Python cv2.resize() Examples
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Example #1
Source File: video_transforms.py From DDPAE-video-prediction with 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
Source File: preprocessor.py From signature-recognition with MIT License | 7 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 #3
Source File: video_transforms.py From DDPAE-video-prediction with 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 #4
Source File: video_transforms.py From DDPAE-video-prediction with 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 #5
Source File: ScreenGrab.py From BiblioPixelAnimations with 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 #6
Source File: face_attack.py From Adversarial-Face-Attack with 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 #7
Source File: ocr_predict.py From dynamic-training-with-apache-mxnet-on-aws with 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 #8
Source File: image.py From dynamic-training-with-apache-mxnet-on-aws with 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 #9
Source File: test_image.py From dynamic-training-with-apache-mxnet-on-aws with 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 #10
Source File: map_utils.py From DOTA_models with 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 #11
Source File: datasets.py From pruning_yolov3 with 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 #12
Source File: blob.py From cascade-rcnn_Pytorch with 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 #13
Source File: utils.py From progressive_growing_of_GANs with 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 #14
Source File: data.py From kuzushiji-recognition with 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 #15
Source File: saliency.py From OpenCV-Computer-Vision-Projects-with-Python with 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 #16
Source File: functional.py From torch-toolbox with 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 #17
Source File: img_utils.py From tools_python with Apache License 2.0 | 6 votes |
def one_pic_to_video(image_path, output_video_path, fps, time): """ 一张图片合成视频 one_pic_to_video('./../source/1.jpeg', './../source/output.mp4', 25, 10) :param path: 图片文件路径 :param output_video_path:合成视频的路径 :param fps:帧率 :param time:时长 :return: """ image_clip = ImageClip(image_path) img_width, img_height = image_clip.w, image_clip.h # 总共的帧数 frame_num = (int)(fps * time) img_size = (int(img_width), int(img_height)) fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v') video = cv2.VideoWriter(output_video_path, fourcc, fps, img_size) for index in range(frame_num): frame = cv2.imread(image_path) # 直接缩放到指定大小 frame_suitable = cv2.resize(frame, (img_size[0], img_size[1]), interpolation=cv2.INTER_CUBIC) # 把图片写进视频 # 重复写入多少次 video.write(frame_suitable) # 释放资源 video.release() return VideoFileClip(output_video_path)
Example #18
Source File: train_featurizer.py From HardRLWithYoutube with 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 #19
Source File: demo.py From ICDAR-2019-SROIE with 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 #20
Source File: utils.py From DeepLab_v3 with 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 #21
Source File: utils.py From DeepLab_v3 with 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 #22
Source File: datasets.py From pytorch-segmentation-toolbox with 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 #23
Source File: blob.py From Collaborative-Learning-for-Weakly-Supervised-Object-Detection with 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 #24
Source File: test.py From Collaborative-Learning-for-Weakly-Supervised-Object-Detection with 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
Source File: test_train.py From Collaborative-Learning-for-Weakly-Supervised-Object-Detection with 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 #26
Source File: opencv_video.py From BiblioPixelAnimations with 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 #27
Source File: im_transform.py From Traffic_sign_detection_YOLO with 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 #28
Source File: predict.py From Traffic_sign_detection_YOLO with 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 #29
Source File: vachat.py From The-chat-room with 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 #30
Source File: gradcam.py From dynamic-training-with-apache-mxnet-on-aws with 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