def show_results(self,
set_number,
seq_number,
frame_number,
clas_guess,
guess_pos,
guess_scores,
original_image=False):
self.load_annotations() # Will be needed
if CaltechDataset.USE_CROPPING:
if original_image:
image = Image.open(
self.dataset_location +
'/images/set{:02d}/V{:03d}.seq/{}.jpg'.format(
set_number, seq_number, frame_number))
else:
image = Image.open(
self.dataset_location +
'/images-cropped/set{:02d}/V{:03d}.seq/{}.jpg'.format(
set_number, seq_number, frame_number))
transform = np.load(
self.dataset_location +
'/images-cropped/set{:02d}/V{:03d}.seq/{}.transform.npy'.
format(set_number, seq_number, frame_number))
else:
image = Image.open(self.dataset_location +
'/images/set{:02d}/V{:03d}.seq/{}.jpg'.format(
set_number, seq_number, frame_number))
dr = ImageDraw.Draw(image)
# Retrieve objects for that frame in annotations
try:
objects = self.annotations['set{:02d}'.format(set_number)][
'V{:03d}'.format(seq_number)]['frames']['{}'.format(
frame_number)]
except KeyError as e:
objects = None # Simply no objects for that frame
if objects:
for o in objects:
pos = (o['pos'][1], o['pos'][0], o['pos'][3], o['pos'][2]
) # Convert to (y, x, h, w)
if CaltechDataset.USE_CROPPING and not original_image:
pos = transform_cropped_pos(pos, transform)
if o['lbl'] in ['person']:
good = True
# Remove objects with very small width (are they errors in labeling?!)
if pos[3] < CaltechDataset.MINIMUM_WIDTH:
good = False
if o['occl'] == 1:
if type(o['posv']) == int:
good = False
else:
visible_pos = (o['posv'][1], o['posv'][0],
o['posv'][3], o['posv'][2]
) # Convert to (y, x, h, w)
if CaltechDataset.USE_CROPPING and not original_image:
visible_pos = transform_cropped_pos(
visible_pos, transform)
if visible_pos[2] * visible_pos[
3] < CaltechDataset.MINIMUM_VISIBLE_RATIO * pos[
2] * pos[3]:
good = False
pos = visible_pos
if good:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='blue')
else:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='pink')
else:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='black')
if not CaltechDataset.USE_CROPPING or not original_image:
for y in range(clas_guess.shape[0]):
for x in range(clas_guess.shape[1]):
for anchor_id in range(clas_guess.shape[2]):
if clas_guess[y, x, anchor_id] == 0.0: # Negative
dr.rectangle(
(CaltechDataset.OUTPUT_CELL_SIZE * x,
CaltechDataset.OUTPUT_CELL_SIZE * y,
CaltechDataset.OUTPUT_CELL_SIZE *
(x + 1) - 1, CaltechDataset.OUTPUT_CELL_SIZE *
(y + 1) - 1),
outline='red')
else: # Positive
dr.rectangle(
(CaltechDataset.OUTPUT_CELL_SIZE * x,
CaltechDataset.OUTPUT_CELL_SIZE * y,
CaltechDataset.OUTPUT_CELL_SIZE *
(x + 1) - 1, CaltechDataset.OUTPUT_CELL_SIZE *
(y + 1) - 1),
outline='green')
for row in range(guess_pos.shape[0]):
pos = guess_pos[row]
if CaltechDataset.USE_CROPPING and original_image:
pos = untransform_cropped_pos(pos, transform)
dr.rectangle((pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='green')
dr.text((pos[1], pos[0]), '{:.3f}'.format(guess_scores[row]))
image.show()
def binary_classificn_pts(extent=0, datt=[], scale=1, r=np.eye(2), t1=0, t2=0,
trnsp1=120, trnsp2=120,
trp1_hi=120, trp1_lo=120,
trp2_hi=120, trp2_lo=120,
hi1_ind=1000, lo1_ind=0,
hi2_ind=1000, lo2_ind=0,
xshift1=0, xshift2=0):
# Basic stuff with points.
w=5
im = Image.new("RGB", (2048, 2048), (1, 1, 1))
draw = ImageDraw.Draw(im, 'RGBA')
if extent == 0:
return (im, draw)
means1_orig = np.array([500, 1500])
means2_orig = np.array([1500, 500])
means1 = generalized_planar_rotation(means1_orig, np.array([1000,1000]), r)
means2 = generalized_planar_rotation(means2_orig, np.array([1000,1000]), r)
p=0.3
center_1_orig = means1_orig*p+means2_orig*(1-p)
center_2_orig = means2_orig*p+means1_orig*(1-p)
center_1 = means1*p+means2*(1-p)
center_2 = means2*p+means1*(1-p)
means = center_1
stds = [300, 300]
corr = 0.8 # correlation
covs = [[stds[0]**2 , stds[0]*stds[1]*corr],
[stds[0]*stds[1]*corr, stds[1]**2]]
np.random.seed(0)
m_1 = np.random.multivariate_normal(center_1_orig, covs, 200)*scale
m = m_1
for i in range(m.shape[0]):
r_pt = generalized_planar_rotation(m[i], np.array([1000,1000]), r)
if extent > 0 and extent < 5:
draw.ellipse((r_pt[0]-w,r_pt[1]-w,r_pt[0]+w,r_pt[1]+w), fill = (255,30,102), outline = (0,0,0))
means = center_2
np.random.seed(4)
m_2 = np.random.multivariate_normal(center_2_orig, covs, 200)*scale
m = m_2
for i in range(m.shape[0]):
r_pt = generalized_planar_rotation(m[i], np.array([1000,1000]), r)
if extent > 1 and extent < 5:
draw.ellipse((r_pt[0]-w,r_pt[1]-w,r_pt[0]+w,r_pt[1]+w), fill = (20,255,102), outline = (0,0,0))
# Draw the separating line.
center = (center_1+center_2)/2
orthog = -np.dot(planar_rotation(-np.pi/2), (center_2-center_1))
main_dircn = (center_2 - center_1)
p=2.0
pt_1 = center + orthog*p
pt_2 = center - orthog*p
if extent > 2:
draw.line((pt_1[0], pt_1[1], pt_2[0], pt_2[1]), fill = 'grey', width = 7)
avgs = []
means = np.sum(m_1,axis=1)/2
avgs2 = []
means2 = np.sum(m_2,axis=1)/2
for i in range(len(means)):
avgs.append((((m_1[i][1]<m_1[i][0]))-0.5)*2*np.sqrt(sum((m_1[i]-means[i])**2)))
avgs2.append((((m_2[i][1]<m_2[i][0]))-0.5)*2*np.sqrt(sum((m_2[i]-means2[i])**2)))
avgs = np.array(avgs)
avgs2 = np.array(avgs2)
dat1 = m_1[avgs > np.percentile(avgs,97)]
dat2 = m_2[avgs2 < np.percentile(avgs,4)]
for i in dat1:
datt.append(i)
for i in dat2:
datt.append(i)
xs = [1000]
for i in range(1,8):
pt_1_a = pt_1 + main_dircn*i/7
pt_1_b = pt_1 - main_dircn*i/7
pt_2_a = pt_2 + main_dircn*i/7
pt_2_b = pt_2 - main_dircn*i/7
if extent > 3:
draw.line((pt_1_a[0], pt_1_a[1], pt_2_a[0], pt_2_a[1]), fill = (255,255,255,100), width = 4)
draw.line((pt_1_b[0], pt_1_b[1], pt_2_b[0], pt_2_b[1]), fill = (255,255,255,100), width = 4)
xs.append(pt_1_a[0])
xs.append(pt_1_b[0])
xs.sort()
xs.append(max(xs)+xs[1]-xs[0])
xs.append(min(xs)-xs[1]+xs[0])
