def test_load_drawings():
qd = QuickDrawData()
qd.load_drawings(["anvil", "ant"])
assert qd.loaded_drawings == ["anvil", "ant"]
qd.get_drawing("angel")
assert qd.loaded_drawings == ["anvil", "ant", "angel"]
def pegar_desenho(Escolha="flower"):
qd = QuickDrawData() #define todos o dataset disponivel
arquivo_str = '' #começa uma string onde as informaçoes serão escritas
if Escolha != "random":
desenho = qd.get_drawing(Escolha) #desenho especifico
else:
desenho = qd.get_drawing(random.choice(
qd.drawing_names)) #desenho aleatorio
desenho.image.save("Desenho.png")
data_array = desenho.image_data #data bruto
print(data_array)
print(len(data_array))
#number of strokes
arquivo_str += "{} ".format(len(data_array))
for stroke in desenho.strokes:
#tamanho do stroke
arquivo_str += "{} ".format(len(stroke))
print(len(stroke))
for x, y in stroke:
#stroke
arquivo_str += "{} {} ".format(x, y)
return arquivo_str #devolve a string construida
def find(different_object, the_object, closer, K):
# closer can be True or False. If True, it means closest. If False, it means farthest.
the_point = the_dict.get(the_object)
distance = []
coordinates = latent_dictionary.get(different_object)
for i in range(len(coordinates[0])):
xy = [coordinates[0][i], coordinates[1][i]]
dist = dstnc.euclidean(the_point, xy)
distance.append(dist)
for M in range(K):
if closer:
the_distance = min(distance)
elif not closer:
the_distance = max(distance)
for i in range(len(distance)):
if distance[i] == the_distance:
index = i
break
coordinate = [coordinates[0][index], coordinates[1][index]]
for key in id_vector:
if id_vector.get(key) == coordinate:
the_id = key
break
qd = QuickDrawData()
doodle = qd.get_drawing(different_object)
while not doodle.key_id == the_id:
doodle = qd.get_drawing(different_object)
found = True
if found:
DEFAULT_SIZE_WHITE_CHANNEL = (1000, 1000, 1)
canvas = np.ones(DEFAULT_SIZE_WHITE_CHANNEL, dtype="uint8") * 255
cv2.namedWindow('Window')
my_stroke_list = doodle.image_data
for N in my_stroke_list:
x_list = N[0]
y_list = N[1]
for point in range((len(x_list) - 1)):
cv2.line(canvas, (x_list[point], y_list[point]),
(x_list[point + 1], y_list[point + 1]), (0, 0, 0),
2)
cv2.imwrite(
"./png_sketches/latent_png/" + different_object + "_" +
the_object + "_" + str(closer) + "_" + str(M) + ".png", canvas)
distance.pop(index)
coordinates[0].pop(index)
coordinates[1].pop(index)
else:
print("Image cannot be found.")
class Quickdraw(Dataset):
def __init__(self, datasize, classname=None, transforms=None):
self.datasize = datasize
self.qd = QuickDrawData(max_drawings=self.datasize)
if classname is None:
self.classname = self.qd.drawing_names
else:
self.classname = classname
self.transfroms = transforms
self.label_ids = self.getLabelID(self.classname, self.datasize)
self.img_ids = self.getImageID(self.classname, self.datasize)
def getLabelID(self, classname, datasize):
label_ids = []
for i in range(len(classname)):
label_id = [i for _ in range(datasize)]
label_ids.append(label_id)
label_ids = [element for sublist in label_ids for element in sublist]
return label_ids
def getImageID(self, classname, datasize):
img_ids = []
for i in range(len(classname)):
for j in range(datasize):
img = self.qd.get_drawing(classname[i], index=j)
img_ids.append(img.image)
return img_ids
def __getitem__(self, idx):
return self.img_ids[idx], self.label_ids[idx]
def __len__(self):
return self.datasize * len(self.classname)
def test_get_specific_drawing():
qd = QuickDrawData()
# get the first anvil drawing and test the values
d = qd.get_drawing("anvil", 0)
assert d.name == "anvil"
