本文整理汇总了Python中object_detection.utils.visualization_utils.visualize_boxes_and_labels_on_image_array方法的典型用法代码示例。如果您正苦于以下问题:Python visualization_utils.visualize_boxes_and_labels_on_image_array方法的具体用法?Python visualization_utils.visualize_boxes_and_labels_on_image_array怎么用?Python visualization_utils.visualize_boxes_and_labels_on_image_array使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.utils.visualization_utils
的用法示例。
在下文中一共展示了visualization_utils.visualize_boxes_and_labels_on_image_array方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: visualize
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def visualize(self, image, output_dict):
"""
Draws the bounding boxes, labels and scores of each detection
Args:
image: (numpy array) input image
output_dict (dictionary) output of object detection model
Returns:
image: (numpy array) image with drawings
"""
# Draw the bounding boxes
vis_util.visualize_boxes_and_labels_on_image_array(
image,
output_dict["detection_boxes"],
output_dict["detection_classes"],
output_dict["detection_scores"],
self.category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=5)
return image
示例2: test_visualize_boxes_and_labels_on_image_array
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def test_visualize_boxes_and_labels_on_image_array(self):
ori_image = np.ones([360, 480, 3], dtype=np.int32) * 255
test_image = np.ones([360, 480, 3], dtype=np.int32) * 255
detections = np.array([[0.8, 0.1, 0.9, 0.1, 1., 0.1],
[0.1, 0.3, 0.8, 0.7, 1., 0.6]])
keypoints = np.array(np.random.rand(2, 5, 2), dtype=np.float32)
labelmap = {1: {'id': 1, 'name': 'cat'}, 2: {'id': 2, 'name': 'dog'}}
visualization_utils.visualize_boxes_and_labels_on_image_array(
test_image,
detections[:, :4],
detections[:, 4].astype(np.int32),
detections[:, 5],
labelmap,
keypoints=keypoints,
track_ids=None,
use_normalized_coordinates=True,
max_boxes_to_draw=1,
min_score_thresh=0.2,
agnostic_mode=False,
line_thickness=8)
self.assertGreater(np.abs(np.sum(test_image - ori_image)), 0)
示例3: visualize_inference_for_single_image_from_path
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def visualize_inference_for_single_image_from_path(self, image_path,
min_score_thresh=.3,
line_thickness=4,
output_image_size=(12, 8),
image_size=300):
image_np = load_image_into_numpy_array_from_path(image_path, image_size)
# Actual detection.
output_dict = self._run_inference_for_single_image(image_np)
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
self.category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
min_score_thresh=min_score_thresh,
line_thickness=line_thickness)
plt.figure(figsize=output_image_size)
plt.imshow(image_np)
plt.show()
示例4: callback
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def callback(self, image_msg):
cv_image = self._cv_bridge.imgmsg_to_cv2(image_msg, "bgr8")
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
image_np = cv_image
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
try:
self._pub.publish(self._cv_bridge.cv2_to_imgmsg(image_np, "bgr8"))
except CvBridgeError as e:
print(e)
示例5: detect_objects
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def detect_objects(image_np, sess, detection_graph):
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
return image_np
示例6: detect_objects
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def detect_objects(image_np, sess, detection_graph):
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=4)
return image_np
示例7: detect_objects
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def detect_objects(image_np, sess, detection_graph):
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
return image_np
示例8: detection
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def detection():
image_label =[]
for image_path in TEST_IMAGE_PATHS:
image_org = Image.open(image_path, 'r')
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = load_image_into_numpy_array(image_org)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
# image_np_expanded = np.expand_dims(image_np, axis=0)
image_name = os.path.basename(os.path.join(image_path))
# Actual detection.
output_dict = run_inference_for_single_image(image_np, detection_graph)
output_path = os.path.join(PATH_TO_TEST_IMAGES_DIR)
# Visualization of the results of a detection.
image, box_to_color_map, box_to_display_str_map = vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.75,
line_thickness=2)
# Crop bounding box to splt images.
lang = 'cont41'
img_label = img_ocr(image_name, output_path, image_org, box_to_color_map, box_to_display_str_map, lang)
# save visualize_boxes_and_labels_on_image_array output image.
image_name = os.path.basename(os.path.join(image_path))
output_image_name = image_name[:-4] + '_out' + image_name[-4:]
image_out = Image.fromarray(image_np)
image_out.save(os.path.join(PATH_TO_TEST_IMAGES_DIR) + '/'+ output_image_name)
image_label.append({str(image_name[:-4]): img_label})
return image_label
示例9: detect_object
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def detect_object(detection_graph, sess, image, category_index):
with detection_graph.as_default():
with sess.as_default() as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
# image_np = load_image_into_numpy_array(image)
image_np = image
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
test_var = tf.placeholder(dtype=tf.int8, shape=[])