# I basically want to transfer the last element to the first position
# so sort is overkill. But it's a small array, so no matter.
xs.sort()
if extent > 4:
drip_to_bins(draw, xs, m, t1, r, rgb=(20,255,102), transparency=trnsp1, trp_hi=trp1_hi, trp_lo=trp1_lo,
hi_ind=hi1_ind,lo_ind=lo1_ind,xshift=xshift1)
drip_to_bins(draw, xs, m_1, t2, r, rgb=(255,20,102), transparency=trnsp2, trp_hi=trp2_hi, trp_lo=trp2_lo,
hi_ind=hi2_ind,lo_ind=lo2_ind,xshift=xshift2)
return (im, draw)
def show_custom_labels(model, photo, min_confidence):
rekognition = boto3.client('rekognition')
# # Load image from S3 bucket
# s3_connection = boto3.resource('s3')
#
# s3_object = s3_connection.Object(bucket, photo)
# s3_response = s3_object.get()
#
# stream = io.BytesIO(s3_response['Body'].read())
# read in photo
image = cv2.imread(photo)
# if has frame then resize and encode it as jpg
hasFrame, imageBytes = cv2.imencode(".jpg", image)
# run image against model
response = rekognition.detect_custom_labels(Image={
'Bytes': imageBytes.tobytes(),
},
ProjectVersionArn=model,
MinConfidence=min_confidence)
#convert from cv2 to PIL
image_pil = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
imgWidth, imgHeight = image_pil.size
draw = ImageDraw.Draw(image_pil)
# calculate and display bounding boxes for each detected custom label
print('Detected custom labels for ' + photo)
for customLabel in response['CustomLabels']:
print('Label ' + str(customLabel['Name']))
print('Confidence ' + str(customLabel['Confidence']))
if 'Geometry' in customLabel:
box = customLabel['Geometry']['BoundingBox']
left = imgWidth * box['Left']
top = imgHeight * box['Top']
width = imgWidth * box['Width']
height = imgHeight * box['Height']
x = left + (width / 2)
y = top - (height / 2)
fnt = ImageFont.truetype('/Library/Fonts/Arial.ttf', 50)
draw.text((left, top),
customLabel['Name'],
fill='#00d400',
font=fnt)
print('Left: ' + '{0:.0f}'.format(left))
print('Top: ' + '{0:.0f}'.format(top))
print('Label Width: ' + "{0:.0f}".format(width))
print('Label Height: ' + "{0:.0f}".format(height))
print('X: {}, Y: {}'.format(x, y))
points = ((left, top), (left + width, top), (left + width,
top + height),
(left, top + height), (left, top))
draw.line(points, fill='#00d400', width=5)
image_pil.show()
return len(response['CustomLabels'])
#print(ud)
#print(kn_fac_encodis)
count=0
for check in kn_fac_encodis:
#print("Check",check)
# Load an image with an unknown face
unknown_image = face_recognition.load_image_file("E:\\Face Detection\\DEMO 3\\image\\tamizhh.jpg") #am , sugu, suu , unknownface, tamizhh,
# Find all the faces and face encodings in the unknown image
face_locations = face_recognition.face_locations(unknown_image) #face_locations = face_recognition.face_locations(image, model="cnn")
#face_encodings = face_recognition.face_encodings(unknown_image, face_locations)
# Convert the image to a PIL-format image so that we can draw on top of it with the Pillow library
# See http://pillow.readthedocs.io/ for more about PIL/Pillow
pil_image = Image.fromarray(unknown_image)
# Create a Pillow ImageDraw Draw instance to draw with
draw = ImageDraw.Draw(pil_image)
#dupl=[]
locations, face_encodings = get_face_embeddings_from_image(unknown_image)
#print("Encoding_Face_embedding",face_encodings[0])
#print("Encoding_Face_encoding",face_encodings)
matches = face_recognition.compare_faces(check, face_encodings[0])
face_distances = face_recognition.face_distance(check, face_encodings[0])
print(face_distances)
if face_distances[0]<0.44:
print("Photo Matched.....!!!")
count=count+1
#print(di[str(check)])
names.append(di[str(check)])
else:
print("Not Matched..!")
def test():
import torchvision
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
])
dataType = 'train2017'
root_path = "../COCO_dataset/images/%s"%(dataType)
list_root_path = "./data/%s.txt"%(dataType)
dataset = ListDataset(root=root_path,list_file=list_root_path, train=True, transform=transform, input_size=360)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=1, collate_fn=dataset.collate_fn)
for images, loc_targets, cls_targets in dataloader:
print(images.shape)
print(loc_targets.shape)
print(cls_targets.shape)
grid = torchvision.utils.make_grid(images, 1)
torchvision.utils.save_image(grid, 'a.jpg')
print('Loading image..')
net = RetinaNet()
net.eval()
img = Image.open('a.jpg')
w = h = 360
img = img.resize((w,h))
print('Predicting..')
x = transform(img)
x = x.unsqueeze(0)
x = Variable(x)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print('Device used:', device)
if torch.cuda.is_available():
net = net.to(device)
x = x.to(device)
loc_targets = loc_targets.to(device)
with torch.no_grad():
loc_preds, cls_preds = net(x)
print(loc_preds.shape)
print(cls_preds.shape)
print('Decoding..')
encoder = DataEncoder()
boxes, labels, _ = encoder.decode(loc_targets.data.cpu()[0], cls_preds.data.cpu().squeeze(), (w,h))
print("Label : ",labels)
draw = ImageDraw.Draw(img)
# use a truetype font
font = ImageFont.truetype("./font/DELIA_regular.ttf", 20)
for i,(box,label) in enumerate(zip(boxes,labels)):
draw.rectangle(list(box), outline=color_map(int(label)),width = 5)
draw.rectangle(list([box[0],box[1]-17,box[0]+10*len(my_cate[int(label)])+5,box[1]]), outline=color_map(int(label)),width = 3,fill=color_map(int(label)))
draw.text((box[0]+3, box[1]-16), my_cate[int(label)],font = font,fill = (0, 0, 0, 100),width = 5)
plt.imshow(img)
plt.savefig("./test.jpg")
break
def detectar_emociones(foto):
data = open(foto, 'rb')
#headers = {'Ocp-Apim-Subscription-Key': KEY} Se puede usar este header cuando usa un url de internet y en lugar de data usa json{url: url(entre comillas)}
headers = {
'Content-Type': 'application/octet-stream',
'Ocp-Apim-Subscription-Key': SUBSCRIPTION_KEY
} #Funciona porque tiene el content type
params = {
'returnFaceAttributes': 'emotion',
}
face_api_url = BASE_URL + 'detect'
response = requests.post(face_api_url,
params=params,
headers=headers,
data=data)
json_detected = response.json()
face_ids = [d['faceId'] for d in json_detected]
identified_faces = CF.face.identify(face_ids, PERSON_GROUP_ID)
for identified in identified_faces:
candidates_list = identified['candidates']
print("Candidates", candidates_list)
if candidates_list != []:
candidates = candidates_list[0]
persona = candidates['personId']
persona_info = CF.person.get(PERSON_GROUP_ID, persona)
person_name = persona_info['name']
emociones = {}
for resp in json_detected:
if resp['faceId'] == identified['faceId']:
faceRectangle = resp['faceRectangle']
width = faceRectangle['width']
top = faceRectangle['top']
height = faceRectangle['height']
left = faceRectangle['left']
emociones = resp['faceAttributes']['emotion']
print("EMOCIONES", emociones)
break
image = Image.open(foto)
draw = ImageDraw.Draw(image)
draw.rectangle((left, top, left + width, top + height),
outline='red')
font = ImageFont.truetype('Roboto-Regular.ttf', 20)
draw.text((0, 0), person_name, font=font, fill="black")
#emociones = detectar_emociones(foto)
y = 20
font_emotion = ImageFont.truetype('Roboto-Regular.ttf', 15)
for e in emociones:
if emociones[e] > 0.0:
emocion = traducir_sentimiento(e)
draw.text((10, y),
emocion,
font=font_emotion,
fill="black")
draw.text((70, y),
str(round(emociones[e] * 100, 2)) + "%",
font=font_emotion,
fill="black")
y += 20
image.show()
return
'''for j in json_detected:
# left, top, right, bottom = 1100, 870, 1700, 1700
# cropped = imageObject.crop((left, top, right, bottom))
#
# # Display the cropped portion
#
# cropped.show()
# cropped_image.save(saved_location)
path = '/Users/jordanwalker/Downloads/lance-modis-sa/'
filelist = glob.glob(os.path.join(path, '*.jpg'))
filenames = sorted(x for x in filelist)
for im in filenames:
saved_location = im.replace('lance-modis-sa', 'lance-modis-sa-name')
im_object = im.split('/')
img_split = im_object[-1].split('.')
