Python cv2.imread() Examples
The following are 30 code examples for showing how to use cv2.imread(). 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: pedestrian-haar-based-detector Author: felipecorrea File: histcomparison.py License: GNU General Public License v2.0 | 9 votes |
def main(): imagePath = "img.jpg" img = cv2.imread(imagePath) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) generate_histogram(gray) cv2.imwrite("before.jpg", gray) gray = cv2.equalizeHist(gray) generate_histogram(gray) cv2.imwrite("after.jpg",gray) return 0
Example 2
Project: pedestrian-haar-based-detector Author: felipecorrea File: detect.py License: GNU General Public License v2.0 | 8 votes |
def main(): #IMG PATHS imagePath = "test3.jpg" cascPath = "cascades/haarcascade_pedestrian.xml" pplCascade = cv2.CascadeClassifier(cascPath) image = cv2.imread(imagePath) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = normalize_grayimage(gray) pedestrians = pplCascade.detectMultiScale( gray, scaleFactor=1.2, minNeighbors=10, minSize=(32,96), flags = cv2.cv.CV_HAAR_SCALE_IMAGE ) print "Found {0} ppl!".format(len(pedestrians)) #Draw a rectangle around the detected objects for (x, y, w, h) in pedestrians: cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2) cv2.imwrite("saida.jpg", image) cv2.imshow("Ppl found", image) cv2.waitKey(0) return 0
Example 3
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: vaegan_mxnet.py License: Apache License 2.0 | 7 votes |
def get_data(path, activation): '''Get the dataset ''' data = [] image_names = [] for filename in os.listdir(path): img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE) image_names.append(filename) if img is not None: data.append(img) data = np.asarray(data) if activation == 'sigmoid': data = data.astype(np.float32)/(255.0) elif activation == 'tanh': data = data.astype(np.float32)/(255.0/2) - 1.0 data = data.reshape((data.shape[0], 1, data.shape[1], data.shape[2])) np.random.seed(1234) p = np.random.permutation(data.shape[0]) X = data[p] return X, image_names
Example 4
Project: pruning_yolov3 Author: zbyuan File: datasets.py License: GNU General Public License v3.0 | 7 votes |
def convert_images2bmp(): # cv2.imread() jpg at 230 img/s, *.bmp at 400 img/s for path in ['../coco/images/val2014/', '../coco/images/train2014/']: folder = os.sep + Path(path).name output = path.replace(folder, folder + 'bmp') if os.path.exists(output): shutil.rmtree(output) # delete output folder os.makedirs(output) # make new output folder for f in tqdm(glob.glob('%s*.jpg' % path)): save_name = f.replace('.jpg', '.bmp').replace(folder, folder + 'bmp') cv2.imwrite(save_name, cv2.imread(f)) for label_path in ['../coco/trainvalno5k.txt', '../coco/5k.txt']: with open(label_path, 'r') as file: lines = file.read() lines = lines.replace('2014/', '2014bmp/').replace('.jpg', '.bmp').replace( '/Users/glennjocher/PycharmProjects/', '../') with open(label_path.replace('5k', '5k_bmp'), 'w') as file: file.write(lines)
Example 5
Project: Face-skin-hair-segmentaiton-and-skin-color-evaluation Author: JACKYLUO1991 File: data_loader.py License: Apache License 2.0 | 7 votes |
def __getitem__(self, idx): images, masks = [], [] for (image_path, mask_path) in zip(self.image_path_list[idx * self.batch_size: (idx + 1) * self.batch_size], self.mask_path_list[idx * self.batch_size: (idx + 1) * self.batch_size]): image = cv2.imread(image_path, 1) mask = cv2.imread(mask_path, 0) image = self._padding(image) mask = self._padding(mask) # augumentation augmentation = self.transformer(image=image, mask=mask) image = augmentation['image'] mask = self._get_result_map(augmentation['mask']) images.append(image) masks.append(mask) images = np.array(images) masks = np.array(masks) images = pinput(images) return images, masks
