Python cv2.resize() Examples
The following are 30 code examples for showing how to use cv2.resize(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: DDPAE-video-prediction Author: jthsieh File: video_transforms.py License: MIT License | 7 votes |
def resize(video, size, interpolation): if interpolation == 'bilinear': inter = cv2.INTER_LINEAR elif interpolation == 'nearest': inter = cv2.INTER_NEAREST else: raise NotImplementedError shape = video.shape[:-3] video = video.reshape((-1, *video.shape[-3:])) resized_video = np.zeros((video.shape[0], size[1], size[0], video.shape[-1])) for i in range(video.shape[0]): img = cv2.resize(video[i], size, inter) if len(img.shape) == 2: img = img[:, :, np.newaxis] resized_video[i] = img return resized_video.reshape((*shape, size[1], size[0], video.shape[-1]))
Example 2
Project: DDPAE-video-prediction Author: jthsieh File: video_transforms.py License: MIT License | 6 votes |
def __call__(self, video): """ Args: video (numpy.ndarray): Video to be scaled. Returns: numpy.ndarray: Rescaled video. """ if isinstance(self.size, int): w, h = video.shape[-2], video.shape[-3] if (w <= h and w == self.size) or (h <= w and h == self.size): return video if w < h: ow = self.size oh = int(self.size*h/w) return resize(video, (ow, oh), self.interpolation) else: oh = self.size ow = int(self.size*w/h) return resize(video, (ow, oh), self.interpolation) else: return resize(video, self.size, self.interpolation)
Example 3
Project: DDPAE-video-prediction Author: jthsieh File: video_transforms.py License: MIT License | 6 votes |
def __call__(self, video): for attempt in range(10): area = video.shape[-3]*video.shape[-2] target_area = random.uniform(0.08, 1.0)*area aspect_ratio = random.uniform(3./4, 4./3) w = int(round(math.sqrt(target_area*aspect_ratio))) h = int(round(math.sqrt(target_area/aspect_ratio))) if random.random() < 0.5: w, h = h, w if w <= video.shape[-2] and h <= video.shape[-3]: x1 = random.randint(0, video.shape[-2]-w) y1 = random.randint(0, video.shape[-3]-h) video = video[..., y1:y1+h, x1:x1+w, :] return resize(video, (self.size, self.size), self.interpolation) # Fallback scale = Scale(self.size, interpolation=self.interpolation) crop = CenterCrop(self.size) return crop(scale(video))
Example 4
Project: BiblioPixelAnimations Author: ManiacalLabs File: ScreenGrab.py License: MIT License | 6 votes |
def step(self, amt=1): image = self._capFrame() if self.crop: image = image[self._cropY + self.yoff:self._ih - self._cropY + self.yoff, self._cropX + self.xoff:self._iw - self._cropX + self.xoff] else: t, b, l, r = self._pad image = cv2.copyMakeBorder( image, t, b, l, r, cv2.BORDER_CONSTANT, value=[0, 0, 0]) resized = cv2.resize(image, (self.width, self.height), interpolation=cv2.INTER_LINEAR) if self.mirror: resized = cv2.flip(resized, 1) for y in range(self.height): for x in range(self.width): self.layout.set(x, y, tuple(resized[y, x][0:3]))
Example 5
Project: Adversarial-Face-Attack Author: ppwwyyxx File: face_attack.py License: GNU General Public License v3.0 | 6 votes |
def detect(self, img): """ img: rgb 3 channel """ minsize = 20 # minimum size of face threshold = [0.6, 0.7, 0.7] # three steps's threshold factor = 0.709 # scale factor bounding_boxes, _ = FaceDet.detect_face( img, minsize, self.pnet, self.rnet, self.onet, threshold, factor) area = (bounding_boxes[:, 2] - bounding_boxes[:, 0]) * (bounding_boxes[:, 3] - bounding_boxes[:, 1]) face_idx = area.argmax() bbox = bounding_boxes[face_idx][:4] # xy,xy margin = 32 x0 = np.maximum(bbox[0] - margin // 2, 0) y0 = np.maximum(bbox[1] - margin // 2, 0) x1 = np.minimum(bbox[2] + margin // 2, img.shape[1]) y1 = np.minimum(bbox[3] + margin // 2, img.shape[0]) x0, y0, x1, y1 = bbox = [int(k + 0.5) for k in [x0, y0, x1, y1]] cropped = img[y0:y1, x0:x1, :] scaled = cv2.resize(cropped, (160, 160), interpolation=cv2.INTER_LINEAR) return scaled, bbox