file_label = img_split[-3]
imageObject = Image.open(im)
draw = ImageDraw.Draw(imageObject)
font = ImageFont.truetype('/Library/Fonts/Arial.ttf', 16)
draw.text((5, 5), file_label[0:4], (255, 255, 255), font=font)
imageObject.save(saved_location)
# If you need to crop
# left, top, right, bottom = 1100, 870, 1700, 1700
# cropped = imageObject.crop((left, top, right, bottom))
# cropped.save(saved_location)
async def seasoncreate(self, data, seasondata, season, profile,
seasonname):
font_file = f"{bundled_data_path(self)}/fonts/RobotoRegular.ttf"
bold_file = f"{bundled_data_path(self)}/fonts/RobotoBold.ttf"
name_fnt = ImageFont.truetype(font_file, 42, encoding="utf-8")
header_fnt = ImageFont.truetype(bold_file, 42, encoding="utf-8")
operator = random.choice(self.bgs)
bg_image = Image.open(
f"{bundled_data_path(self)}/bg/{operator}.jpg").convert("RGBA")
profile_image = Image
profile_avatar = BytesIO()
profile_url = data.avatar_url_256
async with self.session.get(profile_url) as r:
profile_img = await r.content.read()
profile_avatar = BytesIO(profile_img)
profile_image = Image.open(profile_avatar).convert("RGBA")
# set canvas
bg_color = (255, 255, 255, 0)
result = Image.new("RGBA", (1600, 1080), bg_color)
process = Image.new("RGBA", (1600, 1080), bg_color)
# draw
draw = ImageDraw.Draw(process)
# puts in background
bg_image = bg_image.resize((1920, 1090), Image.ANTIALIAS)
bg_image = bg_image.crop((0, 0, 1920, 1080))
result.paste(bg_image, (0, 0))
aviholder = self.add_corners(
Image.new("RGBA", (266, 266), (255, 255, 255, 255)), 10)
process.paste(aviholder, (1295, 15), aviholder)
process.paste(profile_image, (1300, 20))
# op badge
if season >= 14:
ranks = self.ranksember
else:
ranks = self.ranks
url = self.rankurl + ranks[seasondata["rank_text"]]
process.paste(aviholder, (995, 15), aviholder)
im = Image.open(BytesIO(await self.getimg(url)))
im = im.resize((256, 256), Image.ANTIALIAS)
process.paste(im, (1000, 20))
# data
# draw.text((440, 40), "Name: {}".format(self._truncate_text(data.username, 18)), fill=(255, 255, 255, 255), font=header_fnt)
draw.text(
(
self._center(
440,
1000,
"Name: {}".format(self._truncate_text(data.username, 18)),
header_fnt,
),
40,
),
"Name: {}".format(self._truncate_text(data.username, 18)),
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(
self._center(440, 1000, "Platform: {}".format(
str(data.platform).title()), header_fnt),
90,
),
"Platform: {}".format(str(data.platform).title()),
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(
self._center(
440, 1000, "Region: {}".format(
self.regions[seasondata["region"]]), header_fnt),
140,
),
"Region: {}".format(self.regions[seasondata["region"]]),
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(self._center(440, 1000, f"{seasonname} Statistics",
header_fnt), 190),
f"{seasonname} Statistics",
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(520, 320),
"Games Played: {}".format(seasondata["wins"] +
seasondata["losses"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 380),
"Wins: {}".format(seasondata["wins"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(960, 380),
"Losses: {}".format(seasondata["losses"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 440),
"Abandons: {}".format(seasondata["abandons"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
try:
wlr = round(
seasondata["wins"] /
(seasondata["wins"] + seasondata["losses"]), 2)
except ZeroDivisionError:
wlr = 0
draw.text(
(960, 440),
"Total W/LR: {}%".format(wlr * 100),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 520),
"Kills: {}".format(seasondata["kills"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(960, 520),
"Deaths: {}".format(seasondata["deaths"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 580),
"MMR: {}".format(seasondata["mmr"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 640),
"Max MMR: {}".format(seasondata["max_mmr"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 700),
"Previous Rank MMR: {}".format(seasondata["prev_rank_mmr"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 760),
"Next Rank MMR: {}".format(seasondata["next_rank_mmr"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 820),
"Rank: {}".format(seasondata["rank_text"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
if seasondata["rank_text"] == "Champions":
draw.text(
(960, 820),
"Champion Rank Position: {}".format(
seasondata["champions_rank_position"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 880),
"Max Rank: {}".format(seasondata["max_rank_text"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
result = Image.alpha_composite(result, process)
file = BytesIO()
result.save(file, "png")
file.name = f"season-{data.username}.png"
file.seek(0)
image = discord.File(file)
return image
async def profilecreate(self, data):
font_file = f"{bundled_data_path(self)}/fonts/RobotoRegular.ttf"
bold_file = f"{bundled_data_path(self)}/fonts/RobotoBold.ttf"
name_fnt = ImageFont.truetype(font_file, 42, encoding="utf-8")
header_fnt = ImageFont.truetype(bold_file, 42, encoding="utf-8")
operator = random.choice(self.bgs)
bg_image = Image.open(
f"{bundled_data_path(self)}/bg/{operator}.jpg").convert("RGBA")
profile_image = Image
profile_avatar = BytesIO()
profile_url = data.avatar_url_256
async with self.session.get(profile_url) as r:
profile_img = await r.content.read()
profile_avatar = BytesIO(profile_img)
profile_image = Image.open(profile_avatar).convert("RGBA")
# set canvas
bg_color = (255, 255, 255, 0)
result = Image.new("RGBA", (1280, 1080), bg_color)
process = Image.new("RGBA", (1280, 1080), bg_color)
# draw
draw = ImageDraw.Draw(process)
# puts in background
bg_image = bg_image.resize((1920, 1090), Image.ANTIALIAS)
bg_image = bg_image.crop((0, 0, 1920, 1080))
result.paste(bg_image, (0, 0))
aviholder = self.add_corners(
Image.new("RGBA", (266, 266), (255, 255, 255, 255)), 10)
process.paste(aviholder, (995, 15), aviholder)
process.paste(profile_image, (1000, 20))
# data
draw.text(
(
self._center(
440,
1000,
"Name: {}".format(self._truncate_text(data.username, 18)),
header_fnt,
),
40,
),
"Name: {}".format(self._truncate_text(data.username, 18)),
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(
self._center(440, 1000, "Platform: {}".format(
str(data.platform).title()), header_fnt),
90,
),
"Platform: {}".format(str(data.platform).title()),
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(self._center(440, 1000, "General Statistics", header_fnt), 140),
"General Statistics",
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(self._center(1000, 1256, f"Level: {data.level}",
header_fnt), 300),
f"Level: {data.level}",
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(520, 400),
"Wins: {}".format(data.general_stats["wins"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(920, 400),
"Losses: {}".format(data.general_stats["losses"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 470),
"Draws: {}".format(data.general_stats["draws"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(920, 470),
"Total W/LR: {}%".format(
round(
data.general_stats["wins"] /
data.general_stats["games_played"], 2) * 100),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 540),
"Lootbox %: {}%".format(data.lootbox_probability),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 610),
"Kills: {}".format(data.general_stats["kills"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(920, 610),
"Deaths: {}".format(data.general_stats["deaths"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 680),
"Assists: {}".format(data.general_stats["assists"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(920, 680),
"KDR: {}".format(data.general_stats["kd"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 750),
"Revives: {}".format(data.general_stats["revives"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(920, 750),
"Suicides: {}".format(data.general_stats["suicides"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 820),
"Blind Kills: {}".format(data.general_stats["blind_kills"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(920, 820),
"Melee Kills: {}".format(data.general_stats["melee_kills"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 890),
"Pentration Kills: {}".format(
data.general_stats["penetration_kills"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(920, 890),
"DBNOs: {}".format(data.general_stats["dbnos"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
m, _ = divmod(data.general_stats["playtime"], 60)
h, m = divmod(m, 60)
d, h = divmod(h, 24)
draw.text(
(520, 1010),
f"Playtime: {d:d}d {h:d}h {m:02d}m",
fill=(255, 255, 255, 255),
font=name_fnt,
)
result = Image.alpha_composite(result, process)
file = BytesIO()
result.save(file, "png")
file.name = f"general-{data.username}.png"
file.seek(0)
image = discord.File(file)
return image
def plotBBoxes(image,
bboxes,
classes=None,
scores=None,
color='Chartreuse',
linewidth=2,
use_rgb=False):
"""Plot bounding boxes for given input objects
Arguments:
----------
image : numpy.array
input image for drawing
bboxes : list((x1, y1, x2, y2))
input bounding boxes of objects to draw
Keyword Arguments:
------------------
classes : list(str) (default: None)
list of classes for objects
color : str (default: `Chartreuse`)
color to plot
linewidth : int (default: 2)
how thick the shape is
use_rgb : bool (default: False)
whether using RGB image or not (apply for NDArray image)
Returns:
--------
image : numpy.array
output image after drawing
"""
if len(bboxes) == 0:
return image
exit()
if isinstance(image, np.ndarray):
if not use_rgb:
image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
image = Image.fromarray(image)
W, H = image.size
draw = ImageDraw.Draw(image)
if classes is not None:
font = ImageFont.truetype(font=cfg.FONT,
size=np.floor(3e-2 * min(H, W) +
0.5).astype('int32'))