assert d.key_id == 5355190515400704
assert d.recognized == True
assert d.countrycode == "PL"
assert d.timestamp == 1488368345
# 1 stroke, 2 x,y coords, 33 points
assert len(d.image_data) == 1
assert len(d.image_data[0]) == 2
assert len(d.image_data[0][0]) == 33
assert len(d.image_data[0][1]) == 33
assert d.no_of_strokes == 1
assert len(d.strokes) == 1
assert len(d.strokes[0]) == 33
assert len(d.strokes[0][0]) == 2
assert isinstance(d.image, Image)
assert isinstance(
d.get_image(stroke_color=(10, 10, 10),
stroke_width=4,
bg_color=(200, 200, 200)), Image)
def test_anvil():
qd = QuickDrawData()
anvil = qd.get_drawing("anvil")
# anvil.image.show()
trans = transforms.Compose([transforms.Resize(64), transforms.ToTensor()])
anvil_tensor = transforms.ToTensor()(anvil.image)
down_sized_anvil = trans(anvil.image)
plt.imshow(anvil_tensor.permute(1, 2, 0))
plt.show()
plt.imshow(down_sized_anvil.permute(1, 2, 0));
plt.show()
def loadNdjson(tag, scale, pos, index=None, immutable=False):
try:
if tag not in Status.cache:
qd = QuickDrawData(True)
anvil = qd.get_drawing(tag, index)
Status.cache[tag] = list(anvil.strokes)
if not immutable:
Status.objects[tag] = [scale, pos[0], pos[1]]
if tag in Status.cache and not immutable:
Status.objects[tag] = [scale, pos[0], pos[1]]
except:
pass
def get_drawing(self, recognized=True):
dirname = os.path.dirname(__file__)
cache_dir = os.path.join(dirname, '.quickdrawcache')
qd = QuickDrawData(max_drawings=1, cache_dir=cache_dir)
group_name = random.choice(qd.drawing_names)
qd.load_drawings([group_name])
drawing_data = qd.get_drawing(group_name)
while drawing_data.recognized != recognized:
group_name = random.choice(qd.drawing_names)
qd.load_drawings([group_name])
drawing_data = qd.get_drawing(group_name)
drawing_size = drawing_data.get_image().size
drawing = Image.new('RGB', drawing_size, 'white')
draw = ImageDraw.Draw(drawing)
frame = copy.deepcopy(drawing)
frames = []
for stroke in drawing_data.strokes:
draw.line(stroke, 'black')
frame = copy.deepcopy(drawing)
frames.append(frame)
return frames, drawing_data
def bad_sketch(keyword: str) -> str:
"""
Input a noun you want a sketch of, and if Google Quickdraw finds it,
it will save a random doodle of it to keyword.gif, and return the filepath.
"""
qd = QuickDrawData()
if keyword is not None:
keyword = keyword.lower()
if keyword == "person":
keyword = "smiley face"
try:
key = qd.get_drawing(keyword)
filepath = "keyword.gif"
key.image.save(filepath)
return filepath
except ValueError:
return "blank.png"
class QuickDrawDataset(data.Dataset):
def __init__(self, root, classes, transform):
self.classes = classes
self.labels = torch.arange(len(classes))
self.transform = transform
self.qdd = QuickDrawData(recognized=True, max_drawings=10000, cache_dir=root)
self.qdd.load_drawings(classes)
def __getitem__(self, idx):
c = self.classes[idx%len(self.classes)]
label = self.labels[idx%len(self.classes)]
img = self.qdd.get_drawing(c).image
if self.transform:
img = self.transform(img)
return img, label
def __len__(self):
return 10000
def test_get_random_drawing():
qd = QuickDrawData()
d = qd.get_drawing("anvil", 0)
assert d.name == "anvil"
assert isinstance(d.key_id, int)
assert isinstance(d.recognized, bool)
assert isinstance(d.timestamp, int)
assert isinstance(d.countrycode, str)
assert isinstance(d.image_data, list)
assert len(d.image_data) == d.no_of_strokes
assert isinstance(d.strokes, list)
assert len(d.strokes) == d.no_of_strokes
for stroke in d.strokes:
for point in stroke:
assert len(point) == 2
assert isinstance(d.image, Image)
assert isinstance(