# Actual detection.
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh = 0.7)
return image_np
开发者ID:scotthuang1989,项目名称:object_detection_with_tensorflow,代码行数:37,代码来源:object_detection_tf_multiprocessing.py
示例10: detect_object
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def detect_object(detection_graph, sess, image, image_list, category_index):
with detection_graph.as_default():
with sess.as_default() as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# build feed_dict
feed_dict = {}
for i in range(image_per_run):
feed_dict.update({"image_ph%d:0" % i: image_list[i]})
# Actual detection.
feed_image = sess.run(image,
feed_dict=feed_dict)
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: feed_image})
# Visualization of the results of a detection.
for i in range(feed_image.shape[0]):
vis_util.visualize_boxes_and_labels_on_image_array(
feed_image[i],
np.squeeze(boxes[i]),
np.squeeze(classes[i]).astype(np.int32),
np.squeeze(scores[i]),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.20)
return feed_image
开发者ID:scotthuang1989,项目名称:object_detection_with_tensorflow,代码行数:40,代码来源:object_detection_tf_vectorization_thread.py
示例11: detect_image
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def detect_image(image_path):
# load label map
category_index = label_map_util.create_category_index_from_labelmap(
PATH_TO_LABELS)
# load detection graph
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# define input/output tensors
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# load input image
img = cv2.imread(image_path)
if img is None:
sys.exit('failed to load image: %s' % image_path)
img = img[..., ::-1] # BGR to RGB
# run inference
with detection_graph.as_default():
with tf.Session() as sess:
boxes, scores, classes, _ = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: np.expand_dims(img, 0)})
# draw the results of the detection
vis_util.visualize_boxes_and_labels_on_image_array(
img,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=6,
min_score_thresh=0.3)
# save the output image
img = img[..., ::-1] # RGB to BGR
cv2.imwrite(OUTPUT_PATH, img)
print('Output has been written to %s\n' % OUTPUT_PATH)
示例12: vis_detection_result
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def vis_detection_result(graph,image_path,output_image_path):
with graph.as_default():
ops=tf.get_default_graph().get_operations()
all_tensor_names={output.name for op in ops for output in op.outputs}
tensor_dict={}
for key in [
'num_detections','detection_boxes','detection_scores',
'detection_classes','detection_masks'
]:
tensor_name=key+':0'
if tensor_name in all_tensor_names:
tensor_dict[key]=tf.get_default_graph().get_tensor_by_name(tensor_name)
image_tensor=tf.get_default_graph().get_tensor_by_name('image_tensor:0')
with tf.Session() as sess:
print('get in the session')
image = util.data_preprocessing(image_path,target_size=640)
image_np = np.expand_dims(image, axis=0)
output_dict=sess.run(tensor_dict,feed_dict={image_tensor:image_np})
# print(output_dict)
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict[
'detection_classes'][0].astype(np.int64)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
#print(output_dict)
# return output_dict
print('output_dict[\'detection_boxes\'] shape is {}'.format(output_dict['detection_boxes'].shape))
print('output_dict[\'detection_scores\'] shape is {}'.format(output_dict['detection_scores'].shape))
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
image=vis_util.visualize_boxes_and_labels_on_image_array(
image,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=3,min_score_thresh=0.3)
plt.imsave(output_image_path,image)
sess.close()
示例13: detect
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def detect(model, category_index, image_np, i, confidence, min_detections=10, min_confidence=0.7):
"""Detection loop main method
Runs actual detection
Args:
model (model): Model to use
category_index (category_index): category_index
image (byte): Numpy image array
i (int): Iterator
confidence (float): Previous confidence
min_detections (int, optional): Minimum detections required to yield a positive result. Defaults to 10.
min_confidence (float, optional): Minimum average confidence required to yield a positive result. Defaults to 0.7.
Returns:
(bool, int, float, np_aray): Tuple with detection threshold, iterator, confidence, image with labels
"""
# Actual detection.
output_dict = run_inference_for_single_image(model, image_np)