for i, ((x1, y1, x2, y2), cls) in enumerate(zip(bboxes, classes)):
# draw bounding box
draw.rectangle([x1, y1, x2, y2],
outline=ImageColor.getrgb(COLOR_MAP[cls]),
width=linewidth)
# draw label
text = cls
if scores is not None:
text = '{} {:.2f}'.format(cls, scores[i])
label_size = draw.textsize(text, font)
text_coor = np.array(
[x1 + linewidth,
max(0, y1 - label_size[1] - 1)])
rec_coor = np.array([x1, text_coor[1]])
draw.rectangle([
tuple(rec_coor),
tuple(rec_coor + label_size + np.array([linewidth * 2, 0]))
],
fill=ImageColor.getrgb(COLOR_MAP[cls]))
draw.text(text_coor,
text,
fill=ImageColor.getrgb('black'),
font=font)
else:
for i, (x1, y1, x2, y2) in enumerate(bboxes):
draw.rectangle([x1, y1, x2, y2],
outline=ImageColor.getrgb(color),
width=linewidth)
del draw
return image
async def casualstatscreate(self, data):
font_file = f"{bundled_data_path(self)}/fonts/RobotoRegular.ttf"
bold_file = f"{bundled_data_path(self)}/fonts/RobotoBold.ttf"
name_fnt = ImageFont.truetype(font_file, 42, encoding="utf-8")
header_fnt = ImageFont.truetype(bold_file, 42, encoding="utf-8")
operator = random.choice(self.bgs)
bg_image = Image.open(
f"{bundled_data_path(self)}/bg/{operator}.jpg").convert("RGBA")
profile_image = Image
profile_avatar = BytesIO()
profile_url = data.avatar_url_256
async with self.session.get(profile_url) as r:
profile_img = await r.content.read()
profile_avatar = BytesIO(profile_img)
profile_image = Image.open(profile_avatar).convert("RGBA")
# set canvas
bg_color = (255, 255, 255, 0)
result = Image.new("RGBA", (1280, 1080), bg_color)
process = Image.new("RGBA", (1280, 1080), bg_color)
# draw
draw = ImageDraw.Draw(process)
# puts in background
bg_image = bg_image.resize((1920, 1090), Image.ANTIALIAS)
bg_image = bg_image.crop((0, 0, 1920, 1080))
result.paste(bg_image, (0, 0))
aviholder = self.add_corners(
Image.new("RGBA", (266, 266), (255, 255, 255, 255)), 10)
process.paste(aviholder, (995, 15), aviholder)
process.paste(profile_image, (1000, 20))
# data
# draw.text((440, 40), "Name: {}".format(self._truncate_text(data.username, 18)), fill=(255, 255, 255, 255), font=header_fnt)
draw.text(
(
self._center(
440,
1000,
"Name: {}".format(self._truncate_text(data.username, 18)),
header_fnt,
),
40,
),
"Name: {}".format(self._truncate_text(data.username, 18)),
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(
self._center(440, 1000, "Platform: {}".format(
str(data.platform).title()), header_fnt),
90,
),
"Platform: {}".format(str(data.platform).title()),
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(self._center(440, 1000, "Alltime Ranked Statistics",
header_fnt), 140),
"Alltime Casual Statistics",
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(self._center(1000, 1256, f"Level: {data.level}",
header_fnt), 300),
f"Level: {data.level}",
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(520, 400),
"Games Played: {}".format(
data.queue_stats["casual"]["games_played"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 480),
"Wins: {}".format(data.queue_stats["casual"]["wins"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(920, 480),
"Losses: {}".format(data.queue_stats["casual"]["losses"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 560),
"Draws: {}".format(data.queue_stats["casual"]["draws"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
try:
wlr = (round(
data.queue_stats["casual"]["wins"] /
data.queue_stats["casual"]["games_played"],
2,
) * 100)
except ZeroDivisionError:
wlr = 0
draw.text((520, 640),
"Total W/LR: {}%".format(wlr),
fill=(255, 255, 255, 255),
font=name_fnt)
draw.text(
(520, 800),
"Kills: {}".format(data.queue_stats["casual"]["kills"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(920, 800),
"Deaths: {}".format(data.queue_stats["casual"]["deaths"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 880),
"KDR: {}".format(data.queue_stats["casual"]["kd"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
m, _ = divmod(data.queue_stats["casual"]["playtime"], 60)
h, m = divmod(m, 60)
d, h = divmod(h, 24)
draw.text(
(520, 960),
f"Playtime: {d:d}d {h:d}h {m:02d}m",
fill=(255, 255, 255, 255),
font=name_fnt,
)
result = Image.alpha_composite(result, process)
file = BytesIO()
result.save(file, "png")
file.name = f"casual-{data.username}.png"
file.seek(0)
image = discord.File(file)
return image
def create_validate_code(size=(120, 30), chars=chars, mode="RGB", \
bg_color=(255, 255, 255), fg_color=(255, 0, 0), \
font_size=18, font_type="msyh.ttf", \
length=4, draw_points=True, point_chance = 2):
'''
size: 图片的大小，格式（宽，高），默认为(120, 30)
chars: 允许的字符集合，格式字符串
mode: 图片模式，默认为RGB
bg_color: 背景颜色，默认为白色
fg_color: 前景色，验证码字符颜色
font_size: 验证码字体大小
font_type: 验证码字体，需要指定一个字体文件的路径
length: 验证码字符个数
draw_points: 是否画干扰点
point_chance: 干扰点出现的概率，大小范围[0, 50]
'''
width, height = size
img = Image.new(mode, size, bg_color)
draw = ImageDraw.Draw(img)
'''随机取出指定个数符串'''
get_chars = lambda : random.sample(chars, length)
def create_points():
'''绘制干扰点，遍历这个图片的所有点，根据概率决定时候加黑点点'''
chance = min(50, max(0, int(point_chance))) # 大小限制在[0, 50]
for w in xrange(width):
for h in xrange(height):
tmp = random.randint(0, 50)
if tmp > 50 - chance:
draw.point((w, h), fill=(0, 0, 0))
def create_strs():
'''绘制验证码字符，验证码的主体'''
c_chars = get_chars()
strs = '%s' % ''.join(c_chars)
font = ImageFont.truetype(font_type, font_size)
font_width, font_height = font.getsize(strs)
#起码的一组参数是位置
draw.text(((width - font_width) / 3, (height - font_height) / 4),
strs, font=font, fill=fg_color)
return strs
if draw_points:
create_points()
strs = create_strs()
#图形扭曲参数
params = [1 - float(random.randint(1, 2)) / 100, 0, 0, 0,
1 - float(random.randint(1, 10)) / 100,
float(random.randint(1, 2)) / 500, 0.001,
float(random.randint(1, 2)) / 500 ]
img = img.transform(size, Image.PERSPECTIVE, params) # 创建扭曲
img = img.filter(ImageFilter.EDGE_ENHANCE_MORE) # 滤镜，边界加强（阈值更大）
print strs
img.save("code1.jpg", "JPEG", quality=85)
def update_frame(self, frame):
img = Image.fromarray(frame)
draw = ImageDraw.Draw(img)
# draw rectangle around the 5 first detected object with a score greater
# than 60%
if self.result_scores[0][0] > 0.60:
y0 = int(self.get_object_location_y0(0) * frame.shape[0])
x0 = int(self.get_object_location_x0(0) * frame.shape[1])
y1 = int(self.get_object_location_y1(0) * frame.shape[0])
x1 = int(self.get_object_location_x1(0) * frame.shape[1])
label = self.get_label(0)
accuracy = self.result_scores[0][0] * 100
draw.rectangle([(x0, y0), (x1, y1)], outline=(0, 0, 255))
draw.rectangle([(x0, y0),
(x0 +
((len(label) + 4) * char_text_width), y0 + 14)],
(0, 0, 255))
draw.text((x0 + 2, y0 + 2), label + " " + str(int(accuracy)) + "%",
(255, 255, 255))
if self.result_scores[0][1] > 0.60:
y0 = int(self.get_object_location_y0(1) * frame.shape[0])
x0 = int(self.get_object_location_x0(1) * frame.shape[1])
y1 = int(self.get_object_location_y1(1) * frame.shape[0])
x1 = int(self.get_object_location_x1(1) * frame.shape[1])
label = self.get_label(1)
accuracy = self.result_scores[0][1] * 100
draw.rectangle([(x0, y0), (x1, y1)], outline=(255, 0, 0))
draw.rectangle([(x0, y0),
(x0 +
((len(label) + 4) * char_text_width), y0 + 14)],
(255, 0, 0))
draw.text((x0 + 2, y0 + 2), label + " " + str(int(accuracy)) + "%",
(255, 255, 255))
if self.result_scores[0][2] > 0.60:
y0 = int(self.get_object_location_y0(2) * frame.shape[0])
x0 = int(self.get_object_location_x0(2) * frame.shape[1])
y1 = int(self.get_object_location_y1(2) * frame.shape[0])
x1 = int(self.get_object_location_x1(2) * frame.shape[1])
label = self.get_label(2)
accuracy = self.result_scores[0][2] * 100
draw.rectangle([(x0, y0), (x1, y1)], outline=(0, 255, 0))
draw.rectangle([(x0, y0),
(x0 +
((len(label) + 4) * char_text_width), y0 + 14)],
(0, 255, 0))
draw.text((x0 + 2, y0 + 2), label + " " + str(int(accuracy)) + "%",
(255, 255, 255))
if self.result_scores[0][3] > 0.60:
y0 = int(self.get_object_location_y0(3) * frame.shape[0])
x0 = int(self.get_object_location_x0(3) * frame.shape[1])
y1 = int(self.get_object_location_y1(3) * frame.shape[0])
x1 = int(self.get_object_location_x1(3) * frame.shape[1])
label = self.get_label(3)
accuracy = self.result_scores[0][3] * 100
draw.rectangle([(x0, y0), (x1, y1)], outline=(255, 255, 0))
draw.rectangle([(x0, y0),
(x0 +
((len(label) + 4) * char_text_width), y0 + 14)],
(255, 255, 0))
draw.text((x0 + 2, y0 + 2), label + " " + str(int(accuracy)) + "%",
(255, 255, 255))
if self.result_scores[0][4] > 0.60:
y0 = int(self.get_object_location_y0(4) * frame.shape[0])
x0 = int(self.get_object_location_x0(4) * frame.shape[1])
y1 = int(self.get_object_location_y1(4) * frame.shape[0])
x1 = int(self.get_object_location_x1(4) * frame.shape[1])
label = self.get_label(4)
accuracy = self.result_scores[0][4] * 100
draw.rectangle([(x0, y0), (x1, y1)], outline=(0, 255, 255))
draw.rectangle([(x0, y0),
(x0 +
((len(label) + 4) * char_text_width), y0 + 14)],
(0, 255, 255))
draw.text((x0 + 2, y0 + 2), label + " " + str(int(accuracy)) + "%",
(255, 255, 255))
data = img.tobytes()
data = GLib.Bytes.new(data)
pixbuf = GdkPixbuf.Pixbuf.new_from_bytes(
data, GdkPixbuf.Colorspace.RGB, False, 8, frame.shape[1],
frame.shape[0], frame.shape[2] * frame.shape[1])
self.image.set_from_pixbuf(pixbuf.copy())
def compute_matches(self,
set_number,
seq_number,
frame_number,
guess_pos,
guess_scores,
original_image=False,
display_image=False):
self.load_annotations() # Will be needed
if CaltechDataset.USE_CROPPING:
if display_image:
if original_image:
image = Image.open(
self.dataset_location +
'/images/set{:02d}/V{:03d}.seq/{}.jpg'.format(
set_number, seq_number, frame_number))
else:
image = Image.open(
self.dataset_location +
'/images-cropped/set{:02d}/V{:03d}.seq/{}.jpg'.format(
set_number, seq_number, frame_number))
transform = np.load(
self.dataset_location +
'/images-cropped/set{:02d}/V{:03d}.seq/{}.transform.npy'.