Example 6
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: minibatch.py License: MIT License | 6 votes |
def _get_image_blob(roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] for i in range(num_images): im = cv2.imread(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales
Example 7
Project: Traffic_sign_detection_YOLO Author: AmeyaWagh File: objectDetectorYOLO.py License: MIT License | 6 votes |
def processFrames(self): try: for img in self.anotations_list: img = img.split(';') # print(img) # ret,imgcv = cap.read() if self.video: ret,imgcv = self.cap.read() else: imgcv = cv2.imread(os.path.join('../',self.config["dataset"],img[0])) result = self.tfnet.return_predict(imgcv) print(result) imgcv = self.drawBoundingBox(imgcv,result) cv2.imshow('detected objects',imgcv) if cv2.waitKey(10) == ord('q'): print('exitting loop') break except KeyboardInterrupt: cv2.destroyAllWindows() print('exitting program')
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_resize_short(self): try: import cv2 except ImportError: return for img in TestImage.IMAGES: cv_img = cv2.imread(img) mx_img = mx.nd.array(cv_img[:, :, (2, 1, 0)]) h, w, _ = cv_img.shape for _ in range(3): new_size = np.random.randint(1, 1000) if h > w: new_h, new_w = new_size * h // w, new_size else: new_h, new_w = new_size, new_size * w // h for interp in range(0, 2): # area-based/lanczos don't match with cv2? cv_resized = cv2.resize(cv_img, (new_w, new_h), interpolation=interp) mx_resized = mx.image.resize_short(mx_img, new_size, interp) assert_almost_equal(mx_resized.asnumpy()[:, :, (2, 1, 0)], cv_resized, atol=3)
Example 9
Project: DOTA_models Author: ringringyi File: utils.py License: Apache License 2.0 | 6 votes |
def reWriteImgWithMask(srcpath, dstpath, gtpath, srcform, dstform): namelist = GetFileFromThisRootDir(gtpath) for fullname in namelist: objects = parse_bod_poly(fullname) mask_polys = [] for obj in objects: clsname = obj['name'] matches = re.findall('area|mask', clsname) if 'mask' in matches: #print('mask:') mask_polys.append(shgeo.Polygon(obj['poly'])) elif 'area' in matches: #print('area:') mask_polys.append(shgeo.Polygon(obj['poly'])) basename = mybasename(fullname) imgname = os.path.join(srcpath, basename + srcform) img = cv2.imread(imgname) dstname = os.path.join(dstpath, basename + dstform) if len(mask_polys) > 0: saveimageWithMask(img, dstname, mask_polys)
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: pruning_yolov3 Author: zbyuan File: utils.py License: GNU General Public License v3.0 | 6 votes |
def crop_images_random(path='../images/', scale=0.50): # from utils.utils import *; crop_images_random() # crops images into random squares up to scale fraction # WARNING: overwrites images! for file in tqdm(sorted(glob.glob('%s/*.*' % path))): img = cv2.imread(file) # BGR if img is not None: h, w = img.shape[:2] # create random mask a = 30 # minimum size (pixels) mask_h = random.randint(a, int(max(a, h * scale))) # mask height mask_w = mask_h # mask width # box xmin = max(0, random.randint(0, w) - mask_w // 2) ymin = max(0, random.randint(0, h) - mask_h // 2) xmax = min(w, xmin + mask_w) ymax = min(h, ymin + mask_h) # apply random color mask cv2.imwrite(file, img[ymin:ymax, xmin:xmax])
Example 12
Project: ICDAR-2019-SROIE Author: zzzDavid File: boxing.py License: MIT License | 6 votes |