d.get_image(stroke_color=(10, 10, 10),
stroke_width=4,
bg_color=(200, 200, 200)), Image)
return object_list
qd_categories = open_and_read(file_path)
number_of_categories_to_subsample = 60
qd_categories_subsample = random.sample(qd_categories,
number_of_categories_to_subsample)
print("The selected categories are:")
print(qd_categories_subsample)
for category in qd_categories_subsample:
sketch_name = category
number_of_drawings = 20
qd = QuickDrawData()
doodle = qd.get_drawing(sketch_name)
drawing_list = []
with open("./ndjson_files/" + sketch_name + "_simplified_qd.ndjson",
'w') as f:
writer = ndjson.writer(f, ensure_ascii=False)
for i in range(number_of_drawings):
while doodle.recognized is False or doodle in drawing_list:
doodle = qd.get_drawing(sketch_name)
drawing_list.append(doodle)
drawing_map = {
"word": sketch_name,
"key_id": doodle.key_id,
"drawing": doodle.image_data
}
writer.writerow(drawing_map)
from quickdraw import QuickDrawData
qd = QuickDrawData()
anvil = qd.get_drawing("anvil")
stroke_no = 0
for stroke in anvil.strokes:
for x, y in stroke:
# x = xy[0]
# y = xy[1]
print("stroke={} x={} y={}".format(stroke_no, x, y))
stroke_no += 1
import quickDraw
from quickdraw import QuickDrawData
import SptoText as st
tag = st.out
qd = QuickDrawData()
ext = '.jpg'
verb = []
for i in tag:
if i[1] == 'NN' or i[1] == 'NNP':
try:
obj = qd.get_drawing(i[0].lower())
name = i[0].lower()
obj.image.save(name + ext)
except:
continue
elif i[1] == 'VBG':
verb.append(i[0])
else:
continue
for v in verb:
print(v)
max_drawings=1000,
refresh_data=False,
jit_loading=True,
print_messages=True,
cache_dir='./quickdrawcache')
data_out = {}
data_out["categories"] = {}
data_categories = data_out["categories"]
cat_list_f = open(cat_file, encoding="utf-8")
lines = cat_list_f.readlines()
print(len(lines))
for line in lines:
line = line.rstrip('\n')
data_categories[line] = {}
cat_obj = data_categories[line]
cat_obj["drawings"] = []
drawings = cat_obj["drawings"]
for i in range(3):
content = qd.get_drawing(line)
drawing = {}
drawing["id"] = content.key_id
drawing["nb_strokes"] = content.no_of_strokes
drawing["strokes"] = content.strokes
drawings.append(drawing)
with io.open('quickdraw.json', 'w', encoding='utf8') as outfile:
data = json.dumps(data_out, ensure_ascii=False, sort_keys=True, indent=1)
outfile.write(data)
from quickdraw import QuickDrawData
qd = QuickDrawData()
anvil = qd.get_drawing("anvil")
print(anvil)
ant = qd.get_drawing("ant")
print(ant)
import pgzrun
from random import randint
from quickdraw import QuickDrawData
qd = QuickDrawData()
rnd_raccoon = qd.get_drawing("raccoon")
rnd_raccoon.image.save("images/raccoon.png")
raccoon = Actor("raccoon")
beep = tone.create('A3', 0.5)
def draw():
screen.clear()
screen.fill('white')
raccoon.draw()
def place_raccoon():
raccoon.x = randint(100, 750)
raccoon.y = randint(100, 550)
def set_raccoon_normal():
images.cache.clear()
rnd_raccoon = qd.get_drawing("raccoon")
rnd_raccoon.image.save("images/raccoon.png")
raccoon.image = "raccoon.png"
def set_skull():
rnd_skull = qd.get_drawing("skull")
rnd_skull.image.save("images/skull.png")
raccoon.image = "skull.png"
def on_mouse_down(pos):
if raccoon.collidepoint(pos):
from quickdraw import QuickDrawData
if __name__ == '__main__':
quickdraw = QuickDrawData()
print(quickdraw.drawing_names)
n = 10
for i in range(n * 2):
draw = quickdraw.get_drawing("bicycle")
draw.image.save("datasets/trainA/train_bicycle{}.gif".format(i))
for i in range(n):
draw = quickdraw.get_drawing("bicycle")
draw.image.save("datasets/testA/test_bicycle{}.gif".format(i))
for i in range(n * 2):