Example 6
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: ocr_predict.py License: Apache License 2.0 | 6 votes |
def forward_ocr(self, img_): img_ = cv2.resize(img_, (80, 30)) img_ = img_.transpose(1, 0) print(img_.shape) img_ = img_.reshape((1, 80, 30)) print(img_.shape) # img_ = img_.reshape((80 * 30)) img_ = np.multiply(img_, 1 / 255.0) self.predictor.forward(data=img_, **self.init_state_dict) prob = self.predictor.get_output(0) label_list = [] for p in prob: print(np.argsort(p)) max_index = np.argsort(p)[::-1][0] label_list.append(max_index) return self.__get_string(label_list)
Example 7
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: image.py License: Apache License 2.0 | 6 votes |
def resize(im, short, max_size): """ only resize input image to target size and return scale :param im: BGR image input by opencv :param short: one dimensional size (the short side) :param max_size: one dimensional max size (the long side) :return: resized image (NDArray) and scale (float) """ im_shape = im.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) im_scale = float(short) / float(im_size_min) # prevent bigger axis from being more than max_size: if np.round(im_scale * im_size_max) > max_size: im_scale = float(max_size) / float(im_size_max) im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) return im, im_scale
Example 8
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: test_image.py License: Apache License 2.0 | 6 votes |
def test_augmenters(self): # ColorNormalizeAug mean = np.random.rand(3) * 255 std = np.random.rand(3) + 1 width = np.random.randint(100, 500) height = np.random.randint(100, 500) src = np.random.rand(height, width, 3) * 255. # We test numpy and mxnet NDArray inputs color_norm_aug = mx.image.ColorNormalizeAug(mean=mx.nd.array(mean), std=std) out_image = color_norm_aug(mx.nd.array(src)) assert_almost_equal(out_image.asnumpy(), (src - mean) / std, atol=1e-3) # only test if all augmenters will work # TODO(Joshua Zhang): verify the augmenter outputs im_list = [[0, x] for x in TestImage.IMAGES] test_iter = mx.image.ImageIter(2, (3, 224, 224), label_width=1, imglist=im_list, resize=640, rand_crop=True, rand_resize=True, rand_mirror=True, mean=True, std=np.array([1.1, 1.03, 1.05]), brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1, pca_noise=0.1, rand_gray=0.2, inter_method=10, path_root='', shuffle=True) for batch in test_iter: pass
Example 9
Project: DOTA_models Author: ringringyi File: map_utils.py License: Apache License 2.0 | 6 votes |
def resize_maps(map, map_scales, resize_method): scaled_maps = [] for i, sc in enumerate(map_scales): if resize_method == 'antialiasing': # Resize using open cv so that we can compute the size. # Use PIL resize to use anti aliasing feature. map_ = cv2.resize(map*1, None, None, fx=sc, fy=sc, interpolation=cv2.INTER_LINEAR) w = map_.shape[1]; h = map_.shape[0] map_img = PIL.Image.fromarray((map*255).astype(np.uint8)) map__img = map_img.resize((w,h), PIL.Image.ANTIALIAS) map_ = np.asarray(map__img).astype(np.float32) map_ = map_/255. map_ = np.minimum(map_, 1.0) map_ = np.maximum(map_, 0.0) elif resize_method == 'linear_noantialiasing': map_ = cv2.resize(map*1, None, None, fx=sc, fy=sc, interpolation=cv2.INTER_LINEAR) else: logging.error('Unknown resizing method') scaled_maps.append(map_) return scaled_maps
Example 10
Project: pruning_yolov3 Author: zbyuan File: datasets.py License: GNU General Public License v3.0 | 6 votes |
def load_image(self, index): # loads 1 image from dataset img = self.imgs[index] if img is None: img_path = self.img_files[index] img = cv2.imread(img_path) # BGR assert img is not None, 'Image Not Found ' + img_path r = self.img_size / max(img.shape) # size ratio if self.augment and r < 1: # if training (NOT testing), downsize to inference shape h, w, _ = img.shape img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # _LINEAR fastest # Augment colorspace if self.augment: augment_hsv(img, hgain=self.hyp['hsv_h'], sgain=self.hyp['hsv_s'], vgain=self.hyp['hsv_v']) return img
Example 11
Project: cascade-rcnn_Pytorch Author: guoruoqian File: blob.py License: MIT License | 6 votes |
def prep_im_for_blob(im, pixel_means, pixel_stds, target_size, max_size): """Mean subtract and scale an image for use in a blob.""" im = im.astype(np.float32, copy=False) im /= 255.0 im -= pixel_means im /= pixel_stds # im = im[:, :, ::-1] im_shape = im.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE # if np.round(im_scale * im_size_max) > max_size: # im_scale = float(max_size) / float(im_size_max) # im = imresize(im, im_scale) im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) return im, im_scale
Example 12
Project: progressive_growing_of_GANs Author: preritj File: utils.py License: MIT License | 6 votes |
def grid_batch_images(self, images): n, h, w, c = images.shape a = int(math.floor(np.sqrt(n))) # images = (((images - images.min()) * 255) / (images.max() - images.min())).astype(np.uint8) images = images.astype(np.uint8) images_in_square = np.reshape(images[:a * a], (a, a, h, w, c)) new_img = np.zeros((h * a, w * a, c), dtype=np.uint8) for col_i, col_images in enumerate(images_in_square): for row_i, image in enumerate(col_images): new_img[col_i * h: (1 + col_i) * h, row_i * w: (1 + row_i) * w] = image resolution = self.cfg.resolution if self.cfg.resolution != h: scale = resolution / h new_img = cv2.resize(new_img, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST) return new_img
Example 13
Project: kuzushiji-recognition Author: see-- File: data.py License: MIT License | 6 votes |
def __getitem__(self, index, to_tensor=True): fn = self.image_fns[index] img = cv2.cvtColor(cv2.imread(fn, 1), cv2.COLOR_BGR2RGB) img, pad_top, pad_left = KuzushijiDataset.pad_to_ratio(img, ratio=1.5) h, w = img.shape[:2] # print(h / w, pad_left, pad_top) assert img.ndim == 3 scaled_imgs = [] for scale in self.scales: h_scale = int(scale * self.height) w_scale = int(scale * self.width) simg = cv2.resize(img, (w_scale, h_scale)) if to_tensor: assert simg.ndim == 3, simg.ndim simg = simg.transpose((2, 0, 1)) simg = th.from_numpy(simg.copy()) scaled_imgs.append(simg) return scaled_imgs + [fn]
Example 14
Project: OpenCV-Computer-Vision-Projects-with-Python Author: PacktPublishing File: saliency.py License: MIT License | 6 votes |
def __init__(self, img, use_numpy_fft=True, gauss_kernel=(5, 5)): """Constructor This method initializes the saliency algorithm. :param img: an RGB input image :param use_numpy_fft: flag whether to use NumPy's FFT (True) or OpenCV's FFT (False) :param gauss_kernel: Kernel size for Gaussian blur """ self.use_numpy_fft = use_numpy_fft self.gauss_kernel = gauss_kernel self.frame_orig = img # downsample image for processing self.small_shape = (64, 64) self.frame_small = cv2.resize(img, self.small_shape[1::-1]) # whether we need to do the math (True) or it has already # been done (False) self.need_saliency_map = True
Example 15
Project: torch-toolbox Author: PistonY File: functional.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def resized_crop(img, i, j, h, w, size, interpolation='BILINEAR'): """Crop the given CV Image and resize it to desired size. Args: img (CV Image): Image to be cropped. i (int): i in (i,j) i.e coordinates of the upper left corner j (int): j in (i,j) i.e coordinates of the upper left corner h (int): Height of the cropped image. w (int): Width of the cropped image. size (sequence or int): Desired output size. Same semantics as ``resize``. interpolation (int, optional): Desired interpolation. Default is ``BILINEAR``. Returns: CV Image: Cropped image. """ assert _is_numpy_image(img), 'img should be CV Image' img = crop(img, i, j, h, w) img = resize(img, size, interpolation) return img
Example 16
Project: signature-recognition Author: gnbaron File: preprocessor.py License: MIT License | 6 votes |
def prepare(input): # preprocessing the image input clean = cv2.fastNlMeansDenoising(input) ret, tresh = cv2.threshold(clean, 127, 1, cv2.THRESH_BINARY_INV) img = crop(tresh) # 40x10 image as a flatten array flatten_img = cv2.resize(img, (40, 10), interpolation=cv2.INTER_AREA).flatten() # resize to 400x100 resized = cv2.resize(img, (400, 100), interpolation=cv2.INTER_AREA) columns = np.sum(resized, axis=0) # sum of all columns lines = np.sum(resized, axis=1) # sum of all lines h, w = img.shape aspect = w / h return [*flatten_img, *columns, *lines, aspect]
Example 17
Project: HardRLWithYoutube Author: MaxSobolMark File: train_featurizer.py License: MIT License | 6 votes |
def generate_dataset(videos_path, framerate, width, height): """Converts videos from specified path to ndarrays of shape [numberOfVideos, -1, width, height, 1] Args: videos_path: Inside the 'videos/' directory, the name of the subdirectory for videos. framerate: The desired framerate of the dataset. width: The width we will resize the videos to. height: The height we will resize the videos to. Returns: The dataset with the new size and framerate, and converted to monochromatic. """ dataset = [] video_index = 0 for playlist in os.listdir('videos/' + videos_path): for video_name in os.listdir('videos/{}/{}'.format(videos_path, playlist)): dataset.append([]) print('Video: {}'.format(video_name)) video = cv2.VideoCapture('videos/{}/{}/{}'.format(videos_path, playlist, video_name)) while video.isOpened(): success, frame = video.read() if success: frame = preprocess_image(frame, width, height) dataset[video_index].append(frame) frame_index = video.get(cv2.CAP_PROP_POS_FRAMES) video_framerate = video.get(cv2.CAP_PROP_FPS) video.set(cv2.CAP_PROP_POS_FRAMES, frame_index + video_framerate // framerate) last_frame_index = video.get(cv2.CAP_PROP_FRAME_COUNT) if frame_index >= last_frame_index: # Video is over break else: break dataset[video_index] = np.reshape(dataset[video_index], (-1, width, height, 1)) video_index += 1 return dataset
Example 18
Project: ICDAR-2019-SROIE Author: zzzDavid File: demo.py License: MIT License | 6 votes |
def resize_image(img): img_size = img.shape im_size_min = np.min(img_size[0:2]) im_size_max = np.max(img_size[0:2]) im_scale = float(600) / float(im_size_min) if np.round(im_scale * im_size_max) > 1200: im_scale = float(1200) / float(im_size_max) new_h = int(img_size[0] * im_scale) new_w = int(img_size[1] * im_scale) new_h = new_h if new_h // 16 == 0 else (new_h // 16 + 1) * 16 new_w = new_w if new_w // 16 == 0 else (new_w // 16 + 1) * 16 re_im = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR) return re_im, (new_h / img_size[0], new_w / img_size[1])
Example 19
Project: DeepLab_v3 Author: leimao File: utils.py License: MIT License | 6 votes |
def multiscale_single_test(image, input_scales, predictor): ''' Predict image semantic segmentation labeling using multi-scale inputs. Inputs: images: numpy array, [height, width, channel], channel = 3. input_scales: list of scale factors. e.g., [0.5, 1.0, 1.5]. predictor: prediction function which takes one scaled image as input and outputs its semantic segmentation labelings. Returns: Averaged predicted logits of multi-scale inputs ''' image_height_raw = image.shape[0] image_width_raw = image.shape[1] multiscale_outputs = [] for input_scale in input_scales: image_height_scaled = round(image_height_raw * input_scale) image_width_scaled = round(image_width_raw * input_scale) image_scaled = cv2.resize(image, (image_width_scaled, image_height_scaled), interpolation=cv2.INTER_LINEAR) output = predictor(inputs=[image_scaled], target_height=image_height_raw, target_width=image_width_raw)[0] multiscale_outputs.append(output) output_mean = np.mean(multiscale_outputs, axis=0) return output_mean
