for seq in seqNum: N = num[seq] print seq, N, raw, new rawNames = [prms['paths'][raw] % (seq,i) for i in range(N)] newNames = [prms['paths'][new] % (seq,i) for i in range(N)] dirName = os.path.dirname(newNames[0]) if not os.path.exists(dirName): os.makedirs(dirName) for rawIm, newIm in zip(rawNames, newNames): im = scm.imread(rawIm) scm.imsave(newIm, im) ## # Get the names of images
def preprocess(image_dir, new_image_dir, preprocess_fn): image_paths = [] labels = [] if os.path.isdir(new_image_dir): rmtree(new_image_dir) os.makedirs(new_image_dir) classes = os.listdir(image_dir) for clas in classes: class_dir = os.path.join(image_dir, str(clas)) new_class_dir = os.path.join(new_image_dir, str(clas)) os.makedirs(new_class_dir) for image_name in os.listdir(class_dir): image = misc.imread(os.path.join(class_dir, image_name)) image = preprocess_fn(image) misc.imsave(os.path.join(new_class_dir, image_name), image)
def main(args): gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = args.gpu_fraction) with tf.Session(config=tf.ConfigProto(gpu_options = gpu_options)) as sess: saver = tf.train.import_meta_graph('./meta_graph/my-model.meta') saver.restore(sess,tf.train.latest_checkpoint('./model')) image_batch = tf.get_collection('image_batch')[0] GT_trimap = tf.get_collection('GT_trimap')[0] pred_mattes = tf.get_collection('pred_mattes')[0] rgb = misc.imread(args.rgb) alpha = misc.imread(args.alpha,'L') trimap = generate_trimap(np.expand_dims(np.copy(alpha),2),np.expand_dims(alpha,2))[:,:,0] origin_shape = alpha.shape rgb = np.expand_dims(misc.imresize(rgb.astype(np.uint8),[320,320,3]).astype(np.float32)-g_mean,0) trimap = np.expand_dims(np.expand_dims(misc.imresize(trimap.astype(np.uint8),[320,320],interp = 'nearest').astype(np.float32),2),0) feed_dict = {image_batch:rgb,GT_trimap:trimap} pred_alpha = sess.run(pred_mattes,feed_dict = feed_dict) final_alpha = misc.imresize(np.squeeze(pred_alpha),origin_shape) # misc.imshow(final_alpha) misc.imsave('./alpha.png',final_alpha)
def test_15(self): bpth = tempfile.mkdtemp() os.mkdir(os.path.join(bpth, 'a')) ipth = os.path.join(bpth, 'a', 'b.png') img = np.ones((32,32)) misc.imsave(ipth, img) ei = util.ExampleImages(pth=bpth) im = ei.images() assert(len(im) > 0) gp = ei.groups() assert(len(gp) > 0) img = ei.image('b.png', group='a') assert(img.shape == (32,32)) im = ei.image('b.png', group='a', scaled=True, dtype=np.float32, zoom=0.5) os.remove(ipth) os.rmdir(os.path.join(bpth, 'a')) os.rmdir(bpth)
def save(self, data): """Takes in an array of CYX pixel values and writes them to a png :param data: a CYX or YX array with C being the rgb channels for each pixel value """ # check for rgb, rgba, or r if len(data.shape) == 3: assert data.shape[0] in [4, 3, 2, 1] # if three dimensions, transpose to YXC (imsave() needs it in these axes) data = np.transpose(data, (1, 2, 0)) # if there's only one channel, repeat across the next two channels if data.shape[2] == 1: data = np.repeat(data, repeats=3, axis=2) elif data.shape[2] == 2: data = np.pad(data, ((0, 0), (0, 0), (0, 1)), 'constant') elif len(data.shape) != 2: raise ValueError("Data was not of dimensions CYX or YX") imsave(self.file_path, data, format="png")
def test_recognize(args): imdetect = args.detect im1 = args.im1 im2 = args.im2 payload = {'img':file2base64(imdetect)} import numpy as np imarr = np.array(misc.imread(imdetect)) r = requests.get("http://face.icybee.cn/face/face_detect", data=payload) print(json.loads(r.text)['boxes'][0]) box = json.loads(r.text)['boxes'][0] box = [int(i) for i in box] misc.imsave('sample.jpg',imarr[box[1]:box[3],box[0]:box[2],:],) payload = { 'img1':file2base64(im1), 'img2':file2base64(im2) } r = requests.get("http://face.icybee.cn/face/face_recognize", data=payload) print(r.text) #print(json.loads(r.text)['dist'])