draw = quickdraw.get_drawing("campfire")
draw.image.save("datasets/trainB/train_campfire{}.gif".format(i))
for i in range(n):
draw = quickdraw.get_drawing("campfire")
draw.image.save("datasets/testB/test_campfire{}.gif".format(i))
import streamlit as st
import pandas as pd
from quickdraw import QuickDrawData
qd = QuickDrawData(recognized=True, jit_loading=False, max_drawings=1000)
qd.load_all_drawings()
drawings = qd.loaded_drawings
n_strokes = [d.no_of_strokes for d in drawings]
anvil = qd.get_drawing(name="dolphin")
st.image(anvil.image)
st.write(f"Groups loaded: {qd.drawing_names}")
from quickdraw import QuickDrawData
from PIL import Image, ImageDraw, ImageOps
import cv2
import numpy as np
qd = QuickDrawData()
anvil = qd.get_drawing("circle")
anvil_image = Image.new("RGB", (600, 600), color=(255, 255, 255))
anvil_drawing = ImageDraw.Draw(anvil_image)
for stroke in anvil.strokes:
# anvil_drawing.line(stroke, fill=(0,0,0), width=2)
for coordinate in range(len(stroke) - 1):
x1 = stroke[coordinate][0] * 2
y1 = stroke[coordinate][1] * 2
x2 = stroke[coordinate + 1][0] * 2
y2 = stroke[coordinate + 1][1] * 2
anvil_drawing.line((x1, y1, x2, y2), fill=(0, 0, 0), width=2)
old_size = anvil_image.size # old_size[0] is in (width, height) format
new_im = ImageOps.expand(anvil_image, border=10, fill=(255, 255, 255))
new_im.show()
anvil_image.show()
anvil_image = new_im
#image processing
open_cv_image = np.array(anvil_image)
class Detector:
def __init__(self):
# Load a model imported from Tensorflow.
frozen_ssd_model = os.path.join(os.path.dirname(__file__), '../models',
'ssd_mobilenet_model',
'frozen_inference_graph.pb')
ssd_config = os.path.join(os.path.dirname(__file__), '../models',
'ssd_mobilenet_model',
'ssd_mobilenet_v1_coco_2017_11_17.pbtxt')
self.tensorflowNet = cv2.dnn.readNetFromTensorflow(
frozen_ssd_model, ssd_config)
# List of dictionaries containg detected objects information.
self.detected_objects = []
# QuckDraw object
cache_path = os.path.join(os.path.dirname(__file__), 'quickdrawcache')
self.qd = QuickDrawData(recognized=True,
max_drawings=1000,
cache_dir=cache_path)
# Label HashMap
self.classNames = {
0: 'background',
1: 'person',
2: 'bicycle',
3: 'car',
4: 'motorcycle',
5: 'airplane',
6: 'bus',
7: 'train',
8: 'truck',
9: 'boat',
10: 'traffic light',
11: 'fire hydrant',
13: 'stop sign',
14: 'parking meter',
15: 'bench',
16: 'bird',
17: 'cat',
18: 'dog',
19: 'horse',
20: 'sheep',
21: 'cow',
22: 'elephant',
23: 'bear',
24: 'zebra',
25: 'giraffe',
27: 'backpack',
28: 'umbrella',
31: 'handbag',
32: 'tie',
33: 'suitcase',
34: 'frisbee',
35: 'skis',
36: 'snowboard',
37: 'sports ball',
38: 'kite',
39: 'baseball bat',
40: 'baseball glove',
41: 'skateboard',
42: 'surfboard',
43: 'tennis racket',
44: 'bottle',
46: 'wine glass',
47: 'cup',
48: 'fork',
49: 'knife',
50: 'spoon',
51: 'bowl',
52: 'banana',
53: 'apple',
54: 'sandwich',
55: 'orange',
56: 'broccoli',
57: 'carrot',
58: 'hot dog',
59: 'pizza',
60: 'donut',
61: 'cake',
62: 'chair',
63: 'couch',
64: 'potted plant',
65: 'bed',
67: 'dining table',
70: 'toilet',
72: 'tv',
73: 'laptop',
74: 'mouse',
75: 'remote',
76: 'keyboard',
77: 'cell phone',
78: 'microwave',
79: 'oven',
80: 'toaster',
81: 'sink',
82: 'refrigerator',
84: 'book',
85: 'clock',
86: 'vase',
87: 'scissors',
88: 'teddy bear',
89: 'hair drier',
90: 'toothbrush'
}
# Tensorflow to QuickDraw HashMap
self.tensorflow_to_quickdraw_hash = {
'background': '',
'person': 'face',
'bicycle': 'bicycle',
'car': 'car',
'motorcycle': 'motorbike',
'airplane': 'airplane',
'bus': 'bus',
'train': 'train',
'truck': 'truck',