def draw(): f = open(box_path + 'jpglist.txt') # read each image and its label line = f.readline() line_num =0 while line: line_num=line_num+1 print('Image:', line_num) name = line.strip('\n') img = cv2.imread(image_path + name) img_size = img.shape img_size = img_size[0]*img_size[1] # read each coordinate and draw box f_txt = open(image_path + name.strip('.jpg') + '.txt') #line_txt = f_txt.readline() # pass the first ROI information line_txt = f_txt.readline() while line_txt: coor = line_txt.split(',') x1 = int(coor[0].strip('\'')) y1 = int(coor[1].strip('\'')) x3 = int(coor[4].strip('\'')) y3 = int(coor[5].strip('\'')) text = coor[8].strip('\n').strip('\'') text_show = text + '(' + str(x1) + ',' + str(y1) +')' cv2.rectangle(img, (x1, y1), (x3, y3), (255, 0, 0), 1) #cv2.putText(img, text_show, (x1, y1 - 1), # cv2.FONT_HERSHEY_TRIPLEX, 0.35, (0, 0, 255), 1) line_txt = f_txt.readline() cv2.imwrite(box_path + name, img) line = f.readline() # img = cv2.imshow('image', img) # cv2.waitKey(0)
Example 13
Project: ICDAR-2019-SROIE Author: zzzDavid File: data_provider.py License: MIT License | 6 votes |
def generator(vis=False): image_list = np.array(get_training_data()) print('{} training images in {}'.format(image_list.shape[0], DATA_FOLDER)) index = np.arange(0, image_list.shape[0]) while True: np.random.shuffle(index) for i in index: try: im_fn = image_list[i] im = cv2.imread(im_fn) h, w, c = im.shape im_info = np.array([h, w, c]).reshape([1, 3]) _, fn = os.path.split(im_fn) fn, _ = os.path.splitext(fn) txt_fn = os.path.join(DATA_FOLDER, "label", fn + '.txt') if not os.path.exists(txt_fn): print("Ground truth for image {} not exist!".format(im_fn)) continue bbox = load_annoataion(txt_fn) if len(bbox) == 0: print("Ground truth for image {} empty!".format(im_fn)) continue if vis: for p in bbox: cv2.rectangle(im, (p[0], p[1]), (p[2], p[3]), color=(0, 0, 255), thickness=1) fig, axs = plt.subplots(1, 1, figsize=(30, 30)) axs.imshow(im[:, :, ::-1]) axs.set_xticks([]) axs.set_yticks([]) plt.tight_layout() plt.show() plt.close() yield [im], bbox, im_info except Exception as e: print(e) continue
Example 14
Project: ICDAR-2019-SROIE Author: zzzDavid File: main.py License: MIT License | 6 votes |
def draw(): filenames = [os.path.splitext(f)[0] for f in glob.glob("for_task3/*.txt")] txt_files = [s + ".txt" for s in filenames] for txt in txt_files: image = cv2.imread('test_original/'+ txt.split('/')[1].split('.')[0]+'.jpg', cv2.IMREAD_COLOR) with open(txt, 'r') as txt_file: for line in csv.reader(txt_file): box = [int(string, 10) for string in line[0:8]] if len(line) < 9: print(txt) cv2.rectangle(image, (box[0], box[1]), (box[4], box[5]), (0,255,0), 2) font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(image, line[8].upper(), (box[0],box[1]), font, 0.5, (0, 0, 255), 1, cv2.LINE_AA) cv2.imwrite('task2_result_draw/'+ txt.split('/')[1].split('.')[0]+'.jpg', image)
Example 15
Project: DeepLab_v3 Author: leimao File: utils.py License: MIT License | 6 votes |
def image_channel_means(image_filenames): ''' Calculate the means of RGB channels in image dataset. Support extremely large images of different sizes and arbitrarily large number of images. image_filenames: list of image filenames ''' num_pixels = 0 channel_sums = np.zeros(3, dtype=object) for image_filename in tqdm(image_filenames): image = cv2.imread(image_filename) channel_sums += np.sum(image, axis=(0, 1)) num_pixels += np.prod(image.shape[:2]) channel_means = (channel_sums / num_pixels).astype(float) return channel_means
Example 16