def mirror_pic(output_data_path, _dir, pic, img): fname, fextension = os.path.splitext(pic) mirror_x_img = img[:, ::-1, :] mirror_x_img_gray = rgb2gray(mirror_x_img) mirror_y_img = img[::-1, :, :] mirror_y_img_gray = rgb2gray(mirror_y_img) mirror_xy_img = img[::-1, ::-1, :] mirror_xy_img_gray = rgb2gray(mirror_xy_img) misc.imsave(os.path.join(output_data_path, _dir, (fname + '_mirror_x' + fextension)), mirror_x_img_gray) os.chmod(os.path.join(output_data_path, _dir, (fname + '_mirror_x' + fextension)), stat.S_IWRITE) misc.imsave(os.path.join(output_data_path, _dir, (fname + '_mirror_y' + fextension)), mirror_y_img_gray) os.chmod(os.path.join(output_data_path, _dir, (fname + '_mirror_y' + fextension)), stat.S_IWRITE) misc.imsave(os.path.join(output_data_path, _dir, (fname + '_mirror_xy' + fextension)), mirror_xy_img_gray) os.chmod(os.path.join(output_data_path, _dir, (fname + '_mirror_xy' + fextension)), stat.S_IWRITE) return mirror_x_img, mirror_y_img, mirror_xy_img
def process_mot(path): ''' 1920 x 1080 -> 384 x 216 640 x 480 -> 320 x 240 ''' images = [] for dirpath, dirnames, filenames in os.walk(path): for filename in filenames: if filename[-4:] == ".jpg" and "_ds" not in filename: full_path = os.path.join(dirpath, filename) img = misc.imread(full_path,mode='RGB') if img.shape == LARGE_IMAGE_SIZE: img = misc.imresize(img, size=LARGE_IMAGE_RESCALE) img = pad_image(img, FINAL_IMAGE_SIZE) elif img.shape == MEDIUM_IMAGE_SIZE: img = misc.imresize(img, size=MEDIUM_IMAGE_RESCALE) img = pad_image(img, FINAL_IMAGE_SIZE) else: print("Unexpected shape " + str(img.shape)) continue output_filename = os.path.join(dirpath, filename[:-4] + "_ds.jpg") misc.imsave(output_filename, img) images.append(output_filename) return images
def process_vot(path, min_height, min_width): images = [] for dirpath, dirnames, filenames in os.walk(path): img_shape = None pad_height = 0 pad_width = 0 for filename in filenames: if filename[-4:] == ".jpg" and "_ds" not in filename: full_path = os.path.join(dirpath, filename) img = misc.imread(full_path,mode='RGB') img_shape = img.shape ratio = min(float(min_width)/img.shape[1], float(min_height)/img.shape[0]) img = misc.imresize(img, size=ratio) img, pad_height, pad_width = pad_image(img, (min_height, min_width)) output_filename = os.path.join(dirpath, filename[:-4] + "_ds.jpg") misc.imsave(output_filename, img) images.append(output_filename) if img_shape: gt_path = os.path.join(dirpath, "groundtruth.txt") preprocess_label(gt_path, ratio, img_shape, min_height, min_width, pad_height, pad_width) return images
def main(): #ids=[14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29] ids=range(1,41) rate=2.0/3 for id in ids: gtfn=os.path.join(path,'P%d/V2Rct.nii.gz'%id) # outfn=os.path.join(path,'P%d/V2Rct_all.nii.gz'%id) gtOrg=sitk.ReadImage(gtfn) gtMat=sitk.GetArrayFromImage(gtOrg) print 'mat shape, ', gtMat.shape for s in range(1,gtMat.shape[0]): sliceMat=gtMat[s-1,:,:] sliceMatScale = nd.interpolation.zoom(sliceMat, zoom=rate) scmi.imsave('p%d_'%id+'s%d.png'%s, sliceMat) #gtMat=np.transpose(gtMat,(2,1,0)) # gtVol=sitk.GetImageFromArray(gtMat) # sitk.WriteImage(gtVol,outfn) # # prefn='preSub%d_as32_v12.nii'%id # preOrg=sitk.ReadImage(prefn) # preMat=sitk.GetArrayFromImage(preOrg) # preMat=np.transpose(preMat,(2,1,0)) # preVol=sitk.GetImageFromArra(preMat) # sitk.WriteImage(preVol,prefn)
def resize_to_optimal(infile, scale_ratio, rect, outfile): image_array = imread(infile, mode='RGB') im_shape = image_array.shape h, w, _ = im_shape width = float(rect.right()-rect.left()) scale_amount = (optimal_extent * scale_ratio) / width new_w = int(scale_amount * w) new_h = int(scale_amount * h) new_w = new_w - (new_w % 4) new_h = new_h - (new_h % 4) print("optimal resize of width {} and ratio {} went from {},{} to {},{}".format(width, scale_ratio, w, h, new_w, new_h)) new_shape = (new_h, new_w) image_array_resized = imresize(image_array, new_shape) imsave(outfile, image_array_resized) return new_shape
def visualize(code, filename, filename_r, filename_all): gen.eval() generated_by_riter = [[] for _ in range(1+opt.r_iterations)] for i in xrange((code.size(0) - 1) // opt.batch_size + 1): batch_size = min(opt.batch_size, code.size(0) - i * opt.batch_size) batch_code = Variable(code[i * opt.batch_size : i * opt.batch_size + batch_size]) for r_iter in xrange(1+opt.r_iterations): imgs, _ = gen(batch_code, n_execute_lis_layers=r_iter) if opt.output_scale: imgs = imgs * 2 - 1 imgs_np = (imgs.data.cpu().numpy()*255).astype(np.uint8).transpose((0, 2, 3, 1)) generated_by_riter[r_iter].extend(imgs_np) generated_all = [] for i in xrange(len(generated_by_riter[0])): block = [imgs[i] for imgs in generated_by_riter] generated_all.append(np.hstack(block)) misc.imsave(filename, util.draw_grid(generated_by_riter[0], cols=opt.vis_col)) for r_iter in xrange(1, 1+opt.r_iterations): misc.imsave(filename_r.format(r_iter-1), util.draw_grid(generated_by_riter[r_iter], cols=opt.vis_col)) misc.imsave(filename_all, util.draw_grid(generated_all, cols=opt.vis_col)) gen.train()