'boat': 'sailboat',
'traffic light': 'traffic light',
'fire hydrant': 'fire hydrant',
'stop sign': 'stop sign',
'parking meter': '',
'bench': 'bench',
'bird': 'bird',
'cat': 'cat',
'dog': 'dog',
'horse': 'horse',
'sheep': 'sheep',
'cow': 'cow',
'elephant': 'elephant',
'bear': 'bear',
'zebra': 'zebra',
'giraffe': 'giraffe',
'backpack': 'backpack',
'umbrella': 'umbrella',
'handbag': 'purse',
'tie': 'bowtie',
'suitcase': 'suitcase',
'frisbee': 'circle',
'skis': '',
'snowboard': '',
'sports ball': 'soccer ball',
'kite': 'scissors',
'baseball bat': 'baseball bat',
'baseball glove': '',
'skateboard': 'skateboard',
'surfboard': '',
'tennis racket': 'tennis racquet',
'bottle': 'wine bottle',
'wine glass': 'wine glass',
'cup': 'cup',
'fork': 'fork',
'knife': 'knife',
'spoon': 'spoon',
'bowl': '',
'banana': 'banana',
'apple': 'apple',
'sandwich': 'sandwich',
'orange': '',
'broccoli': 'broccoli',
'carrot': 'carrot',
'hot dog': 'hot dog',
'pizza': 'pizza',
'donut': 'donut',
'cake': 'cake',
'chair': 'chair',
'couch': 'couch',
'potted plant': 'house plant',
'bed': 'bed',
'dining table': 'table',
'toilet': 'toilet',
'tv': 'television',
'laptop': 'laptop',
'mouse': 'mouse',
'remote': 'remote control',
'keyboard': 'keyboard',
'cell phone': 'cell phone',
'microwave': 'microwave',
'oven': 'oven',
'toaster': 'toaster',
'sink': 'sink',
'refrigerator': '',
'book': 'book',
'clock': 'clock',
'vase': 'vase',
'scissors': 'scissors',
'teddy bear': 'teddy-bear',
'hair drier': '',
'toothbrush': 'toothbrush'
}
def detect_object(self, img):
# Input image
img = cv2.cvtColor(numpy.array(img), cv2.COLOR_BGR2RGB)
img_height, img_width, channels = img.shape
# Use the given image as input, which needs to be blob(s).
self.tensorflowNet.setInput(
cv2.dnn.blobFromImage(img,
size=(300, 300),
swapRB=True,
crop=False))
# Runs a forward pass to compute the net output.
networkOutput = self.tensorflowNet.forward()
# For mask rcnn model
# networkOutput, mask = self.tensorflowNet.forward(["detection_out_final", "detection_masks"])
'''
Loop over the detected objects
Detection Indexes:
0: (not used)
1: the identifier of the object's class ex. 5 = 'airplane'
2: the accuracy (score) of the object detected
3: dist of object from the left
4: dist of object from the top
5: dist of object from the right
6: dist of object from the bottom
'''
for detection in networkOutput[0, 0]:
score = float(detection[2])
if score > 0.5:
# Object dimensions
obj_left = detection[3] * img_width
obj_top = detection[4] * img_height
obj_right = detection[5] * img_width
obj_bottom = detection[6] * img_height
# Object name, scale, and x,y offet
name_of_object = self.classNames[detection[1]]
xy_scale = self.get_object_scale(obj_left, obj_right, obj_top,
obj_bottom, img_width,
img_height)
xy_normalized = self.normalize_object_coordinates(
obj_left, obj_top, img_width, img_height)
strokes = self.get_quickdraw_drawing(
self.tensorflow_to_quickdraw_hash[name_of_object])
if strokes is not None:
self.detected_objects.append({
"name": name_of_object,
"scale": xy_scale,
"normalized": xy_normalized,
"img_width": img_width,
"img_height": img_height,
"strokes": strokes
})
# Check for a person to be detected
if detection[1] == 1:
self.person_detected(obj_left, obj_right, obj_top,
obj_bottom, img_width, img_height)
# draw a red rectangle around detected objects
cv2.rectangle(img, (int(obj_left), int(obj_top)),
(int(obj_right), int(obj_bottom)), (0, 0, 255),
thickness=8)
# Resize the image
# scaled_width = 1000
# scaled_height = int(scaled_width * img_height / img_width)
# img = cv2.resize(img, (scaled_width, scaled_height), interpolation = cv2.INTER_AREA)
object_info = self.detected_objects