format(set_number, seq_number, frame_number))
elif display_image:
image = Image.open(self.dataset_location +
'/images/set{:02d}/V{:03d}.seq/{}.jpg'.format(
set_number, seq_number, frame_number))
if display_image:
dr = ImageDraw.Draw(image)
# Retrieve objects for that frame in annotations
try:
objects = self.annotations['set{:02d}'.format(set_number)][
'V{:03d}'.format(seq_number)]['frames']['{}'.format(
frame_number)]
except KeyError as e:
objects = None # Simply no objects for that frame
# We attempt to mimic the reasonable test set
# i.e. 50 pixels or taller, no occlusion (not even partial)
persons = []
undesirables = []
if objects:
for o in objects:
good = False
pos = (o['pos'][1], o['pos'][0], o['pos'][3], o['pos'][2]
) # Convert to (y, x, h, w)
if CaltechDataset.USE_CROPPING and not original_image:
pos = transform_cropped_pos(pos, transform)
if o['lbl'] in ['person']:
good = True
# 50 pixels or taller
if pos[2] < 50:
good = False
if o['occl'] == 1:
good = False
if good:
persons.append(pos)
if display_image:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='blue')
else:
undesirables.append(pos)
if display_image:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='pink')
# Move data to numpy
persons = np.array(persons, dtype=np.float32)
undesirables = np.array(undesirables, dtype=np.float32)
# Sort guesses
index = np.argsort(
guess_scores[:])[::-1] # Decreasing order with [::-1]
guess_pos = guess_pos[index]
guess_scores = guess_scores[index]
default_false_positives = 0
guess_matched = np.zeros(guess_scores.shape, dtype=np.bool)
for row in range(guess_pos.shape[0]):
pos = guess_pos[row]
if CaltechDataset.USE_CROPPING and original_image:
pos = untransform_cropped_pos(pos, transform)
# Try matching
matched = False
if persons.shape[0] > 0:
for r in range(persons.shape[0]):
if IoU(pos, persons[r]) > 0.5:
matched = True
persons = np.delete(persons, r, 0)
break
if matched:
guess_matched[row] = True
if display_image:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='green')
else:
if undesirables.shape[0] > 0:
for r in range(undesirables.shape[0]):
if IoA(pos, undesirables[r]) > 0.5:
matched = True
break
if not matched:
default_false_positives += 1
if display_image:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='red')
elif display_image:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='yellow')
if display_image:
dr.text((pos[1], pos[0]), '{:.3f}'.format(guess_scores[row]))
# Sorted scores of all matches made for computing curves
matched_scores = guess_scores[guess_matched]
default = np.array([default_false_positives, persons.shape[0]])
if display_image:
image.show()
return matched_scores, default
def interface():
#Initialisation
pygame.init()
base, label, distance_matrix = init()
#Création de la fenêtre
window = pygame.display.set_mode((560, 340), pygame.RESIZABLE)
#chargement et application de la zone de dessin et des boutons
draw = pygame.image.load("vide.png").convert()
validate = pygame.image.load("valider.png").convert()
cancel = pygame.image.load("annuler.png").convert()
b0 = pygame.image.load("0.png").convert()
b1 = pygame.image.load("1.png").convert()
b2 = pygame.image.load("2.png").convert()
b3 = pygame.image.load("3.png").convert()
b4 = pygame.image.load("4.png").convert()
b5 = pygame.image.load("5.png").convert()
b6 = pygame.image.load("6.png").convert()
b7 = pygame.image.load("7.png").convert()
b8 = pygame.image.load("8.png").convert()
b9 = pygame.image.load("9.png").convert()
blearn = pygame.image.load("?.png").convert()
black = pygame.image.load("noir.png").convert()
bRM = pygame.image.load("ADD.png").convert()
window.blit(draw, (10, 10))
window.blit(cancel, (120, 10))
window.blit(validate, (230, 10))
window.blit(bRM, (450, 10))
window.blit(b0, (10, 120))
window.blit(b1, (120, 120))
window.blit(b2, (230, 120))
window.blit(b3, (340, 120))
window.blit(b4, (450, 120))
window.blit(b5, (10, 230))
window.blit(b6, (120, 230))
window.blit(b7, (230, 230))
window.blit(b8, (340, 230))
window.blit(b9, (450, 230))
#création de l'image destination et mise en blanc
picture = Image.new("RGB", (100, 100))
initPicture(picture)
draw_img = ImageDraw.Draw(picture)
#Raffraichissement de la fenêtre
pygame.display.flip()
learn = 0
continuer = 1
# saving the last px used for smoothing
last_x = 0
last_y = 0
while continuer:
for event in pygame.event.get():
#gestion de la fermeture de la fenêtre
if event.type == QUIT:
close(base, label, distance_matrix)
continuer = 0
pygame.quit()
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 11 and event.pos[0] < 109 and \
event.pos[1] > 11 and event.pos[1] < 109 :
last_x = event.pos[0]
last_y = event.pos[1]
#gestion du dessin
if event.type == MOUSEMOTION and event.buttons[0] == 1 and \
event.pos[0] > 11 and event.pos[0] < 109 and \
event.pos[1] > 11 and event.pos[1] < 109 :
learn = 1
new_x = event.pos[0]
new_y = event.pos[1]
window.blit(black, (340, 10))
draw_img.line(
(last_x - 10, last_y - 10, new_x - 10, new_y - 10),
fill='black',
width=5)
last_x = new_x
last_y = new_y
#drawPixel(picture,event.pos[0]-10,event.pos[1]-10)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
pygame.display.flip()
#gestion du boulon annuler
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 121 and event.pos[0] < 219 and \
event.pos[1] > 11 and event.pos[1] < 109 :
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
#gestion du bouton RM
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 451 and event.pos[0] < 549 and \
event.pos[1] > 11 and event.pos[1] < 109 :
learn = 1
window.blit(blearn, (340, 10))
pygame.display.flip()
#gestion du bouton valider
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 231 and event.pos[0] < 329 and \
event.pos[1] > 11 and event.pos[1] < 109 and learn == 1 :
picture.save("new.png")
#ouverture du fichier source
new_picture = Image.open("new.png")
word = picture2word(new_picture)
digit = analyse.analyse_triangle_knn_multi(
word, base, label, distance_matrix, 3)
word2 = None
if (zoomout("new.png", "new2.png")):
new_picture2 = Image.open("new2.png")
word2 = picture2word(new_picture2)
digit2 = analyse.analyse_triangle_knn_multi(
word2, base, label, distance_matrix, 3)
if digit == 0:
window.blit(b0, (340, 10))
elif digit == 1:
window.blit(b1, (340, 10))
elif digit == 2:
window.blit(b2, (340, 10))
elif digit == 3:
window.blit(b3, (340, 10))
elif digit == 4:
window.blit(b4, (340, 10))
elif digit == 5:
window.blit(b5, (340, 10))
elif digit == 6:
window.blit(b6, (340, 10))
elif digit == 7:
window.blit(b7, (340, 10))
elif digit == 8:
window.blit(b8, (340, 10))
elif digit == 9:
window.blit(b9, (340, 10))
else:
window.blit(blearn, (340, 10))
# if digit != -1 :
# learn = 2
# base[digit].append(word)
# if learn == 1 :
# window.blit(blearn, (340,10))
# else :
# initPicture(picture)
# picture.save("temp.png")
# draw = pygame.image.load("temp.png").convert()
# window.blit(draw, (10,10))
pygame.display.flip()
#gestion des boutons chiffres
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 11 and event.pos[0] < 109 and \
event.pos[1] > 121 and event.pos[1] < 219 and learn == 1 :
base.append(word)
label.append(0)
distance_matrix = update_matrix(base, distance_matrix, word)
if (word2 != None):
base.append(word2)
label.append(0)
distance_matrix = update_matrix(base, distance_matrix,
word2)
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 121 and event.pos[0] < 219 and \
event.pos[1] > 121 and event.pos[1] < 219 and learn == 1 :
base.append(word)
label.append(1)
distance_matrix = update_matrix(base, distance_matrix, word)
if (word2 != None):
base.append(word2)
label.append(1)
distance_matrix = update_matrix(base, distance_matrix,
word2)
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 231 and event.pos[0] < 329 and \
event.pos[1] > 121 and event.pos[1] < 219 and learn == 1 :
base.append(word)
label.append(2)
distance_matrix = update_matrix(base, distance_matrix, word)
if (word2 != None):
base.append(word2)
label.append(2)