# Visualization of the results of a detection.
np_det_img = vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks_reframed', None),
use_normalized_coordinates=True,
line_thickness=8)
cv2.imshow('object_detection', cv2.resize(image_np, (800, 600)))
# print the most likely
if 'detection_scores' not in output_dict or len(category_index) < 1 or len(output_dict['detection_scores']) <= 0:
return (False, i, confidence, np_det_img)
max_label = category_index[1]
max_score = output_dict['detection_scores'][0] # ['name']
if max_label['name'] == 'person':
i += 1
confidence += max_score
avg_confidence = confidence/i
logger.debug('Count: {}, avg_confidence: {}'.format(i, avg_confidence))
if i >= min_detections and avg_confidence >= min_confidence:
logger.debug('HUMAN DETECTED! DEPLOY BORK BORK NOM NOM! {} {}'.format(
i, avg_confidence))
i = 0
confidence = 0
avg_confidence = 0
return (True, i, confidence, np_det_img)
else:
return (False, i, confidence, np_det_img)
示例14: img_ocr
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def img_ocr(image_name, output_path, image_org, box_to_color_map, box_to_display_str_map, lang = 'cont41'):
cont_num_find = 0
img_label = []
# Convert coordinates to raw pixels.
for box, color in box_to_color_map.items():
ymin, xmin, ymax, xmax = box
# loads the original image, visualize_boxes_and_labels_on_image_array returned image had draw bounding boxs on it.
image_corp_org = Image.fromarray(np.uint8(image_org))
img_width, img_height = image_corp_org.size
new_xmin = int(xmin * img_width)
new_xmax = int(xmax * img_width)
new_ymin = int(ymin * img_height)
new_ymax = int(ymax * img_height)
# Increase cropping security boundary(px).
offset = 5
if new_xmin - offset >= 0:
new_xmin = new_xmin - offset
if new_xmax + offset <= img_width:
new_xmax = new_xmax + offset
if new_ymin - offset >= 0:
new_ymin = new_ymin - offset
if new_ymax + offset <= img_height:
new_ymax = new_ymax + offset
# Get the label name of every bounding box,and rename 'xxx: 90%' to 'xxx-90%'.
img_label_name = box_to_display_str_map[box][0].split(': ')
# Corp image. Note that the PLI and Numpy coordinates are reversed!!!
image_corp_org = load_image_into_numpy_array(image_org)[new_ymin:new_ymax,new_xmin:new_xmax]
image_corp_org = Image.fromarray(np.uint8(image_corp_org))
# Tesseract OCR
lang_use = 'eng+'+lang+'+letsgodigital+snum+eng_f'
if re.match('container_number+', img_label_name[0]):
cont_num_find += 1
image_corp_gray = image_preprocessing(image_corp_org)
if re.match('container_number_v+', img_label_name[0]):
cont_num = pytesseract.image_to_string(image_corp_gray, lang=lang_use, config='--psm 6')
elif re.match('container_number_e+', img_label_name[0]):
cont_num = pytesseract.image_to_string(image_corp_gray, lang=lang_use, config='--psm 6')
else :
cont_num = pytesseract.image_to_string(image_corp_gray, lang=lang_use, config='--psm 4')
# Save corp image to outo_path ,and join lable in name.
# image_corp_name make up like this :'image_name(input)'_'cont_num_find'_'img_label_name'
image_corp_name = image_name[:-4]+ '_'+ str(cont_num_find)+ '_'+ img_label_name[0]
# img_lable[{lable,actual,cont_num,image_corp_name}]
img_label.append({'lable':img_label_name[0], 'actual':img_label_name[1], 'cont_num':cont_num, 'image_corp_name':image_corp_name})
image_corp_org.save(os.path.join(output_path) + '/' + image_corp_name + '_org_'+ image_name[-4:])
cv2.imwrite(os.path.join(output_path) + '/' + image_corp_name + '_gray_'+ image_name[-4:], image_corp_gray)
file = open(os.path.join(PATH_TO_TEST_IMAGES_DIR, 'cont_num.txt'), 'a')
file.write(img_label[cont_num_find - 1]['image_corp_name']+ '_' + img_label[cont_num_find - 1]['actual'] + '\n' + img_label[cont_num_find - 1]['cont_num']+ '\n')
file.close()
return img_label # image_corp_org, image_corp_gray
示例15: predict
# 需要导入模块: from object_detection.utils import visualization_utils [as 别名]
# 或者: from object_detection.utils.visualization_utils import visualize_boxes_and_labels_on_image_array [as 别名]
def predict(self, img):
""" # Arguments
img: a numpy array
# Returns
The url to an image with the bounding boxes
"""
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
with self.graph.as_default():
with tf.Session(graph=self.graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = self.graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = self.graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = self.graph.get_tensor_by_name('detection_scores:0')
detection_classes = self.graph.get_tensor_by_name('detection_classes:0')
num_detections = self.graph.get_tensor_by_name('num_detections:0')
image = Image.fromarray(img)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = load_image_into_numpy_array(image)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
self.category_index,
use_normalized_coordinates=True,
line_thickness=8)
im = Image.fromarray(image_np)
filename = str(uuid.uuid4()) + '.jpg'
save_dir = './outputs'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir, filename)
im.save(save_path)
return json.dumps({'output': filename})