# Empty the detected objects for the next call
self.detected_objects = []
return img, object_info
def person_detected(self, obj_left, obj_right, obj_top, obj_bottom,
img_width, img_height):
# Remove face from detected objects
object_data = self.detected_objects.pop()
# Calculate new top and bottom heights
face_bottom = obj_bottom - ((obj_bottom - obj_top) * (2 / 3))
shirt_bottom = obj_bottom - ((obj_bottom - obj_top) * (1 / 3))
pants_bottom = obj_bottom
face_top = obj_top
shirt_top = obj_top + ((obj_bottom - obj_top) * (1 / 3))
pants_top = obj_top + ((obj_bottom - obj_top) * (2 / 3))
# Scale
face_scale = self.get_object_scale(obj_left, obj_right, face_top,
face_bottom, img_width, img_height)
shirt_scale = self.get_object_scale(obj_left, obj_right, shirt_top,
shirt_bottom, img_width,
img_height)
pants_scale = self.get_object_scale(obj_left, obj_right, pants_top,
pants_bottom, img_width,
img_height)
# Normalize
face_normalize = self.normalize_object_coordinates(
obj_left, face_top, img_width, img_height)
shirt_normalize = self.normalize_object_coordinates(
obj_left, shirt_top, img_width, img_height)
pants_normalize = self.normalize_object_coordinates(
obj_left, pants_top, img_width, img_height)
# Strokes
face_strokes = object_data["strokes"]
shirt_strokes = self.get_quickdraw_drawing("t-shirt")
pants_strokes = self.get_quickdraw_drawing("pants")
# Add objects
self.detected_objects.append({
"name": "face",
"scale": face_scale,
"normalized": face_normalize,
"img_width": img_width,
"img_height": img_height,
"strokes": face_strokes
})
self.detected_objects.append({
"name": "t-shirt",
"scale": shirt_scale,
"normalized": shirt_normalize,
"img_width": img_width,
"img_height": img_height,
"strokes": shirt_strokes
})
self.detected_objects.append({
"name": "pants",
"scale": pants_scale,
"normalized": pants_normalize,
"img_width": img_width,
"img_height": img_height,
"strokes": pants_strokes
})
def get_object_scale(self, obj_left, obj_right, obj_top, obj_bottom,
img_width, img_height):
scale_x = (obj_right - obj_left) / img_width
scale_y = (obj_bottom - obj_top) / img_height
return [scale_x, scale_y]
def normalize_object_coordinates(self, obj_left, obj_top, img_width,
img_height):
x_normalized = obj_left / img_width
y_normalized = obj_top / img_height
return [x_normalized, y_normalized]
'''
Example Input:
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Lets say we want to normalize the cartoon point of (100,100) ->
cartoon_img_width = 255
cartoon_img_height = 255
img_width = 2000
img_height = 1300
canvas_width = 1200
canvas_height = 700
img_scale_x = found w/ get_object_scale() = 0.30
img_scale_y = found w/ get_object_scale() = 0.98
obj_left = 1000
obj_top = 20
obj_bottom = 1300
obj_right = 1600
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Determining the normalized coordinates for the cartoon drawing:
- Call normalize_object_coordinates() to get the normalized x and y coordinates for the object: x_norm = 0.5 | y_norm = 0.015
- x point scaled and normalized = ((cartoon_point_x / cartoon_img_width) * (img_scale_x * img_width)) + (x_norm*canvas_width) -> ((100/255)*.30*1200) + (.5*1200)
- y point scaled and normalized = ((cartoon_point_y / cartoon_img_height) * (img_scale_y * img_height)) + (y_norm*canvas_height) -> ((100/255)*0.98*700) + (.015*700)
'''
def get_quickdraw_drawing(self, name):
# Initialize a QuickDraw object to access the API
if name != "":
cur_object = self.qd.get_drawing(name)
else:
print("Not a valid QuickDraw image!")