distance_matrix = update_matrix(base, distance_matrix,
word2)
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 341 and event.pos[0] < 439 and \
event.pos[1] > 121 and event.pos[1] < 219 and learn == 1:
base.append(word)
label.append(3)
distance_matrix = update_matrix(base, distance_matrix, word)
if (word2 != None):
base.append(word2)
label.append(3)
distance_matrix = update_matrix(base, distance_matrix,
word2)
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 451 and event.pos[0] < 549 and \
event.pos[1] > 121 and event.pos[1] < 219 and learn == 1 :
base.append(word)
label.append(4)
distance_matrix = update_matrix(base, distance_matrix, word)
if (word2 != None):
base.append(word2)
label.append(4)
distance_matrix = update_matrix(base, distance_matrix,
word2)
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 11 and event.pos[0] < 109 and \
event.pos[1] > 231 and event.pos[1] < 329 and learn == 1 :
base.append(word)
label.append(5)
distance_matrix = update_matrix(base, distance_matrix, word)
if (word2 != None):
base.append(word2)
label.append(5)
distance_matrix = update_matrix(base, distance_matrix,
word2)
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 121 and event.pos[0] < 219 and \
event.pos[1] > 231 and event.pos[1] < 329 and learn == 1 :
base.append(word)
label.append(6)
distance_matrix = update_matrix(base, distance_matrix, word)
if (word2 != None):
base.append(word2)
label.append(6)
distance_matrix = update_matrix(base, distance_matrix,
word2)
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 231 and event.pos[0] < 329 and \
event.pos[1] > 231 and event.pos[1] < 329 and learn == 1 :
base.append(word)
label.append(7)
distance_matrix = update_matrix(base, distance_matrix, word)
if (word2 != None):
base.append(word2)
label.append(7)
distance_matrix = update_matrix(base, distance_matrix,
word2)
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 341 and event.pos[0] < 439 and \
event.pos[1] > 231 and event.pos[1] < 329 and learn == 1 :
base.append(word)
label.append(8)
distance_matrix = update_matrix(base, distance_matrix, word)
if (word2 != None):
base.append(word2)
label.append(8)
distance_matrix = update_matrix(base, distance_matrix,
word2)
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
if event.type == MOUSEBUTTONDOWN and event.button == 1 and \
event.pos[0] > 451 and event.pos[0] < 549 and \
event.pos[1] > 231 and event.pos[1] < 329 and learn == 1 :
base.append(word)
label.append(9)
distance_matrix = update_matrix(base, distance_matrix, word)
if (word2 != None):
base.append(word2)
label.append(9)
distance_matrix = update_matrix(base, distance_matrix,
word2)
learn = 0
initPicture(picture)
picture.save("temp.png")
draw = pygame.image.load("temp.png").convert()
window.blit(draw, (10, 10))
window.blit(black, (340, 10))
pygame.display.flip()
async def operatorstatscreate(self, data, index, profile):
font_file = f"{bundled_data_path(self)}/fonts/RobotoRegular.ttf"
bold_file = f"{bundled_data_path(self)}/fonts/RobotoBold.ttf"
name_fnt = ImageFont.truetype(font_file, 42, encoding="utf-8")
header_fnt = ImageFont.truetype(bold_file, 42, encoding="utf-8")
operator = random.choice(self.bgs)
opdata = data.operators[index]
bg_image = Image.open(
f"{bundled_data_path(self)}/bg/{operator}.jpg").convert("RGBA")
profile_image = Image
profile_avatar = BytesIO()
profile_url = data.avatar_url_256
async with self.session.get(profile_url) as r:
profile_img = await r.content.read()
profile_avatar = BytesIO(profile_img)
profile_image = Image.open(profile_avatar).convert("RGBA")
# set canvas
bg_color = (255, 255, 255, 0)
result = Image.new("RGBA", (1600, 1080), bg_color)
process = Image.new("RGBA", (1600, 1080), bg_color)
# draw
draw = ImageDraw.Draw(process)
# puts in background
bg_image = bg_image.resize((1920, 1090), Image.ANTIALIAS)
bg_image = bg_image.crop((0, 0, 1920, 1080))
result.paste(bg_image, (0, 0))
aviholder = self.add_corners(
Image.new("RGBA", (266, 266), (255, 255, 255, 255)), 10)
process.paste(aviholder, (1295, 15), aviholder)
process.paste(profile_image, (1300, 20))
# op badge
url = opdata["badge_image"]
process.paste(aviholder, (995, 15), aviholder)
im = Image.open(BytesIO(await self.getimg(url)))
im = im.resize((256, 256), Image.ANTIALIAS)
process.paste(im, (1000, 20))
# data
# draw.text((440, 40), "Name: {}".format(self._truncate_text(data.username, 18)), fill=(255, 255, 255, 255), font=header_fnt)
draw.text(
(
self._center(
440,
1000,
"Name: {}".format(self._truncate_text(data.username, 18)),
header_fnt,
),
40,
),
"Name: {}".format(self._truncate_text(data.username, 18)),
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(
self._center(440, 1000, "Platform: {}".format(
str(data.platform).title()), header_fnt),
90,
),
"Platform: {}".format(str(data.platform).title()),
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(self._center(440, 1000, f"{opdata['name']} Statistics",
header_fnt), 140),
f"{opdata['name']} Statistics",
fill=(255, 255, 255, 255),
font=header_fnt,
)
draw.text(
(520, 320),
"Games Played: {}".format(opdata["wins"] + opdata["losses"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text((520, 380),
"Wins: {}".format(opdata["wins"]),
fill=(255, 255, 255, 255),
font=name_fnt)
draw.text(
(920, 380),
"Losses: {}".format(opdata["losses"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
try:
wlr = round(opdata["wins"] / (opdata["wins"] + opdata["losses"]),
2)
except ZeroDivisionError:
wlr = 0
draw.text(
(520, 440),
"Total W/LR: {}%".format(wlr * 100),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(520, 520),
"Kills: {}".format(opdata["kills"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text(
(920, 520),
"Deaths: {}".format(opdata["deaths"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
draw.text((520, 580),
"KDR: {}".format(opdata["kd"]),
fill=(255, 255, 255, 255),
font=name_fnt)
draw.text(
(520, 640),
"Headshots: {}".format(opdata["headshots"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
y = 760
for ability in opdata["abilities"]:
draw.text(
(520, y),
"{}: {}".format(ability["ability"], ability["value"]),
fill=(255, 255, 255, 255),
font=name_fnt,
)
y += 80
m, _ = divmod(opdata["playtime"], 60)
h, m = divmod(m, 60)
d, h = divmod(h, 24)
draw.text(
(520, 700),
f"Playtime: {d:d}d {h:d}h {m:02d}m",
fill=(255, 255, 255, 255),
font=name_fnt,
)
result = Image.alpha_composite(result, process)
result = result.crop((500, 0, 1600, 1080))
file = BytesIO()
result.save(file, "png")
file.name = f"operator-{data.username}.png"
file.seek(0)
image = discord.File(file)
return image
# http://www.pythonchallenge.com/pc/hex/copper.html
# Page source says 'or maybe white.gif would be more bright'
from PIL import Image, ImageDraw
# white.gif downloaded from http://www.pythonchallenge.com/pc/hex/white.gif
# white.gif is almost all black, with 1 pixel with (8, 8, 8)
# Only 1 pixel has different color
# The pixel is moving in the white.gif
# Need to emulate the joystick
img = Image.open('./resource/white.gif')
# Variable to store new image
new = Image.new('RGB', (500, 200))
draw = ImageDraw.Draw(new)
# Set first point to start at the edge of image
cx, cy = 0, 100
# Read all frames in white.gif
for frame in range(img.n_frames):
# Get frame
img.seek(frame)
# getbbox() get black box boundary
left, upper, right, lower = img.getbbox()
# Calculates the position of the non-black pixel
# Set movement direction accordingly
dx = left - 100
dy = upper - 100
# If black pixel is reset to original position
# Move cursor 50 pixels to the right for more space
if dx == dy == 0:
def round_corner(self, radius):
"""Draw a round corner"""
corner = Image.new("L", (radius, radius), 0)
draw = ImageDraw.Draw(corner)
draw.pieslice((0, 0, radius * 2, radius * 2), 180, 270, fill=255)
return corner
def show_image_analysis(image_path, analysis):
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw
import numpy as np
# Display the image
fig = plt.figure(figsize=(16, 8))
a = fig.add_subplot(1, 2, 1)
img = Image.open(image_path)
# Get the caption
caption_text = ''
if (len(analysis.description.captions) == 0):
caption_text = 'No caption detected'
else:
for caption in analysis.description.captions:
caption_text = caption_text + " '{}'\n(Confidence: {:.2f}%)".format(
caption.text, caption.confidence * 100)
plt.title(caption_text)
# Get objects
if analysis.objects:
# Draw a rectangle around each object
for object in analysis.objects:
r = object.rectangle
bounding_box = ((r.x, r.y), (r.x + r.w, r.y + r.h))
draw = ImageDraw.Draw(img)
draw.rectangle(bounding_box, outline='magenta', width=5)
plt.annotate(object.object_property, (r.x, r.y),
backgroundcolor='magenta')
# Get faces
if analysis.faces:
# Draw a rectangle around each face
for face in analysis.faces:
r = face.face_rectangle
bounding_box = ((r.left, r.top), (r.left + r.width,
r.top + r.height))
draw = ImageDraw.Draw(img)
draw.rectangle(bounding_box, outline='lightgreen', width=5)
annotation = 'Person aged approxilately {}'.format(face.age)
plt.annotate(annotation, (r.left, r.top),
backgroundcolor='lightgreen')
plt.axis('off')
plt.imshow(img)
# Add a second plot for addition details
a = fig.add_subplot(1, 2, 2)
# Get ratings
ratings = 'Ratings:\n - Adult: {}\n - Racy: {}\n - Gore: {}'.format(
analysis.adult.is_adult_content,
analysis.adult.is_racy_content,
analysis.adult.is_gory_content,
)
# Get tags
tags = 'Tags:'
for tag in analysis.tags:
tags = tags + '\n - {}'.format(tag.name)
# Print details
details = '{}\n\n{}'.format(ratings, tags)
a.text(0, 0.4, details, fontsize=12)
plt.axis('off')
plt.show()
def draw_status_pie(status_totals: Counter, avatar_fp: Optional[BytesIO], *, show_totals: bool = True) -> BytesIO:
image, draw = base_image()
# Make pie max size if no totals
if not show_totals:
pie_size = IMAGE_SIZE
pie_offset = (0,) * 2
else:
pie_size = int(IMAGE_SIZE * 0.66)
pie_offset = (0, (IMAGE_SIZE - pie_size) // 2)
# Draw status pie
pie_0 = add((0,) * 2, pie_offset)
pie_1 = add((pie_size,) * 2, pie_offset)
degrees = 270.0
for status, percentage in status_totals.most_common():
start = degrees
degrees += 360 * percentage
draw.pieslice((pie_0, pie_1), start, degrees, fill=COLOURS[status])
if avatar_fp is not None:
# Load avatar image
avatar = Image.open(avatar_fp)
if avatar.mode != 'RGBA':
avatar = avatar.convert('RGBA')
avatar = avatar.resize((int(pie_size // 1.5),) * 2, resample=Image.LANCZOS)
# Apply circular mask to image
_, _, _, alpha = avatar.split()
if alpha.mode != 'L':
alpha = alpha.convert('L')
mask = Image.new('L', avatar.size, 0)
draw = ImageDraw.Draw(mask)
draw.ellipse((0, 0) + avatar.size, fill=255)
mask = ImageChops.darker(mask, alpha)
avatar.putalpha(mask)
# Overlay avatar
image.paste(avatar, add((pie_size // 6,) * 2, pie_offset), avatar)
# Add status percentages
if show_totals:
draw = ImageDraw.Draw(image)
font = ImageFont.truetype('res/roboto-bold.ttf', IMAGE_SIZE // 20)
x_offset = IMAGE_SIZE // 4 * 3
y_offset = IMAGE_SIZE // 3
circle_size = (IMAGE_SIZE // 30,) * 2
for status, percentage in status_totals.most_common():
offset = (x_offset, y_offset)
draw.ellipse(offset + add(offset, circle_size), fill=COLOURS[status])
draw.text((x_offset + IMAGE_SIZE // 20, y_offset - IMAGE_SIZE // 60), f'{percentage:.2%}', font=font, align='left', fill=WHITE)
y_offset += IMAGE_SIZE // 8
return as_bytes(resample(image))
from PIL import Image, ImageDraw
# Image size (pixels)
WIDTH = 36000
HEIGHT = 24000
# Plot window
RE_START = -2
RE_END = 1
IM_START = -1
IM_END = 1
palette = []
im = Image.new('RGB', (WIDTH, HEIGHT), (0, 0, 0))
draw = ImageDraw.Draw(im)
im = Image.new('RGB', (WIDTH, HEIGHT), (0, 0, 0))
draw = ImageDraw.Draw(im)
for x in range(0, WIDTH):
for y in range(0, HEIGHT):
# Convert pixel coordinate to complex number
c = complex(RE_START + (x / WIDTH) * (RE_END - RE_START),
IM_START + (y / HEIGHT) * (IM_END - IM_START))
# Compute the number of iterations
m = mandelbrot(c)
# The color depends on the number of iterations
color = 255 - int(m * 255 / MAX_ITER)
# Plot the point
draw.point([x, y], (color, color, color))
def draw_status_log(status_log: List[LogEntry], *, timezone: datetime.timezone =
datetime.timezone.utc, show_labels: bool = False, num_days: int = 30) -> BytesIO:
row_count = 1 + num_days + show_labels
image, draw = base_image(IMAGE_SIZE * row_count, 1)
# Set consts
day_width = IMAGE_SIZE / (60 * 60 * 24)
day_height = IMAGE_SIZE // row_count
now = datetime.datetime.now(timezone)
time_offset = now.utcoffset().total_seconds() # type: ignore
total_duration = 0.0
if show_labels:
time_offset += 60 * 60 * 24
# Draw status log entries
for status, _, timespan in status_log:
duration: float = timespan.total_seconds()
total_duration += duration
new_time_offset = time_offset + duration
start_x = round(time_offset * day_width)
end_x = round(new_time_offset * day_width)
draw.rectangle(((start_x, 0), (end_x, 1)), fill=COLOURS[status])
time_offset = new_time_offset
# Reshape Image
pixels = numpy.array(image)
# pixels = pixels[:, IMAGE_SIZE:]
pixels = pixels.reshape(row_count, IMAGE_SIZE, 4)
pixels = pixels.repeat(day_height, 0)
image = Image.fromarray(pixels, 'RGBA')
if show_labels:
overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
draw = ImageDraw.Draw(overlay)
# Set offsets based on font size
font = ImageFont.truetype('res/roboto-bold.ttf', IMAGE_SIZE // int(1.66 * num_days))
text_width, text_height = draw.textsize('\N{FULL BLOCK}' * 2, font=font)
height_offset = (day_height - text_height) // 2
x_offset = text_width
y_offset = day_height
# Add date labels
date = now - datetime.timedelta(seconds=total_duration)
for _ in range(int(total_duration // ONE_DAY) + 2): # 2 because of timezone offset woes
# if weekend draw signifier
if date.weekday() == 5:
draw.rectangle((0, y_offset, IMAGE_SIZE, y_offset + (2 * day_height)), fill=TRANSLUCENT)
# Add date
_, text_height = draw.textsize(date.strftime('%b. %d'), font=font)
draw.text((x_offset, y_offset + height_offset), date.strftime('%b. %d'), font=font, align='left', fill=WHITE)
y_offset += day_height
date += datetime.timedelta(days=1)
# Add timezone label
draw.text((x_offset, height_offset), str(timezone), font=font, align='left', fill='WHITE')
# Add hour lines
for i in range(1, 24):
x_offset = ONE_HOUR * i
colour = WHITE if not i % 6 else OPAQUE
draw.line((x_offset, day_height, x_offset, IMAGE_SIZE), fill=colour, width=DOWNSAMPLE * 4)
image = Image.alpha_composite(image, overlay)
draw = ImageDraw.Draw(image)
# Add time labels
time = start_of_day(now)
for x_offset in (ONE_HOUR * 6, ONE_HOUR * 12, ONE_HOUR * 18):
time += datetime.timedelta(hours=6)
text_width, _ = draw.textsize(time.strftime('%H:00'), font=font)
draw.text((x_offset - (text_width // 2), height_offset), time.strftime('%H:00'), font=font, align='left', fill=WHITE)
return as_bytes(resample(image))
def SavePILText(strAPODFileName):
#use isfile logic to only create new ones, True to recreate them all
if not os.path.isfile(strAPODPicsWithText + strAPODFileName): #True:
print "Building Image With Text"
#strAPODDataFolder + strAPODFileName.replace('.jpg', '_Info.txt')
strInfo = ""
strTitle = ""
strContents = ""
#if strAPODFileName == "ison_encke_hi1_srem_a.jpg":
# print "stop here"
#if strAPODFileName == "pileus_lowenstein_4320.jpg":
# print "stop here"
try:
with open(
strAPODDataFolder +
strAPODFileName.replace('.jpg', '_Info.txt'),
'r') as content_file:
strInfo = content_file.read()
except Exception as e:
print str(e)
try:
with open(
strAPODDataFolder +
strAPODFileName.replace('.jpg', '_Title.txt'),
'r') as content_file:
strTitle = content_file.read()
except Exception as e:
print str(e)
if strTitle.strip() <> "":
strContents = strTitle.strip()
if strInfo.strip() <> "":
if strContents <> "":
strContents = strContents + ': ' + strInfo.strip()
else:
strContents = strInfo.strip()
else:
strContents = strInfo.strip()
try:
TEXTCOLOR = (0, 0, 0)
IMGCOLOR = (0, 0, 0, 255)
OPCOLOR = (150, 150, 150, 255)
X = 10
Y = 3
fontsize = 24
#how to I manage this on linux?