Example 20
Project: DeepLab_v3 Author: leimao File: utils.py License: MIT License | 6 votes |
def multiscale_single_validate(image, label, input_scales, validator): image_height_raw = image.shape[0] image_width_raw = image.shape[1] multiscale_outputs = [] multiscale_losses = [] for input_scale in input_scales: image_height_scaled = round(image_height_raw * input_scale) image_width_scaled = round(image_width_raw * input_scale) image_scaled = cv2.resize(image, (image_width_scaled, image_height_scaled), interpolation=cv2.INTER_LINEAR) output, loss = validator(inputs=[image_scaled], target_height=image_height_raw, target_width=image_width_raw, labels=[label]) multiscale_outputs.append(output[0]) multiscale_losses.append(loss) output_mean = np.mean(multiscale_outputs, axis=0) loss_mean = np.mean(multiscale_losses) return output_mean, loss_mean
Example 21
Project: pytorch-segmentation-toolbox Author: speedinghzl File: datasets.py License: MIT License | 6 votes |
def __getitem__(self, index): datafiles = self.files[index] image = cv2.imread(datafiles["img"], cv2.IMREAD_COLOR) image = cv2.resize(image, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_LINEAR) size = image.shape name = osp.splitext(osp.basename(datafiles["img"]))[0] image = np.asarray(image, np.float32) image = (image - image.min()) / (image.max() - image.min()) img_h, img_w, _ = image.shape pad_h = max(self.crop_h - img_h, 0) pad_w = max(self.crop_w - img_w, 0) if pad_h > 0 or pad_w > 0: image = cv2.copyMakeBorder(image, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=(0.0, 0.0, 0.0)) image = image.transpose((2, 0, 1)) return image, np.array(size), name
Example 22
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: blob.py License: MIT License | 5 votes |
def prep_im_for_blob(im, pixel_means, target_size, max_size): """Mean subtract and scale an image for use in a blob.""" im = im.astype(np.float32, copy=False) im -= pixel_means im_shape = im.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > max_size: im_scale = float(max_size) / float(im_size_max) im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) return im, im_scale
Example 23
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: test.py License: MIT License | 5 votes |
def _get_image_blob(im): """Converts an image into a network input. Arguments: im (ndarray): a color image in BGR order Returns: blob (ndarray): a data blob holding an image pyramid im_scale_factors (list): list of image scales (relative to im) used in the image pyramid """ im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) processed_ims = [] im_scale_factors = [] for target_size in cfg.TEST.SCALES: im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE: im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max) im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) im_scale_factors.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, np.array(im_scale_factors)
Example 24
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: test_train.py License: MIT License | 5 votes |
def _get_image_blob(im): """Converts an image into a network input. Arguments: im (ndarray): a color image in BGR order Returns: blob (ndarray): a data blob holding an image pyramid im_scale_factors (list): list of image scales (relative to im) used in the image pyramid """ im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) processed_ims = [] im_scale_factors = [] for target_size in cfg.TEST.SCALES: im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE: im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max) im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) im_scale_factors.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, np.array(im_scale_factors)
Example 25
Project: BiblioPixelAnimations Author: ManiacalLabs File: opencv_video.py License: MIT License | 5 votes |
def step(self, amt=1): ret, frame = self._vid.read() image = cv2.cvtColor(frame, cv2.COLOR_RGB2BGRA) if self.crop: image = image[self._cropY + self.yoff:self._ih - self._cropY + self.yoff, self._cropX + self.xoff:self._iw - self._cropX + self.xoff] else: t, b, l, r = self._pad image = cv2.copyMakeBorder( image, t, b, l, r, cv2.BORDER_CONSTANT, value=[0, 0, 0]) resized = cv2.resize(image, (self.width, self.height), interpolation=cv2.INTER_CUBIC) if self.mirror: resized = cv2.flip(resized, 1) for y in range(self.height): for x in range(self.width): self.layout.set(x, y, tuple(resized[y, x][0:3])) if not isinstance(self.videoSource, int): self._frameCount += 1 if self._frameCount >= self._frameTotal: self._vid.set(1, 0) # CV_CAP_PROP_POS_FRAMES self._frameCount = 0 self.animComplete = True
Example 26
Project: Traffic_sign_detection_YOLO Author: AmeyaWagh File: im_transform.py License: MIT License | 5 votes |
def imcv2_affine_trans(im): # Scale and translate h, w, c = im.shape scale = np.random.uniform() / 10. + 1. max_offx = (scale-1.) * w max_offy = (scale-1.) * h offx = int(np.random.uniform() * max_offx) offy = int(np.random.uniform() * max_offy) im = cv2.resize(im, (0,0), fx = scale, fy = scale) im = im[offy : (offy + h), offx : (offx + w)] flip = np.random.binomial(1, .5) if flip: im = cv2.flip(im, 1) return im, [w, h, c], [scale, [offx, offy], flip]
Example 27
Project: Traffic_sign_detection_YOLO Author: AmeyaWagh File: predict.py License: MIT License | 5 votes |
def resize_input(self, im): h, w, c = self.meta['inp_size'] imsz = cv2.resize(im, (w, h)) imsz = imsz / 255. imsz = imsz[:,:,::-1] return imsz
Example 28
Project: The-chat-room Author: 11ze File: vachat.py License: MIT License | 5 votes |
def run(self): while True: try: self.sock.connect(self.ADDR) break except: time.sleep(3) continue if self.showme: cv2.namedWindow('You', cv2.WINDOW_NORMAL) print("VEDIO client connected...") while self.cap.isOpened(): ret, frame = self.cap.read() if self.showme: cv2.imshow('You', frame) if cv2.waitKey(1) & 0xFF == 27: self.showme = False cv2.destroyWindow('You') sframe = cv2.resize(frame, (0, 0), fx=self.fx, fy=self.fx) data = pickle.dumps(sframe) zdata = zlib.compress(data, zlib.Z_BEST_COMPRESSION) try: self.sock.sendall(struct.pack("L", len(zdata)) + zdata) except: break for i in range(self.interval): self.cap.read()
Example 29
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: gradcam.py License: Apache License 2.0 | 5 votes |
def get_cam(imggrad, conv_out): """Compute CAM. Refer section 3 of https://arxiv.org/abs/1610.02391 for details""" weights = np.mean(imggrad, axis=(1, 2)) cam = np.ones(conv_out.shape[1:], dtype=np.float32) for i, w in enumerate(weights): cam += w * conv_out[i, :, :] cam = cv2.resize(cam, (imggrad.shape[1], imggrad.shape[2])) cam = np.maximum(cam, 0) cam = (cam - np.min(cam)) / (np.max(cam) - np.min(cam)) cam = np.uint8(cam * 255) return cam
Example 30
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: gradcam_demo.py License: Apache License 2.0 | 5 votes |
def read_image_cv(path): return cv2.resize(cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB), image_sz) # synset.txt contains the names of Imagenet categories # Load the file to memory and create a helper method to query category_index -> category name