return None
return cur_object.strokes
def visualize_the_matrix(number_of_categories):
for obj in matrix_info:
row_source = obj[0]
for i in range(3):
column_target = (obj[1])[i]
the_id = (obj[2])[i]
most_similar_source_id = (obj[3])[i]
# Find the most similar target sketches.
qd = QuickDrawData()
doodle = qd.get_drawing(column_target)
while not doodle.key_id == the_id:
doodle = qd.get_drawing(column_target)
found = True
if found:
DEFAULT_SIZE_WHITE_CHANNEL = (300, 300, 1)
canvas = np.ones(DEFAULT_SIZE_WHITE_CHANNEL,
dtype="uint8") * 255
cv2.namedWindow('Window')
my_stroke_list = doodle.image_data
for N in my_stroke_list:
x_list = N[0]
y_list = N[1]
for point in range((len(x_list) - 1)):
cv2.line(canvas, (x_list[point], y_list[point]),
(x_list[point + 1], y_list[point + 1]),
(0, 0, 0), 2)
cv2.imwrite(
"./png_sketches/trial_pngs" + "source_" + row_source +
"_" + str(i) + ".png", canvas)
for i in range(3):
column_target = (obj[1])[i]
the_id = (obj[2])[i]
most_similar_source_id = (obj[3])[i]
# Find the most similar target sketches.
qd = QuickDrawData()
doodle = qd.get_drawing(row_source)
while not doodle.key_id == most_similar_source_id:
doodle = qd.get_drawing(row_source)
found = True
if found:
DEFAULT_SIZE_WHITE_CHANNEL = (300, 300, 1)
canvas = np.ones(DEFAULT_SIZE_WHITE_CHANNEL,
dtype="uint8") * 255
cv2.namedWindow('Window')
my_stroke_list = doodle.image_data
for N in my_stroke_list:
x_list = N[0]
y_list = N[1]
for point in range((len(x_list) - 1)):
cv2.line(canvas, (x_list[point], y_list[point]),
(x_list[point + 1], y_list[point + 1]),
(0, 0, 0), 2)
cv2.imwrite(
"./png_sketches/trial_pngs" + "source_" + row_source +
"_" + str(i) + "_sourceimage.png", canvas)
excel = xlsxwriter.Workbook("./png_sketches/" + "matrix_six_with_targets" +
".xlsx")
worksheet = excel.add_worksheet()
worksheet.set_default_row(300)
worksheet.set_column('A:ZZ', 50)
row_index = -1
column_empty = True
for source_categ in reference_dict.keys():
row_index += 1
column_index = -1
for ordered in range(6):
column_index += 1
if ordered < 3:
pic = "./png_sketches/trial_pngs" + "source_" + source_categ + "_" + str(
ordered) + ".png"
worksheet.insert_image(row_index + 1, column_index + 1, pic)
if column_empty:
worksheet.write(0, column_index + 1,
("target_similar" + str(ordered + 1)))
else:
pic = "./png_sketches/trial_pngs" + "source_" + source_categ + "_" + str(
ordered - 3) + "_sourceimage.png"
worksheet.insert_image(row_index + 1, column_index + 1, pic)
if column_empty:
worksheet.write(0, column_index + 1,
("source_similar_to" + str(ordered - 2)))
column_empty = False
worksheet.write(row_index + 1, 0, source_categ)
excel.close()
def getTree():
qd = QuickDrawData()
return qd.get_drawing("tree")
for l in lines:
ax.plot(l["x"], l["y"])
return fig, ax
ad = AxiDraw()
ad.interactive()
ad.connect()
ad.options.speed_pendown = 5
ad.options.speed_penup = 100
ad.options.units = 2
ad.update()
try:
# 6435186029887488
drawing = qd.get_drawing(name="key")
st.write(f"strokes : {drawing.no_of_strokes}")
lines = reshape_strokes(drawing, scale=6)
fig, ax = draw_pic_from_lines(lines)
fig.savefig("test.png")
reference_xy = (
np.random.uniform(low=0, high=250, size=1)[0],
np.random.uniform(low=0, high=150, size=1)[0],
)
# draw_lines(ad,lines,reference_xy=reference_xy)
# st.pyplot(fig,figsize=(2, 2))
# st.write(lines)
finally: