def map(self, image_id, image_binary): image = resize(imfeat.image_fromstring(image_binary)) print(image.shape) st = time.time() box_num = -1 for box_num, (box, f) in enumerate(feature.image_patch_features_dense(image, normalize_box=True)): yield (image_id, box.tolist()), np.dot(self.coefs, f.reshape((f.size, 1))).ravel() + self.intercepts hadoopy.counter('stats', 'num_boxes', box_num + 1) print('ImageTime[%f]' % (time.time() - st))
def map(self, image_id, image_binary): image = imfeat.image_fromstring(image_binary) print(image.shape) st = time.time() box_num = -1 for box_num, (box, f) in enumerate(feature.image_patch_features_dense(image, normalize_box=True)): scores = np.dot(self.coefs, f.reshape((f.size, 1))) + self.intercepts pred_common = [image_id, box.tolist(), f.tolist()] for score, preds in zip(scores, self.preds): pred = self.output_formatter([float(score[0])] + pred_common) if len(preds) >= self.max_hard: heapq.heappushpop(preds, pred) else: heapq.heappush(preds, pred) hadoopy.counter('stats', 'num_boxes', box_num + 1) print('ImageTime[%f]' % (time.time() - st))
def map(self, image_id, image_binary): image = imfeat.image_fromstring(image_binary) print(image.shape) st = time.time() box_num = -1 for box_num, (box, f) in enumerate(feature.image_patch_features_dense(image, normalize_box=True)): scores = np.dot(self.coefs, f.reshape((f.size, 1))) + self.intercepts pred_common = [image_id, box.tolist(), f.tolist()] for score, preds in zip(scores, self.preds): pred = self.output_formatter([float(score[0])] + pred_common) if len(preds) >= self.max_hard: heapq.heappushpop(preds, pred) else: heapq.heappush(preds, pred) hadoopy.counter("stats", "num_boxes", box_num + 1) print("ImageTime[%f]" % (time.time() - st))
def map(self, image_id, image_binary): image = imfeat.image_fromstring(image_binary) for box, f in feature.image_patch_features_dense(image, normalize_box=True): yield (image_id, box.tolist()), f