Print('using GPU, but you'd still better make a cup of coffee') Print('SHITTTTTTTTTTTTTT You're running CPU man =D') Param_fn = model_path + model_name #'bvlc_googlenet.caffemodel' If layers is None: layers = 'customloop' # If blend is None: blend = 0.5 #can be nr (constant), random, or loop Source File: 2_dreaming_time.py def main(input, output, image_type, gpu, model_path, model_name, preview, octaves, octave_scale, iterations, jitter, zoom, stepsize, blend, layers, guide_image, start_frame, end_frame, verbose): Newimg = resizePicture(newframe, preview)Р В РІР‚СњР В РІР‚РЋrame = morphPicture(saveframe, newframe, blend, preview) Print 'Processing: ' + inputdir + '/' + vid GetStats(saveframe, var_counter, vids, difference) Saveframe = outputdir + '/' + 'frame_%06d.png' % (var_counter) Halludiff = cv2.remap(halludiff, flow, None, cv2.INTER_LINEAR) Img = np.float32((newframe))Р В РІР‚СњР В РІР‚РЋlow = cv2.calcOpticalFlowFarneback(previousGrayImg, grayImg, pyr_scale=0.5, levels=3, winsize=15, GrayImg = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)Р В РІР‚СњР вЂ™Р’В®ndparam = layers Img = np.float32((inputdir + '/' + vids)) Timeleft = difference * (len(vids) - var_counter) Print 'Frame Time: ' + str(difference) + 's' Print 'Frame ' + str(var_counter) + ' of ' + str(len(vids)) Crop, resize images in JPEGPNGGIF format to the exact pixels or proportion you specified, compress them to reduce the file sizes, making it easy to use them as your desktop wallpaper, Facebook. Octave_scale=octave_scale, jitter=jitter, end=endparam, objective=objective_guide)Р В РІР‚СњР вЂ™Р’Вef getStats(saveframe, var_counter, vids, difference): Octave_scale=octave_scale, jitter=jitter, end=endparam) Return deepdream(net, frame, iter_n=iterations, step_size=stepsize, octave_n=octaves, Src, dst = net.blobs, net.blobsР В РІР‚СњР В РІР‚С› = x.T.dot(y) # compute the matrix of dot-products with guide featuresР В РІР‚СњР вЂ™Р’Вst.diff.reshape(ch, -1) = y # select ones that match best Guideimgresized = guideimg.resize((224, 224), ) Img = resizePicture(inputdir + '/' + vids, preview) #vidinput = natsort.natsorted(os.listdir(inputdir)) Mean=np.float32(), # ImageNet mean, training set dependentР В РІР‚СњР вЂ™Р’В¬hannel_swap=(2, 1, 0)) # the reference model has channels in BGR order instead of RGB Net = caffe.Classifier('tmp.prototxt', param_fn, Open('tmp.prototxt', 'w').write(str(model)) Text_format.Merge(open(net_fn).read(), model) Model = caffe.io.caffe_pb2.NetParameter() # Note that you can also manually add 'force_backward: true' line to 'deploy.prototxt'. # Patching model to be able to compute gradients. Param_fn = model_path + 'bvlc_googlenet.caffemodel' Model_path = '././caffe/models/' + model_name + '/' If octave_scale is None: octave_scale = 1.5 Source File: dreamer.py def main(inputdir, outputdir, preview, octaves, octave_scale, iterations, jitter, zoom, stepsize, blend, layers, guide, Raise BadImage(_('Cannot load image: %s') % exception)

# Width and height will be reversed for these orientations. # Fix width and height to keep aspect ration for non-cropped images. If self._orientation not in ORIENTATION_OPERATIONS: Self._orientation = int(('exif_orientation', 1))

Raise BadImage(_('Cannot open image: %s') % exception) Source File: processor.py def _load(self): validate ( image, , validate = False ) cover = resizeimage. open ( 'test-image.jpeg' ) as image : resizeimage. validate ( image, ) # do something else. open () as image : is_valid = resizeimage. The first exemple is rewritten in the following snippet to use thisР В РІР‚СњР В РІР‚РЋeature: from PIL import Image from resizeimage import resizeimage with open ( 'test-image.jpeg', 'r+b' ) with Image. The viability of the resize without doing it just after validation. Validate function attached to resized function which allows to test You can also create a two step process validation then processing using resize_cover ( image, , validate = False ) To avoid the test add validate=FalseР В РІР‚СњР В РІР‚С›s argument: cover = resizeimage. A resize is considered valid if it doesnР В Р вЂ Р В РІР‚С™Р Р†РІР‚С›РЎС›t require to format )Р В РІР‚СњР вЂ™Р’В«efore resizing, python-image-resize will check whether the operationР В РІР‚СњР вЂ™Р’В¬an be done. open ( f ) as image : cover = resizeimage. In the following example, we open an image, crop it and save as newР В РІР‚СњР В РІР‚РЋile: from PIL import Image from resizeimage import resizeimage with open ( 'test-image.jpeg', 'r+b' ) as f : with Image. Size argument which can be a single integer or tuple of two Python-resize-image takes as first argument a PIL.Image and then

Pil resize image