def deepdream( img_path, zoom=True, scale_coefficient=0.05, irange=100, iter_n=10, octave_n=4, octave_scale=1.4, end="inception_4c/output", clip=True, network="bvlc_googlenet", loc="", gif=False, reverse=False, duration=0.1, loop=False, gpu=False, gpuid=0): img = np.float32(img_open(img_path)) s = scale_coefficient h, w = img.shape[:2] if gpu: print("Enabling GPU {}...".format(gpuid)) set_device(gpuid) set_mode_gpu() # Select, load DNN model NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(network, loc) net = Classifier( NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP) img_pool = [img_path] # Save settings used in a log file logging.info(( "{} zoom={}, scale_coefficient={}, irange={}, iter_n={}, " "octave_n={}, octave_scale={}, end={}, clip={}, network={}, gif={}, " "reverse={}, duration={}, loop={}").format( img_path, zoom, scale_coefficient, irange, iter_n, octave_n, octave_scale, end, clip, network, gif, reverse, duration, loop)) print("Dreaming...") for i in xrange(irange): img = _deepdream( net, img, iter_n=iter_n, octave_n=octave_n, octave_scale=octave_scale, end=end, clip=clip) img_fromarray(np.uint8(img)).save("{}_{}.jpg".format( img_path, i)) if gif: img_pool.append("{}_{}.jpg".format(img_path, i)) print("Dream {} saved.".format(i)) if zoom: img = affine_transform( img, [1-s, 1-s, 1], [h*s/2, w*s/2, 0], order=1) if gif: print("Creating gif...") frames = None if reverse: frames = [img_open(f) for f in img_pool[::-1]] else: frames = [img_open(f) for f in img_pool] writeGif( "{}.gif".format(img_path), frames, duration=duration, repeat=loop) print("gif created.")
def deepdream_video( video, iter_n=10, octave_n=4, octave_scale=1.4, end="inception_4c/output", clip=True, network="bvlc_googlenet", frame_rate=24): # Select, load DNN model NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(network) net = Classifier( NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP) print("Extracting video...") _extract_video(video) output_dir = _output_video_dir(video) images = listdir(output_dir) print("Dreaming...") for image in images: image = "{}/{}".format(output_dir, image) img = np.float32(img_open(image)) img = _deepdream( net, img, iter_n=iter_n, octave_n=octave_n, octave_scale=octave_scale, end=end, clip=clip) img_fromarray(np.uint8(img)).save(image) print("Creating dream video...") _create_video(video, frame_rate) print("Dream video created.")
def get_png_photo( png_factor: int = 9 ) -> typing.Tuple[typing.Optional[Image], typing.List[str]]: """Get image from web camera. apt-get install fswebcam """ img_path = f"/tmp/{uuid.uuid4().hex}.png" result = subprocess.run([ "/usr/bin/fswebcam", "-r", RESOLUTION, "--no-banner", "--device", f"/dev/{DEVICE}", "--png", f"{png_factor}", img_path, ], capture_output=True, text=True) lines = result.stdout.split("\n") if os.path.exists(img_path): image = img_open(img_path) os.remove(img_path) else: image = None return image, lines
def compare_scenes(self, img_data=None): if img_data is None: img_data = self.screen io_img = BytesIO(img_data) img = img_open(io_img) for scene in self._scenes.values(): scene.compare(img) io_img.close() most_acc_scene = self.most_acc_scene self.callback_scenes_update(most_acc_scene) return most_acc_scene
def avatar(user, **kwargs): #user=a.user.get() avatar = user.user_avatar.earliest() return avatar.avatar.url #with img_open(avatar.avatar.path) as fin: # return HttpResponse(content=fin.read(), content_type='image/png') response = HttpResponse(content_type='image/jpeg') im = img_open(avatar.avatar.path) im.save(response, 'JPEG') #print(dir(response)) return b64encode(response.content) return response.content '''
def deepdream( img_path, zoom=True, scale_coefficient=0.05, irange=100, iter_n=10, octave_n=4, octave_scale=1.4, end="inception_4c/output", clip=True): img = np.float32(img_open(img_path)) s = scale_coefficient h, w = img.shape[:2] # Load DNN model net = Classifier( NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP) print("Dreaming...") for i in xrange(irange): img = _deepdream( net, img, iter_n=iter_n, octave_n=octave_n, octave_scale=octave_scale, end=end, clip=clip) img_fromarray(np.uint8(img)).save("{}_{}.jpg".format( img_path, i)) print("Dream {} saved.".format(i)) if zoom: img = affine_transform( img, [1-s, 1-s, 1], [h*s/2, w*s/2, 0], order=1)
def load_from_maker(self, path_dir: str): project = Project.open(path_dir) scenes = {} for name, scene in project.scenes.items(): scene: MakerSceneModel io_img = open(scene.img_path, 'rb') img = img_open(io_img) # type: Image new_scene = M.SceneModel(self._event) new_scene.name = scene.name for feature in scene.features: new_feature = M.FeatureModel() new_feature.load_data(**feature.data) new_feature.img.load_image(img, *feature.rect) new_scene.features.append(new_feature) for object_ in scene.objects: new_object = M.ObjectModel() new_object.load_data(**object_.data) new_object.img.load_image(img, *object_.rect) new_scene.objects.append(new_object) io_img.close() scenes[name] = new_scene self._scenes = scenes
def load_image(self): self.img = img_open(self.file_name).convert(self._mode_)
def deepdream( q, dreamname,toepath, img_path, modegpu=True, gpudevice = 0, zoom=True, scale_coefficient=0.05, irange=5, iter_n=10, octave_n=4, octave_scale=1.4, end="inception_4c/output", clip=True, network="bvlc_googlenet", gif=False, reverse=False, duration=0.1, loop=False): #logging.debug('Starting') if modegpu: caffe.set_mode_gpu() caffe.set_device(gpudevice) print("GPU mode [device id: {}]".format(gpudevice)) print("using GPU, but you'd still better make a cup of coffee") else: caffe.set_mode_cpu() print("using CPU...") img = np.float32(img_open(toepath+"actualframe.jpg")) s = scale_coefficient h, w = img.shape[:2] # Select, load DNN model NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(network, toepath) net = Classifier( NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP) img_pool = [img_path,] # Save settings used in a log file ''' logging.info("{} zoom={}, scale_coefficient={}, irange={}, iter_n={}, octave_n={}, octave_scale={}, end={},"\ "clip={}, network={}, gif={}, reverse={}, duration={}, loop={}".format( img_path, zoom, scale_coefficient, irange, iter_n, octave_n, octave_scale, end, clip, network, gif, reverse, duration, loop)) ''' print("Dreaming...") for i in range(irange): img = _deepdream( net, img, iter_n=iter_n, octave_n=octave_n, octave_scale=octave_scale, end=end, clip=clip) img_fromarray(np.uint8(img)).save("{}_{}.jpg".format( img_path+dreamname, i)) if gif: img_pool.append("{}_{}.jpg".format(img_path+dreamname, i)) print("Dream layer depth {} saved.".format(i)) print("{}_{}.jpg".format(img_path+dreamname, i)) q.put([i, "{}_{}.jpg".format(img_path+dreamname, i)]) if zoom: img = affine_transform( img, [1-s, 1-s, 1], [h*s/2, w*s/2, 0], order=1) if gif: frames = None if reverse: frames = [img_open(f) for f in img_pool[::-1]] else: frames = [img_open(f) for f in img_pool] writeGif( "{}.gif".format(img_path), frames, duration=duration, repeat=loop) print("gif created.") print("Weak up") print("¡¡Awake!!")
def get_image_last_area() -> Image: """Read lasr image. """ return img_open(PATH_ACTUAL_IMG)
def deepdream(q, dreamname, toepath, img_path, modegpu=True, gpudevice=0, zoom=True, scale_coefficient=0.05, irange=5, iter_n=10, octave_n=4, octave_scale=1.4, end="inception_4c/output", clip=True, network="bvlc_googlenet", gif=False, reverse=False, duration=0.1, loop=False): #logging.debug('Starting') if modegpu: caffe.set_mode_gpu() caffe.set_device(gpudevice) print("GPU mode [device id: {}]".format(gpudevice)) print("using GPU, but you'd still better make a cup of coffee") else: caffe.set_mode_cpu() print("using CPU...") img = np.float32(img_open(toepath + "actualframe.jpg")) s = scale_coefficient h, w = img.shape[:2] # Select, load DNN model NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network( network, toepath) net = Classifier(NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP) img_pool = [ img_path, ] # Save settings used in a log file ''' logging.info("{} zoom={}, scale_coefficient={}, irange={}, iter_n={}, octave_n={}, octave_scale={}, end={},"\ "clip={}, network={}, gif={}, reverse={}, duration={}, loop={}".format( img_path, zoom, scale_coefficient, irange, iter_n, octave_n, octave_scale, end, clip, network, gif, reverse, duration, loop)) ''' print("Dreaming...") for i in range(irange): img = _deepdream(net, img, iter_n=iter_n, octave_n=octave_n, octave_scale=octave_scale, end=end, clip=clip) img_fromarray(np.uint8(img)).save("{}_{}.jpg".format( img_path + dreamname, i)) if gif: img_pool.append("{}_{}.jpg".format(img_path + dreamname, i)) print("Dream layer depth {} saved.".format(i)) print("{}_{}.jpg".format(img_path + dreamname, i)) q.put([i, "{}_{}.jpg".format(img_path + dreamname, i)]) if zoom: img = affine_transform(img, [1 - s, 1 - s, 1], [h * s / 2, w * s / 2, 0], order=1) if gif: frames = None if reverse: frames = [img_open(f) for f in img_pool[::-1]] else: frames = [img_open(f) for f in img_pool] writeGif("{}.gif".format(img_path), frames, duration=duration, repeat=loop) print("gif created.") print("Weak up") print("¡¡Awake!!")