def generate(sample_image): start_time = time.time() g = ModelGraph() with tf.Session() as sess: # We need to initialize variables in this case because the Variable `generator/x` will not restored. tf.sg_init(sess) vars = [v for v in tf.global_variables() if "generator" not in v.name] saver = tf.train.Saver(vars) saver.restore(sess, tf.train.latest_checkpoint('asset/train/ckpt')) i = 0 while True: mse, _ = sess.run([g.mse, g.train_gen], {g.y: transform_image(sample_image)}) # (16, 28) if time.time() - start_time > 60: # Save every 60 seconds gen_image = sess.run(g.x) gen_image = np.squeeze(gen_image) misc.imsave('gen_images/%s/gen_%.2f.jpg' % (label, mse), gen_image) start_time = time.time() i += 1 if i == 60: break # Finish after 1 hour
def process_image(im_fp, dset_part): bn = path.basename(im_fp) dn = path.dirname(im_fp) img_idx = int(bn[:bn.find("_")]) body_fp = path.join(dn, bn + '_body.pkl') im = sm.imread(im_fp) if not path.exists(body_fp): raise Exception("Body fit not found for `%s`!" % (im_fp)) rendering = upr.render_body_impl(body_fp, resolution=(im.shape[0], im.shape[1]), quiet=True, use_light=False)[0] annotation = upm.regions_to_classes(rendering, upm.six_region_groups, warn_id=str(img_idx)) out_fp = path.join('..', 'data', 'pose', 'extracted', dset_part, "{:0{width}d}_bodysegments.png".format( img_idx, width=bn.find("_"))) sm.imsave(out_fp, annotation) out_fp = path.join('..', 'data', 'pose', 'extracted', dset_part, "{:0{width}d}_bodysegments_vis.png".format( img_idx, width=bn.find("_"))) sm.imsave(out_fp, vs.apply_colormap(annotation, vmin=0, vmax=6, cmap=config.CMAP)[:, :, 0:3])
def process_frame(frame_idx, img, model, write_to_dir, conf_threshold, input_size=224): """Finds bounding boxes in a video frame, draws these bounding boxes and saves the result to HDD. """ # find BBs in frame bbs, time_model = find_bbs(img, model, conf_threshold, input_size=input_size) # draw BBs img_out = np.copy(img) for (bb, score) in bbs: if score > conf_threshold and bb.width > 2 and bb.height > 2: img_out = bb.draw_on_image(img_out, color=[0, 255, 0], thickness=3) # save to output directory save_to_fp = os.path.join(write_to_dir, "%05d.jpg" % (frame_idx,)) misc.imsave(save_to_fp, img_out) return time_model
def main(): img = imread(args.input_path) img = ndimage.rotate(img, args.angle, mode=args.mode) misc.imsave(args.output_path, img)
def main(): print(args) for dir_path, dir_names, file_names in os.walk(args.input_data_dir): # dir_path is a string, the path to the directory # dir_names is a list of the names of the subdirectories in dir_path (excluding '.' and '..') # file_names is a list of the names of the non-directory files in dir_path dir_absolute_path = args.output_data_dir + dir_path.replace(args.input_data_dir, '') if not os.path.exists(dir_absolute_path): os.mkdir(dir_absolute_path) for file_name in file_names: # Split the pathname path into a pair (root, ext) such that root + ext == path, and ext is empty or begins # with a period and contains at most one period. (root, ext) = os.path.splitext(file_name) new_file_name = '%s/%s.%dx%d%s' % ( dir_absolute_path, root, args.width, args.height, ext) print(new_file_name) if not os.path.exists(new_file_name): img = imread(dir_path + '/' + file_name) # type(img) = ndarray, https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html (width, height) = img.shape[0:2] if width > height: size = (args.width, height * args.width / width) else: size = (width * args.height / height, args.height) new_img = misc.imresize(img, size) misc.imsave(new_file_name, new_img)
def setup(self, pre_encode=False): sbd_path = get_data_path('sbd') voc_path = get_data_path('pascal') target_path = self.root + '/SegmentationClass/pre_encoded/' if not os.path.exists(target_path): os.makedirs(target_path) sbd_train_list = tuple(open(sbd_path + 'dataset/train.txt', 'r')) sbd_train_list = [id_.rstrip() for id_ in sbd_train_list] self.files['train_aug'] = self.files['train'] + sbd_train_list if pre_encode: print("Pre-encoding segmentation masks...") for i in tqdm(sbd_train_list): lbl_path = sbd_path + 'dataset/cls/' + i + '.mat' lbl = io.loadmat(lbl_path)['GTcls'][0]['Segmentation'][0].astype(np.int32) lbl = m.toimage(lbl, high=lbl.max(), low=lbl.min()) m.imsave(target_path + i + '.png', lbl) for i in tqdm(self.files['trainval']): lbl_path = self.root + '/SegmentationClass/' + i + '.png' lbl = self.encode_segmap(m.imread(lbl_path)) lbl = m.toimage(lbl, high=lbl.max(), low=lbl.min()) m.imsave(target_path + i + '.png', lbl)
def pcaCreate(image_files,dir,name_num, dir_list): image_list = [] new_file_name = dir save_dir = dir_list + new_file_name save_dir_tt = save_dir + "\\" for image_file in image_files: image_list.append(misc.imread(image_file)) for image in image_list: img = np.asarray(image, dtype='float32') img = img / 255. img_size = img.size / 3 img1 = img.reshape(img_size, 3) img1 = np.transpose(img1) img_cov = np.cov([img1[0], img1[1], img1[2]]) lamda, p = np.linalg.eig(img_cov) p = np.transpose(p) alpha1 = random.normalvariate(0, 0.3) alpha2 = random.normalvariate(0, 0.3) alpha3 = random.normalvariate(0, 0.3) v = np.transpose((alpha1 * lamda[0], alpha2 * lamda[1], alpha3 * lamda[2])) add_num = np.dot(p, v) img2 = np.array([img[:, :, 0] + add_num[0], img[:, :, 1] + add_num[1], img[:, :, 2] + add_num[2]]) img2 = np.swapaxes(img2, 0, 2) img2 = np.swapaxes(img2, 0, 1) misc.imsave(save_dir_tt + np.str(name_num) + '.jpg', img2) name_num += 1 return image_list
def pcaCreate_Ori(image_files,dir): parser = argparse.ArgumentParser() parser.add_argument("file_suffix", help="specific the file suffix") parser.add_argument("root_dir", help="E:\\") parser.add_argument("-f", "--file", help="record result to file") parser.add_argument("data_set",help= "specific the file suffix") args = parser.parse_args() img_num = len(os.listdir(args.root_dir + '/' + args.dataset)) for i in range(img_num): img_name = os.listdir(args.root_dir + '/' + args.dataset)[i] img = Image.open(os.path.join(args.root_dir, args.dataset, img_name)) img = np.asarray(img, dtype='float32') img = img / 255. img_size = img.size / 3 img1 = img.reshape(img_size, 3) img1 = np.transpose(img1) img_cov = np.cov([img1[0], img1[1], img1[2]]) lamda, p = np.linalg.eig(img_cov) p = np.transpose(p) alpha1 = random.normalvariate(0, 0.3) alpha2 = random.normalvariate(0, 0.3) alpha3 = random.normalvariate(0, 0.3) v = np.transpose((alpha1 * lamda[0], alpha2 * lamda[1], alpha3 * lamda[2])) add_num = np.dot(p, v) img2 = np.array([img[:, :, 0] + add_num[0], img[:, :, 1] + add_num[1], img[:, :, 2] + add_num[2]]) img2 = np.swapaxes(img2, 0, 2) img2 = np.swapaxes(img2, 0, 1) misc.imsave('test2222.jpg', img2)
def saveImage(rgb_array, savepath): """ Converts image array to file :param rgb_array: input rgb array :param savepath: save file location """ misc.imsave(savepath, rgb_array)
def saveAllImages(rgb_array, directory, foldername): """ Saves unfiltered and filtered images to a file directory :param rgb_array: image rgb array :param directory: image directory name :param foldername: name of the folder that will contain saved files :return: """ rgb_array_red = rgb_array * 1 r_array = setImageColor(rgb_array_red, 'r') rgb_array_green = rgb_array * 1 g_array = setImageColor(rgb_array_green, 'g') rgb_array_blue = rgb_array * 1 b_array = setImageColor(rgb_array_blue, 'b') trueimage = 'image.png' redimage = 'red.png' greenimage = 'green.png' blueimage = 'blue.png' plotimage = 'plot.png' if not os.path.exists(os.path.join(directory, foldername)): os.mkdir(os.path.join(directory, foldername)) misc.imsave(os.path.join(directory, foldername, trueimage), rgb_array) misc.imsave(os.path.join(directory, foldername, redimage), r_array) misc.imsave(os.path.join(directory, foldername, greenimage), g_array) misc.imsave(os.path.join(directory, foldername, blueimage), b_array) savePlot(rgb_array, os.path.join(directory, foldername, plotimage))
def main(): ''' ???????? ''' im_array = ndimage.imread("greytech.png", mode='RGB') print(len(im_array), len(im_array[0])) color = set() for i in im_array: for j in i: color.add(tuple(j)) # tmp = [[0 for i in range(len(im_array[0]))] for j in range(len(im_array))] # # for i in range((len(im_array))): # for j in range(len(im_array[0])): # print(str(tuple(im_array[i][j]))) # if str(tuple(im_array[i][j]))!= "(255, 255, 255)": # tmp[i][j]=(0,0,0) # else: # tmp[i][j]=im_array[i][j] # # misc.imsave("test.bmp", tmp) print('{') for i in color: print("\"{0}\":,".format(i)) print('}') # for noi,i in enumerate(im_array): # for noj,j in enumerate(i): # print("Row:%d Col:%d color: %s" %(noi, noj, j))
def main(): ''' ???????? ''' im_array = ndimage.imread("ustc.bmp", mode='RGB') print(len(im_array), len(im_array)) color = set() for i in im_array: for j in i: color.add(tuple(j)) # tmp = [[0 for i in range(len(im_array[0]))] for j in range(len(im_array))] # # for i in range((len(im_array))): # for j in range(len(im_array[0])): # print(str(tuple(im_array[i][j]))) # if str(tuple(im_array[i][j]))!= "(255, 255, 255)": # tmp[i][j]=(0,0,0) # else: # tmp[i][j]=im_array[i][j] # # misc.imsave("test.bmp", tmp) print('{') for i in color: print("\"{0}\":,".format(i)) print('}') # for noi,i in enumerate(im_array): # for noj,j in enumerate(i): # print("Row:%d Col:%d color: %s" %(noi, noj, j))
def main(): ''' ???????? ''' im_array = ndimage.imread("ms.bmp", mode='RGB') print(len(im_array), len(im_array)) color = set() for i in im_array: for j in i: color.add(tuple(j)) # tmp = [[0 for i in range(len(im_array[0]))] for j in range(len(im_array))] # # for i in range((len(im_array))): # for j in range(len(im_array[0])): # print(str(tuple(im_array[i][j]))) # if str(tuple(im_array[i][j]))!= "(255, 255, 255)": # tmp[i][j]=(0,0,0) # else: # tmp[i][j]=im_array[i][j] # # misc.imsave("test.bmp", tmp) print('{') for i in color: print("\"{0}\":,".format(i)) print('}') # for noi,i in enumerate(im_array): # for noj,j in enumerate(i): # print("Row:%d Col:%d color: %s" %(noi, noj, j))
def load_image_array(image_file, image_size, image_id, data_dir='Data/datasets/mscoco/train2014', mode='train'): img = None if os.path.exists(image_file): #print('found' + image_file) img = skimage.io.imread(image_file) else: print('notfound' + image_file) img = skimage.io.imread('http://mscoco.org/images/%d' % (image_id)) img_path = os.path.join(data_dir, 'COCO_%s2014_%.12d.jpg' % ( mode, image_id)) skimage.io.imsave(img_path, img) # GRAYSCALE if len(img.shape) == 2: img_new = np.ndarray( (img.shape[0], img.shape[1], 3), dtype = 'uint8') img_new[:,:,0] = img img_new[:,:,1] = img img_new[:,:,2] = img img = img_new img_resized = skimage.transform.resize(img, (image_size, image_size)) # FLIP HORIZONTAL WIRH A PROBABILITY 0.5 if random.random() > 0.5: img_resized = np.fliplr(img_resized) return img_resized.astype('float32')
def save_concat_images(imgs, img_path): concated = np.concatenate(imgs, axis=1) misc.imsave(img_path, concated)
def swap_attribute(src_img, att_img, model_dir, model, gpu): ''' Input src_img: the source image that you want to change its attribute att_img: the attribute image that has certain attribute model_dir: the directory that contains the checkpoint, ckpt.* files model: the GeneGAN network that defined in train.py gpu: for example, '0,1'. Use '' for cpu mode Output out1: src_img with attributes out2: att_img without attributes ''' os.environ["CUDA_VISIBLE_DEVICES"] = gpu saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) ckpt = tf.train.get_checkpoint_state(model_dir) # print(ckpt) # print(ckpt.model_checkpoint_path) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) out2, out1 = sess.run([model.Ae, model.Bx], feed_dict={model.Ax: att_img, model.Be: src_img}) misc.imsave('out1.jpg', out1[0]) misc.imsave('out2.jpg', out2[0])