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) size = image.shape name = osp.splitext(osp.basename(datafiles["img"]))[0] image = np.asarray(image, np.float32) image -= self.mean 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, name, size
Example 17
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) size = image.shape name = osp.splitext(osp.basename(datafiles["img"]))[0] image = np.asarray(image, np.float32) image -= self.mean 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, name, size
Example 18
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 19
Project: CSD-SSD Author: soo89 File: voc0712.py License: MIT License | 6 votes |
def pull_item(self, index): img_id = self.ids[index] target = ET.parse(self._annopath % img_id).getroot() img = cv2.imread(self._imgpath % img_id) height, width, channels = img.shape if self.target_transform is not None: target = self.target_transform(target, width, height) if self.transform is not None: target = np.array(target) img, boxes, labels = self.transform(img, target[:, :4], target[:, 4]) # to rgb img = img[:, :, (2, 1, 0)] # img = img.transpose(2, 0, 1) target = np.hstack((boxes, np.expand_dims(labels, axis=1))) return torch.from_numpy(img).permute(2, 0, 1), target, height, width # return torch.from_numpy(img), target, height, width
Example 20
Project: CSD-SSD Author: soo89 File: voc07_consistency_init.py License: MIT License | 6 votes |
def pull_item(self, index): img_id = self.ids[index] target = ET.parse(self._annopath % img_id).getroot() img = cv2.imread(self._imgpath % img_id) height, width, channels = img.shape if self.target_transform is not None: target = self.target_transform(target, width, height) if self.transform is not None: target = np.array(target) img, boxes, labels = self.transform(img, target[:, :4], target[:, 4]) # to rgb img = img[:, :, (2, 1, 0)] # img = img.transpose(2, 0, 1) target = np.hstack((boxes, np.expand_dims(labels, axis=1))) if(img_id[0][(len(img_id[0]) - 7):]=='VOC2007'): semi = np.array([1]) else: semi = np.array([0]) target = np.zeros([1, 5]) return torch.from_numpy(img).permute(2, 0, 1), target, height, width, semi # return torch.from_numpy(img), target, height, width
Example 21
Project: CartoonGAN-Tensorflow Author: taki0112 File: edge_smooth.py License: MIT License | 6 votes |
def make_edge_smooth(dataset_name, img_size) : check_folder('./dataset/{}/{}'.format(dataset_name, 'trainB_smooth')) file_list = glob('./dataset/{}/{}/*.*'.format(dataset_name, 'trainB')) save_dir = './dataset/{}/trainB_smooth'.format(dataset_name) kernel_size = 5 kernel = np.ones((kernel_size, kernel_size), np.uint8) gauss = cv2.getGaussianKernel(kernel_size, 0) gauss = gauss * gauss.transpose(1, 0) for f in tqdm(file_list) : file_name = os.path.basename(f) bgr_img = cv2.imread(f) gray_img = cv2.imread(f, 0) bgr_img = cv2.resize(bgr_img, (img_size, img_size)) pad_img = np.pad(bgr_img, ((2, 2), (2, 2), (0, 0)), mode='reflect') gray_img = cv2.resize(gray_img, (img_size, img_size)) edges = cv2.Canny(gray_img, 100, 200) dilation = cv2.dilate(edges, kernel) gauss_img = np.copy(bgr_img) idx = np.where(dilation != 0) for i in range(np.sum(dilation != 0)): gauss_img[idx[0][i], idx[1][i], 0] = np.sum( np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 0], gauss)) gauss_img[idx[0][i], idx[1][i], 1] = np.sum( np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 1], gauss)) gauss_img[idx[0][i], idx[1][i], 2] = np.sum( np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 2], gauss)) cv2.imwrite(os.path.join(save_dir, file_name), gauss_img)
