Split a list of dictionaries into multiple chunks - python

I have this one list
list_dict = [
{'id': 1},
{'item': 'apple'},
{'id': 2},
{'item': 'pear'},
{'id': 1},
{'item': 'peach'},
{'id': 2},
{'item': 'kiwi'},
{'id': 3},
{'item': 'banana'},
{'id': 4},
{'item': 'mango'},
{'id': 1},
{'item': 'watermelon'},
{'id': 2},
{'item': 'plum'},
{'id': 3},
{'item': 'grapes'}]
and I want to split like this (start to make sublist when "id" is 1)
result = [
[{'id': 1},
{'item': 'apple'},
{'id': 2},
{'item': 'pear'}],
[{'id': 1},
{'item': 'peach'},
{'id': 2},
{'item': 'kiwi'},
{'id': 3},
{'item': 'banana'},
{'id': 4},
{'item': 'mango'}],
[{'id': 1},
{'item': 'watermelon'},
{'id': 2},
{'item': 'plum'},
{'id': 3},
{'item': 'grapes'}]]


Some nested for loop will work
list_dict = [{"id":1},{"item":"apple"},{"id":2},{"item":"pear"},{"id":1},{"item":"peach"},{"id":2},{"item":"kiwi"},{"id":3},{"item":"banana"},{"id":4},{"item":"mango"},{"id":1},{"item":"watermelon"},{"id":2},{"item":"plum"},{"id":3},{"item":"grapes"}]
output = []
temp = []
for i in list_dict:
if i.get('id', -1) == 1 and temp:
output.append(temp.copy())
temp.clear()
temp.append(i)
else:
temp.append(i)
output.append(temp.copy())
print(output)
#[[{'id': 1}, {'item': 'apple'}, {'id': 2}, {'item': 'pear'}], [{'id': 1}, {'item': 'peach'}, {'id': 2}, {'item': 'kiwi'}, {'id': 3}, {'item': 'banana'}, {'id': 4}, {'item': 'mango'}], [{'id': 1}, {'item': 'watermelon'}, {'id': 2}, {'item': 'plum'}, {'id': 3}, {'item': 'grapes'}]]


Maybe something like this?
main_list = []
current_list = []
for el in list_dict:
if el.get("id", 0) == 1:
if current_list:
main_list.append(current_list)
current_list = [el]
else:
current_list.append(el)
if current_list:
main_list.append(current_list)
print(main_list)

Related

change dict in pandas

I got this dictionary:
[{'id': 1, 'code': 'a'},
{'id': 2, 'code': 'b'},
{'id': 3, 'code': 'c'}]
and I want to change it to:
[{ 1: 'a'},
{2: 'b'},
{ 3: 'c'}]
(python pandas)

a = [{'id': 1, 'code': 'a'},
{'id': 2, 'code': 'b'},
{'id': 3, 'code': 'c'}]
b = []
for dic in a:
b.append({dic['id'] : dic['code']})
print(b)
>>[{1: 'a'}, {2: 'b'}, {3: 'c'}]

One option is with a dictionary comprehension:
[{ent['id']: ent['code']} for ent in a]
[{1: 'a'}, {2: 'b'}, {3: 'c'}]

Python: Descending order and just 3 objects has a high value [duplicate]

This question already has answers here:
How do I sort a list of dictionaries by a value of the dictionary?
(20 answers)
Closed 6 months ago.
I have an array object like that, Not sort value, I want descending order and just 3 objects has a high value:
[{'id': 1, 'value': 3},
{'id': 2, 'value': 6},
{'id': 3, 'value': 8},
{'id': 4, 'value': 8},
{'id': 5, 'value': 10},
{'id': 6, 'value': 9},
{'id': 7, 'value': 8},
{'id': 8, 'value': 4},
{'id': 9, 'value': 5}]
I want result is descending order and just 3 objects have a high value, like this
[{'id': 5, 'value': 10},
{'id': 6, 'value': 9},
{'id': 7, 'value': 8},
{'id': 3, 'value': 8},
{'id': 4, 'value': 8},]
Please help me, thanks

t = [{'id': 1, 'value': 3},
{'id': 2, 'value': 6},
{'id': 3, 'value': 8},
{'id': 4, 'value': 8},
{'id': 5, 'value': 10},
{'id': 6, 'value': 9},
{'id': 7, 'value': 8}]
newlist = sorted(t, key=lambda d: d['value'])
newlist.reverse()
print(newlist[:3])
# [{'id': 5, 'value': 10}, {'id': 6, 'value': 9}, {'id': 7, 'value': 8}]
More info about list slicing
More info about reverse()
More info

How do I split list of dict in Python?

lis_dict = [
{item: "some item"},
{quantity: 2},
{id: 10},
{quantity: 2},
{id: 11},
{quantity: 2},
{quantity: 2},
{id: 12}
]
I have above list of dict, which I would like to split into a sub-list.
result = [
[{item: "some item"}, {quantity: 2}, {id: 10}],
[{quantity: 2}, {id: 11}],
[{quantity: 2}, {quantity: 2}, {id: 12}]
]

The question asks to split the list by "id" into a nested list.
Input:
lis_dict
[{'item': 'some item'},
{'quantity': 2},
{'id': 10},
{'quantity': 2},
{'id': 11},
{'quantity': 2},
{'quantity': 2},
{'id': 12}]
Code:
result = []
s = 0
for i,j in enumerate(lis_dict):
if ("id" in j.keys()):
result.append(lis_dict[s:i+1])
s = i+1
Prints:
[[{'item': 'some item'}, {'quantity': 2}, {'id': 10}],
[{'quantity': 2}, {'id': 11}],
[{'quantity': 2}, {'quantity': 2}, {'id': 12}]]

A more general approach might be the following (in which you could add more dictionaries in a sublist):
lis_dict = [{'item': 'some item'},
{'quantity': 2},
{'id': 10},
{'quantity': 2},
{'item': 'some item'},
{'id': 11},
{'quantity': 2},
{'quantity': 2},
{'id': 12}]
result = []
sublist = []
for d in lis_dict:
sublist.append(d)
if d.get("id"):
result.append(sublist)
sublist = []
print(result)
# [[{'item': 'some item'}, {'quantity': 2}, {'id': 10}], [{'quantity': 2},
# {'item': 'some item'}, {'id': 11}], [{'quantity': 2}, {'quantity': 2}, {'id': 12}]]

TensorFlow Object Detection API print objects found on image to console

I'm trying to return list of objects that have been found at image with TF Object Detection API.
To do that I'm using print([category_index.get(i) for i in classes[0]]) to print list of objects that have been found or print(num_detections) to display number of found objects, but in both cases it gives me list with 300 values or simply value [300.] correspondingly.
How it`s possible to return only that objects that are on image? Or if there is some mistake please help to figure out what is wrong.
I was using Faster RCNN models config file and checkpoints while training. Be sure it really detects few objects at image, here it is:
My code:
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
PATH_TO_CKPT = 'frozen_graph/frozen_inference_graph.pb'
PATH_TO_LABELS = 'object_detection/pascal_label_map.pbtxt'
NUM_CLASSES = 7
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
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)
PATH_TO_TEST_IMAGES_DIR = 'object_detection/test_images/'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 2) ]
IMAGE_SIZE = (12, 8)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
sess.run(tf.global_variables_initializer())
img = 1
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
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)
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')
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')
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
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)
plt.figure(figsize=IMAGE_SIZE)
plt.imsave('RESULTS/' + str(img) + '.jpg', image_np)
img += 1
# Return found objects
print([category_index.get(i) for i in classes[0]])
print(boxes.shape)
print(num_detections)
Which gives following result:
[{'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'chesterfield_blue', 'id': 1}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_gold', 'id': 5}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_red', 'id': 7}, {'name': 'chesterfield_red', 'id': 2}, {'name': 'marlboro_red', 'id': 7}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_red', 'id': 7}, {'name': 'lucky_strike_blue', 'id': 3}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'lucky_strike_red', 'id': 4}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'marlboro_mentol', 'id': 6}, {'name': 'lucky_strike_red', 'id': 4}]
(1, 300, 4)
[ 300.]
Thanks in advance for any information!
UPD:
Thousand thanks for everyone who helped with this question.
Following line of code is exactly what I needed, it gives me list with objects that were found so I can do other operations on them.
print [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5]

As far as I can see you have 300 detections. visualize_boxes_and_labels_on_image_array shows very few of them because min_score_thresh=.5 (this is the default value) is too high for the most of them.
If you want to add such filtering to the output you can write:
min_score_thresh = 0.5
print([category_index.get(i) for i in classes[0] if scores[0, i] > min_score_thresh)
You can change min_score_thresh to choose threshold value you need. It may be useful to print the score values with the category names.

From the function signature visualize_boxes_and_labels_on_image_array, you have to set the arguments max_boxes_to_draw, min_score_thresh,
visualize_boxes_and_labels_on_image_array(image,
boxes,
classes,
scores,
category_index,
instance_masks=None,
keypoints=None,
use_normalized_coordinates=False,
max_boxes_to_draw=20,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4)

Try to set the min_score_thresh to 0. Then you will probably see 300 detections.

open visualization_utils.py and add--> print(class_name) after
else:
class_name = 'N/A'
display_str = '{}: {}%'.format(
class_name,
int(100*scores[i]))
this will print the detected objects

adding print(class_name) after
else:
class_name = 'N/A'
display_str = '{}: {}%'.format(
class_name,
int(100*scores[i]))
in visualization_utils.py file prints the detected object.
I wonder where to add print command to print timestamps as well as percentage of accuracy in output.

// this will load the labels and categories along with category index
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
//to print the identified object do the following :
print category instead of category index. The index holds the numeric value and the category contains the name of the objects. Once identified with the mentioned threshold the
min_score_thresh = 0.5
print([category.get(1)] for i in classes[0] if scores[0, i] > min_score_thresh)
this will print the identified category.

How to use list comprehensions to make a dict having list of list as values

I Have a list as following and I want to convert that as output shown below using List comprehensions. Any help is appreciated.
a = [{'type': 'abc', 'values': 1},
{'type': 'abc', 'values': 2},
{'type': 'abc', 'values': 3},
{'type': 'xyz', 'values': 4},
{'type': 'xyz', 'values': 5},
{'type': 'pqr', 'values': 6},
{'type': 'pqr', 'values': 8},
{'type': 'abc', 'values': 9},
{'type': 'mno', 'values': 10},
{'type': 'def', 'values': 11}]
This is the output I am expecting.
output = {'abc': [1,2,3,9], 'xyz': [4,5], 'pqr': [6,8], 'mno': [10], 'def': [11]}

from operator import itemgetter
from itertools import groupby
a = [{'type': 'abc', 'values': 1},
{'type': 'abc', 'values': 2},
{'type': 'abc', 'values': 3},
{'type': 'xyz', 'values': 4},
{'type': 'xyz', 'values': 5},
{'type': 'pqr', 'values': 6},
{'type': 'pqr', 'values': 8},
{'type': 'abc', 'values': 9},
{'type': 'mno', 'values': 10},
{'type': 'def', 'values': 11}]
typegetter = itemgetter('type')
valuesgetter = itemgetter('values')
groups = groupby(sorted(a, key=typegetter), key=typegetter)
print {k:list(map(valuesgetter, v)) for k, v in groups}

a = [{'type': 'abc', 'values': 1},
{'type': 'abc', 'values': 2},
{'type': 'abc', 'values': 3},
{'type': 'xyz', 'values': 4},
{'type': 'xyz', 'values': 5},
{'type': 'pqr', 'values': 6},
{'type': 'pqr', 'values': 8},
{'type': 'abc', 'values': 9},
{'type': 'mno', 'values': 10},
{'type': 'def', 'values': 11}]
output = {}
for item in a:
output[item['type']] = [item['values']] if output.get(item['type'], None) is None else output[item['type']] + [item['values']]
print output

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