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
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