Example 22
Project: iAI Author: aimuch File: call_engine_to_infer_all_print_predict_on_image_6classes.py License: MIT License | 6 votes |
def load_image(img_path, net_input_shape): imgBGR = cv2.imread(img_path) img = cv2.resize(imgBGR, net_input_shape) # BGR -> RGB #img = img[:,:, (2, 1, 0)] ## Method 1 # imgT = np.transpose(img, (2, 0, 1)) # c,w,h # imgF = np.asarray(imgT, dtype=np.float32) # mean = [[[88.159309]], [[97.966286]], [[103.66106]]] # Caffe image mean # imgS = np.subtract(imgF,mean) ## Method 2 imgF = np.asarray(img, dtype=np.float32) mean = [128.0, 128.0, 128.0] # Caffe image mean # mean = [88.159309, 97.966286, 103.66106] # Caffe image mean imgSS = np.subtract(imgF, mean)/128.0 imgS = np.transpose(imgSS, (2, 0, 1)) # c,w,h # RGB_MEAN_PIXELS = np.array([88.159309, 97.966286, 103.66106]).reshape((1,1,1,3)).astype(np.float32) return imgBGR, np.ascontiguousarray(imgS, dtype=np.float32) # avoid error: ndarray is not contiguous
Example 23
Project: iAI Author: aimuch File: call_engine_to_infer_all_analysis_error_6classes.py License: MIT License | 6 votes |
def load_image(img_path, net_input_shape): imgBGR = cv2.imread(img_path) img = cv2.resize(imgBGR, net_input_shape) # BGR -> RGB #img = img[:,:, (2, 1, 0)] ## Method 1 # imgT = np.transpose(img, (2, 0, 1)) # c,w,h # imgF = np.asarray(imgT, dtype=np.float32) # mean = [[[88.159309]], [[97.966286]], [[103.66106]]] # Caffe image mean # imgS = np.subtract(imgF,mean) ## Method 2 imgF = np.asarray(img, dtype=np.float32) mean = [128.0, 128.0, 128.0] # Caffe image mean # mean = [88.159309, 97.966286, 103.66106] # Caffe image mean imgSS = np.subtract(imgF, mean)/128.0 imgS = np.transpose(imgSS, (2, 0, 1)) # c,w,h # RGB_MEAN_PIXELS = np.array([88.159309, 97.966286, 103.66106]).reshape((1,1,1,3)).astype(np.float32) return imgBGR, np.ascontiguousarray(imgS, dtype=np.float32) # avoid error: ndarray is not contiguous
Example 24
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: demo.py License: MIT License | 5 votes |
def demo(net, image_name): """Detect object classes in an image using pre-computed object proposals.""" # Load the demo image im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) im = cv2.imread(im_file) # Detect all object classes and regress object bounds timer = Timer() timer.tic() scores, boxes = im_detect(net, im) timer.toc() print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time(), boxes.shape[0])) # Visualize detections for each class CONF_THRESH = 0.8 NMS_THRESH = 0.3 for cls_ind, cls in enumerate(CLASSES[1:]): cls_ind += 1 # because we skipped background cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] cls_scores = scores[:, cls_ind] dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32) keep = nms(torch.from_numpy(dets), NMS_THRESH) dets = dets[keep.numpy(), :] vis_detections(im, cls, dets, thresh=CONF_THRESH)
Example 25
Project: Traffic_sign_detection_YOLO Author: AmeyaWagh File: predict.py License: MIT License | 5 votes |
def preprocess(self, im, allobj = None): """ Takes an image, return it as a numpy tensor that is readily to be fed into tfnet. If there is an accompanied annotation (allobj), meaning this preprocessing is serving the train process, then this image will be transformed with random noise to augment training data, using scale, translation, flipping and recolor. The accompanied parsed annotation (allobj) will also be modified accordingly. """ if type(im) is not np.ndarray: im = cv2.imread(im) if allobj is not None: # in training mode result = imcv2_affine_trans(im) im, dims, trans_param = result scale, offs, flip = trans_param for obj in allobj: _fix(obj, dims, scale, offs) if not flip: continue obj_1_ = obj[1] obj[1] = dims[0] - obj[3] obj[3] = dims[0] - obj_1_ im = imcv2_recolor(im) im = self.resize_input(im) if allobj is None: return im return im#, np.array(im) # for unit testing
Example 26
Project: Traffic_sign_detection_YOLO Author: AmeyaWagh File: test_darkflow.py License: MIT License | 5 votes |
def test_RETURNPREDICT_PBLOAD_YOLOv2(): #Test the .pb and .meta files generated in the previous step #NOTE: This test verifies that the code executes properly, and the .pb and .meta files that were created are able to be loaded and used for inference. # The predictions that are generated will be compared against expected predictions. options = {"pbLoad": pbPath, "metaLoad": metaPath, "threshold": 0.4} tfnet = TFNet(options) imgcv = cv2.imread(testImg["path"]) loadedPredictions = tfnet.return_predict(imgcv) assert compareObjectData(testImg["expected-objects"]["yolo"], loadedPredictions, testImg["width"], testImg["height"], threshCompareThreshold, posCompareThreshold), "Generated object predictions from return_predict() were not within margin of error compared to expected values." #TESTS FOR TRAINING
Example 27
Project: Adversarial-Face-Attack Author: ppwwyyxx File: face_attack.py License: GNU General Public License v3.0 | 5 votes |
def compute_victim(self, lfw_160_path, name): imgfolder = os.path.join(lfw_160_path, name) assert os.path.isdir(imgfolder), imgfolder images = glob.glob(os.path.join(imgfolder, '*.png')) + glob.glob(os.path.join(imgfolder, '*.jpg')) image_batch = [cv2.imread(f, cv2.IMREAD_COLOR)[:, :, ::-1] for f in images] for img in image_batch: assert img.shape[0] == 160 and img.shape[1] == 160, \ "--data should only contain 160x160 images. Please read the README carefully." embeddings = self.eval_embeddings(image_batch) self.victim_embeddings = embeddings return embeddings
Example 28
Project: Adversarial-Face-Attack Author: ppwwyyxx File: face_attack.py License: GNU General Public License v3.0 | 5 votes |
def validate_on_lfw(model, lfw_160_path): # Read the file containing the pairs used for testing pairs = lfw.read_pairs('validation-LFW-pairs.txt') # Get the paths for the corresponding images paths, actual_issame = lfw.get_paths(lfw_160_path, pairs) num_pairs = len(actual_issame) all_embeddings = np.zeros((num_pairs * 2, 512), dtype='float32') for k in tqdm.trange(num_pairs): img1 = cv2.imread(paths[k * 2], cv2.IMREAD_COLOR)[:, :, ::-1] img2 = cv2.imread(paths[k * 2 + 1], cv2.IMREAD_COLOR)[:, :, ::-1] batch = np.stack([img1, img2], axis=0) embeddings = model.eval_embeddings(batch) all_embeddings[k * 2: k * 2 + 2, :] = embeddings tpr, fpr, accuracy, val, val_std, far = lfw.evaluate( all_embeddings, actual_issame, distance_metric=1, subtract_mean=True) print('Accuracy: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy))) print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far)) auc = metrics.auc(fpr, tpr) print('Area Under Curve (AUC): %1.3f' % auc) eer = brentq(lambda x: 1. - x - interpolate.interp1d(fpr, tpr)(x), 0., 1.) print('Equal Error Rate (EER): %1.3f' % eer)
Example 29
Project: neural-pipeline Author: toodef File: img_segmentation.py License: MIT License | 5 votes |
def __getitem__(self, item): img = cv2.imread(self.__image_pathes[item]['data']) return self.__aug({'data': img, 'target': cv2.imread(self.__image_pathes[item]['target'], cv2.IMREAD_UNCHANGED)})
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