def interpolation(src_img, att_img, inter_num, model_dir, model, gpu): ''' Input src_img: the source image that you want to change its attribute att_img: the attribute image that has certain attribute inter_num: number of interpolation points model_dir: the directory that contains the checkpoint, ckpt.* files model: the GeneGAN network that defined in train.py gpu: for example, '0,1'. Use '' for cpu mode Output out: [src_img, inter1, inter2, ..., inter_{inter_num}] ''' os.environ["CUDA_VISIBLE_DEVICES"] = gpu saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) ckpt = tf.train.get_checkpoint_state(model_dir) # print(ckpt) # print(ckpt.model_checkpoint_path) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) out = src_img[0] for i in range(1, inter_num + 1): lambda_i = i / float(inter_num) model.out_i = model.joiner('G_joiner', model.B, model.x * lambda_i) out_i = sess.run(model.out_i, feed_dict={model.Ax: att_img, model.Be: src_img}) out = np.concatenate((out, out_i[0]), axis=1) # print(out.shape) misc.imsave('interpolation.jpg', out)
def interpolation2(src_img, att_img, inter_num, model_dir, model, gpu): ''' Input src_img: the source image that you want to change its attribute att_img: the attribute image that has certain attribute inter_num: number of interpolation points model_dir: the directory that contains the checkpoint, ckpt.* files model: the GeneGAN network that defined in train.py gpu: for example, '0,1'. Use '' for cpu mode Output out: [src_img, inter1, inter2, ..., inter_{inter_num}] ''' os.environ["CUDA_VISIBLE_DEVICES"] = gpu saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) ckpt = tf.train.get_checkpoint_state(model_dir) # print(ckpt) # print(ckpt.model_checkpoint_path) if ckpt and ckpt.model_checkpoint_path: saver.restore(sess, ckpt.model_checkpoint_path) B, src_feat = sess.run([model.B, model.e], feed_dict={model.Be: src_img}) att_feat = sess.run(model.x, feed_dict={model.Ax: att_img}) out = src_img[0] for i in range(1, inter_num + 1): lambda_i = i / float(inter_num) out_i = sess.run(model.joiner('G_joiner', B, src_feat + (att_feat - src_feat) * lambda_i) ) out = np.concatenate((out, out_i[0]), axis=1) # print(out.shape) misc.imsave('interpolation2.jpg', out)
def AddNoize(i): R = random.randint(0, 1) if (R==1): i=np.fliplr(i)#random mirroring R = random.randint(0, 1) if (R==1): R = random.randint(-10, 10) i= ndimage.interpolation.rotate(i,R)#random rotation R = random.randint(0, 1) if (R==1): crop_left=random.randint(0,15) crop_right = random.randint(1, 15) crop_top = random.randint(0, 15) crop_bot = random.randint(1, 15) i=i[crop_left:-crop_right,crop_top:-crop_bot,:] #Randomcrop #Next code is VERY SLOW becoase it use Python to change brightness #need to optimase it, but i have no time yet:) R = random.randint(0, 2) if (R==2): #Random brightness in R channel d = random.random()+1 i[:, :, 0] = adjust_gamma(i[:,:,0],d) R = random.randint(0, 2) if (R==2): #Random brightness in G channel d = random.random()+1 i[:, :, 1] = adjust_gamma(i[:, :, 1], d) R = random.randint(0, 2) if (R==2): #Random brightness in B channel d = random.random()+1 i[:, :, 2] = adjust_gamma(i[:, :, 2], d) #misc.imsave("test.jpg",i) return i #Prepare data for learning
def save_image(image, image_size, save_dir, name=""): """ Save image by unprocessing assuming mean 127.5 :param image: :param save_dir: :param name: :return: """ image += 1 image *= 127.5 image = np.clip(image, 0, 255).astype(np.uint8) image = np.reshape(image, (image_size, image_size, -1)) misc.imsave(os.path.join(save_dir, name + "pred_image.png"), image)
def resize_raw_images(prms): rawNames,_,_ = get_imnames(prms, isRaw=True) tgNames,_,_ = get_imnames(prms) for rn,tn in zip(rawNames, tgNames): im = scm.imread(rn) im = scm.imresize(im, [320, 480]) dName = os.path.dirname(tn) if not os.path.exists(dName): os.makedirs(dName) scm.imsave(tn, im) ## # Write pairs file
def vis_pairs(prms, isSave=False, svIdx=None, svPath=None): imName1, imName2, euls, trans = read_pairs(prms) N = len(imName1) seed = 3 randState = np.random.RandomState(seed) perm = randState.permutation(N) fig = plt.figure() plt.ion() imName1 = [imName1[i] for i in perm] imName2 = [imName2[i] for i in perm] euls = [euls[i] for i in perm] trans = [trans[i] for i in perm] titleStr = 'Trans: ' + '%.3f ' * 3 + 'Rot: ' + '%.3f ' * 3 count = 0 numSave = 0 for (im1,im2,eu,tr) in zip(imName1, imName2, euls, trans): titleName = titleStr % (tuple(tr) + eu) im1 = scm.imread(im1) im2 = scm.imread(im2) print count if isSave: if count in svIdx: imN1 = svPath % (count,1) imN2 = svPath % (count,2) scm.imsave(imN1,im1) scm.imsave(imN2,im2) numSave += 1 if numSave==len(svIdx): return else: vu.plot_pairs(im1, im2, fig, titleStr=titleName) cmd = raw_input() if cmd == 'exit': return count += 1 ##
def reshape_images(cls, source_folder, target_folder, height=128, width=128, extensions=('.jpg', '.jpeg', '.png')): """ copy images and reshape them""" # check source_folder and target_folder: cls.check_folder_existance(source_folder, throw_error_if_no_folder=True) cls.check_folder_existance(target_folder, display_msg=False) if source_folder[-1] == "/": source_folder = source_folder[:-1] if target_folder[-1] == "/": target_folder = target_folder[:-1] # read images and reshape: print("Resizing '", source_folder, "' images...") for filename in os.listdir(source_folder): if os.path.isdir(source_folder + '/' + filename): cls.reshape_images(source_folder + '/' + filename, target_folder + '/' + filename, height, width, extensions=extensions) else: if extensions == '' and os.path.splitext(filename)[1] == '': copy2(source_folder + "/" + filename, target_folder + "/" + filename) image = ndimage.imread(target_folder + "/" + filename, mode="RGB") image_resized = misc.imresize(image, (height, width)) misc.imsave(target_folder + "/" + filename, image_resized) else: for extension in extensions: if filename.endswith(extension): copy2(source_folder + "/" + filename, target_folder + "/" + filename) image = ndimage.imread(target_folder + "/" + filename, mode="RGB") image_resized = misc.imresize(image, (height, width)) misc.imsave(target_folder + "/" + filename, image_resized)
def crop_images(cls, source_folder, target_folder, height=128, width=128, extensions=('.jpg', '.jpeg', '.png')): """ copy images and center crop them""" # check source_folder and target_folder: cls.check_folder_existance(source_folder, throw_error_if_no_folder=True) cls.check_folder_existance(target_folder, display_msg=False) if source_folder[-1] == "/": source_folder = source_folder[:-1] if target_folder[-1] == "/": target_folder = target_folder[:-1] # read images and crop: print("Cropping '", source_folder, "' images...") for filename in os.listdir(source_folder): if os.path.isdir(source_folder + '/' + filename): cls.crop_images(source_folder + '/' + filename, target_folder + '/' + filename, height, width, extensions=extensions) else: if extensions == '' and os.path.splitext(filename)[1] == '': copy2(source_folder + "/" + filename, target_folder + "/" + filename) image = ndimage.imread(target_folder + "/" + filename, mode="RGB") [width_original, height_original, _] = image.shape offset_w = (width_original - width) / 2 offset_h = (width_original - width) / 2 image_cropped = image[offset_w : width + offset_w, offset_h : height + offset_h, :] misc.imsave(target_folder + "/" + filename, image_cropped) else: for extension in extensions: if filename.endswith(extension): copy2(source_folder + "/" + filename, target_folder + "/" + filename) image = ndimage.imread(target_folder + "/" + filename, mode="RGB") [width_original, height_original, _] = image.shape offset_w = (width_original - width) / 2 offset_h = (width_original - width) / 2 image_cropped = image[offset_w : width + offset_w, offset_h : height + offset_h, :] misc.imsave(target_folder + "/" + filename, image_cropped)
def convert_to_grayscale(cls, source_folder, target_folder, extensions=('.jpg', '.jpeg', '.png')): """ convert images from RGB to Grayscale""" # check source_folder and target_folder: cls.check_folder_existance(source_folder, throw_error_if_no_folder=True) cls.check_folder_existance(target_folder, display_msg=False) if source_folder[-1] == "/": source_folder = source_folder[:-1] if target_folder[-1] == "/": target_folder = target_folder[:-1] # read images and reshape: print("Convert '", source_folder, "' images to grayscale...") for filename in os.listdir(source_folder): if os.path.isdir(source_folder + '/' + filename): cls.convert_to_grayscale(source_folder + '/' + filename, target_folder + '/' + filename, extensions=extensions) else: if extensions == '' and os.path.splitext(filename)[1] == '': copy2(source_folder + "/" + filename, target_folder + "/" + filename) image = ndimage.imread(target_folder + "/" + filename, flatten=True) misc.imsave(target_folder + "/" + filename, image) else: for extension in extensions: if filename.endswith(extension): copy2(source_folder + "/" + filename, target_folder + "/" + filename) image = ndimage.imread(target_folder + "/" + filename, flatten=True) misc.imsave(target_folder + "/" + filename, image)
def convert_format(cls, source_folder, target_folder, extensions=('.jpg', '.jpeg', '.png'), new_extension='.jpg'): """ change images from one format to another (eg. change png files to jpeg) """ # check source_folder and target_folder: cls.check_folder_existance(source_folder, throw_error_if_no_folder=True) cls.check_folder_existance(target_folder, display_msg=False) if source_folder[-1] == "/": source_folder = source_folder[:-1] if target_folder[-1] == "/": target_folder = target_folder[:-1] # read images and reshape: print("Change format of '", source_folder, "' files...") for filename in os.listdir(source_folder): if os.path.isdir(source_folder + '/' + filename): cls.convert_format(source_folder + '/' + filename, target_folder + '/' + filename, extensions=extensions, new_extension=new_extension) else: if extensions == '' and os.path.splitext(filename)[1] == '': copy2(source_folder + "/" + filename, target_folder + "/" + filename + new_extension) image = ndimage.imread(target_folder + "/" + filename + new_extension) misc.imsave(target_folder + "/" + filename + new_extension, image) else: for extension in extensions: if filename.endswith(extension): new_filename = os.path.splitext(filename)[0] + new_extension copy2(source_folder + "/" + filename, target_folder + "/" + new_filename) image = ndimage.imread(target_folder + "/" + new_filename) misc.imsave(target_folder + "/" + new_filename, image)
def save_image(image, save_dir, name, mean=None): """ Save image by unprocessing if mean given else just save :param mean: :param image: :param save_dir: :param name: :return: """ if mean: image = unprocess_image(image, mean) misc.imsave(os.path.join(save_dir, name + ".png"), image)
def save_alpha_img(org, mat, name): w, h, _ = mat.shape #print(mat[200:210, 200:210]) rmat = np.reshape(mat, (w, h)) amat = np.zeros((w, h, 4), dtype=np.int) amat[:, :, 3] = rmat * 1000 amat[:, :, 0:3] = org print(amat[200:205, 200:205]) #im = Image.fromarray(np.uint8(amat)) #im.save(name + '.png') misc.imsave(name + '.png', amat)
def save_image(image, save_dir, name, mean=None): """ Save image by unprocessing if mean given else just save :param mean: :param image: :param save_dir: :param name: :return: """ if mean: image = unprocess_image(image, mean) misc.imsave(os.path.join(save_dir, name + ".png"), image) # as describe at Sec.4.2
def save_alpha_img(org, mat, name): w, h = mat.shape[0], mat.shape[1] #print(mat[200:210, 200:210]) rmat = np.reshape(mat, (w, h)) amat = np.zeros((w, h, 4), dtype=np.int) amat[:, :, 3] = np.round(rmat * 1000) amat[:, :, 0:3] = org #print(amat[200:205, 200:205]) #im = Image.fromarray(np.uint8(amat)) #im.save(name + '.png') misc.imsave(name + '.png', amat)
def _write_deepdream(images, layer, path_outdir, path_logdir): is_success = True images = _im_normlize([images]) layer, units, k = layer # write into disk path_out = os.path.join(path_outdir, "deepdream_" + layer.lower().replace("/", "_")) is_success = make_dir(path_out) for i in range(len(images)): for j in range(images[i].shape[0]): img_save = images[i][j] if img_save.shape[2] == 1: img_save = np.squeeze(img_save, axis=2) imsave(os.path.join(path_out, "image_%d.png" % (units[(i * images[i].shape[0]) + j + k])), img_save, format = "png") # write into logfile path_log = os.path.join(path_logdir, layer.lower().replace("/", "_")) is_success = make_dir(path_log) with tf.Graph().as_default() as g: image = tf.placeholder(tf.float32, shape = [None, None, None, None]) image_summary_t = tf.summary.image(name = "One_By_One_DeepDream", tensor = image, max_outputs = config["MAX_FEATUREMAP"]) with tf.Session() as sess: summary = sess.run(image_summary_t, feed_dict = {image : np.concatenate(images, axis = 0)}) try: file_writer = tf.summary.FileWriter(path_log, g) # create file writer # compute and write the summary file_writer.add_summary(summary) except: is_success = False print("Error occured in writting results into log file.") finally: file_writer.close() # close file writer return is_success