font = ImageFont.truetype('arial.ttf', fontsize)
im = PILImage.new(
'RGBA', (1920, 1080),
IMGCOLOR) # PILImage.open('G:\\blank.png') #.convert('RGB')
imgWidth, imgHeight = im.size
draw = ImageDraw.Draw(im)
sngHeight = imgHeight * 1.0
sngWidth = imgWidth * 1.0
bImageToTop = True
#INSERT THE ORIGINAL INTO THE CANVAS
strSource = strAPODPicturesFolder + strAPODFileName
if os.path.isfile(strSource):
imIN = PILImage.open(strSource)
imgWidthIN, imgHeightIN = imIN.size
if strAPODPicsVLF <> "":
#print "checking for very large image"
#convert very large images on the Pi down to QHD
# if dimentions larger than 3840×2160 scale down to that size first
sngWidthQHD = 3840.0
sngHeightQHD = 2160.0
if imgWidthIN > sngWidthQHD or imgHeightIN > sngHeightQHD:
#convert titan5km_huygens_big.jpg -resize 3840x2160 test.jpg
#subprocess.call(["ls", "-l"])
print "converting VLF"
#strArgs = " '" + + "'"
#print "convert " + strArgs
#call(["ls", "-l"])
call([
"convert", strAPODPicturesFolder + strAPODFileName,
"-resize", "3840x2160",
strAPODPicsVLF + strAPODFileName
])
print "reopening VLF"
imIN = PILImage.open(strAPODPicsVLF + strAPODFileName)
imgWidthIN, imgHeightIN = imIN.size
bResized = False
if imgHeightIN > sngHeight:
bResized = True
sngChange = sngHeight / imgHeightIN
imgHeightIN = int(sngHeight)
imgWidthIN = int(imgWidthIN * sngChange)
if imgWidthIN > sngWidth:
bResized = True
sngChange = sngWidth / imgWidthIN
imgWidthIN = int(sngWidth)
imgHeightIN = int(imgHeightIN * sngChange)
szNew = imgWidthIN, imgHeightIN
if bResized:
imIN = imIN.resize(szNew, PILImage.ANTIALIAS)
#does image go to right or to top?
if ((imgWidthIN * 1.0) / imgHeightIN) > (
(imgWidth * 1.0) / imgHeight):
#print "Input Image More Landscape - print to top"
if imgHeightIN < (1080 / 2):
offset = ((imgWidth - imgWidthIN) / 2,
(1080 / 2) - imgHeightIN
) #(imgHeight-imgHeightIN)/2)
else:
offset = ((imgWidth - imgWidthIN) / 2, 0
) #(imgHeight-imgHeightIN)/2)
im.paste(imIN, offset)
else:
bImageToTop = False
#print "Input Image More Portrait - print to right"
offset = (min((1920 / 2), (imgWidth - imgWidthIN)),
(imgHeight - imgHeightIN) / 2)
im.paste(imIN, offset)
#MANAGE THE TEXT OVERLAY
#decide what gets printed on what line
tmpLine = ""
strLine = ""
#print "Splitting Contents:" + strContents
words = strContents.split(' ')
#list of lines to print
strLines = []
bLines = False
if bImageToTop == True:
#print "printing image to top, text underneath"
for word in words:
#print "10"
if strLine == "":
#print "20"
strLine = word
else:
#print "30"
strLine = strLine + ' ' + word
#print "35"
strLine = strLine.strip()
#print "40: " + strLine
if strLine <> "":
#print "45"
intWidth, intHeight = draw.textsize(strLine, font=font)
#print "50"
#print 'Text Render Width = ' + str(intWidth) + ', Text Render Height = ' + str(intHeight)
if intWidth > (imgWidth - 20):
#print "Drawing Text loop: " + tmpLine
#draw.text((X, Y), tmpLine, font=font, fill=TEXTCOLOR)
strLines.append(tmpLine)
#print "100 %s, %s", X, Y
#Y += 33 # intHeight * 1.1
#print "110"
strLine = word
tmpLine = strLine
else:
tmpLine = strLine
#print "Drawing Text"
#print "tmpLine = " + tmpLine
if not tmpLine == "":
#draw.text((X, Y), tmpLine, font=font, fill=TEXTCOLOR)
strLines.append(tmpLine)
else:
print "Nothing To Print"
if True: #False:
#Print On The Opacity Layer behind the text
#print "making opacity background"
intLineEstimate = len(strLines)
intRectHeight = intLineEstimate * 33 # 250 # calced by length of text
intGradHeight = 40
sngGradVal = 0
intGradMaxOpacity = 210
intX = 1920
intY = 1080
bgimg = PILImage.new('RGBA', (intX, intY), OPCOLOR)
gradient = PILImage.new('L', (1, intY))
for y in range(intY):
if (y < (intY - (intRectHeight + intGradHeight))):
gradient.putpixel((0, y), 0)
elif (y < (intY - intRectHeight)):
gradient.putpixel((0, y), int(sngGradVal))
sngGradVal = sngGradVal + (
(intGradMaxOpacity * 1.0) / intGradHeight)
else:
gradient.putpixel((0, y), intGradMaxOpacity)
# resize the gradient to the size of im...
alpha = gradient.resize(bgimg.size)
# put alpha in the alpha band of im...
bgimg.putalpha(alpha)
#draw bgimg onto im
im.paste(bgimg, (0, 0), bgimg)
#print on the text
bLines = False
Y = (imgHeight - 10) - (len(strLines) * 33)
for strLine in strLines:
if strLine <> "":
bLines = True
draw.text((X, Y), strLine, font=font, fill=TEXTCOLOR)
Y += 33
#print strLine
else:
intMaxDesiredLineWidth = max(
20,
min((1920 / 2), (1920 - imgWidthIN)) - (X * 2)) - 10
#print "print image right, text to the left"
if True:
#print "new logic"
intMaxLines = 32
intLinesUsed = 33
intTotalWidth, intTotalHeight = draw.textsize(strContents,
font=font)
while intLinesUsed > intMaxLines:
#print "logic here"
#run draw logic to find number of current lines
intMaxDesiredLineWidth += 5
strLines[:] = []
#with PILImage.new('RGBA',(1920, 1080), IMGCOLOR) as imT:
if True:
imT = PILImage.new('RGBA', (1920, 1080), IMGCOLOR)
imgWidthT, imgHeightT = imT.size
drawT = ImageDraw.Draw(imT)
for word in words:
if strLine == "":
strLine = word
else:
strLine = strLine + ' ' + word
strLine = strLine.strip()
if strLine <> "":
intWidth, intHeight = drawT.textsize(
strLine, font=font)
if intWidth > (intMaxDesiredLineWidth -
(X * 2)):
strLines.append(tmpLine)
strLine = word
tmpLine = strLine
else:
tmpLine = strLine
if not tmpLine == "":
strLines.append(tmpLine)
else:
print "Nothing To Print"
intLinesUsed = len(strLines)
strLine = ""
tmpLine = ""
else:
print "old logic"
intTotalWidth, intTotalHeight = draw.textsize(strContents,
font=font)
intTotalPossibleLines = int(1080 / 33.0) - 3
intMaxDesiredLineWidth = max(
30,
min((1920 / 2), (1920 - imgWidthIN)) - (X * 2))
intCurrentLines = intTotalWidth / (intMaxDesiredLineWidth -
10)
intCurrentLines = (intTotalWidth *
1.15) / (intMaxDesiredLineWidth)
#print "TotalPossibleLines: " + str(intTotalPossibleLines)
#print "TotalWidth: " + str(intTotalWidth)
#guess width per line
# if lines * maxdesiredlinewidth > inttotalwidth make maxdesiredlinewidth longer
while (intCurrentLines > intTotalPossibleLines) and (
intMaxDesiredLineWidth < 1920):
#print "recalc intMaxDesiredLineWidth"
intMaxDesiredLineWidth += 10
#intCurrentLines = (intTotalWidth * 1.1) / (intMaxDesiredLineWidth) # - ((X * 2) + 30))
intCurrentLines = (intTotalWidth * 1.15) / (
intMaxDesiredLineWidth) # - 10)
if len(strLines) == intMaxLines:
print "stop here"
strLines[:] = []
for word in words:
#print "10"
if strLine == "":
#print "20"
strLine = word
else:
#print "30"
strLine = strLine + ' ' + word
#print "35"
strLine = strLine.strip()
#print "40: " + strLine
if strLine <> "":
#print "45"
intWidth, intHeight = draw.textsize(strLine, font=font)
#print "50"
#print 'Text Render Width = ' + str(intWidth) + ', Text Render Height = ' + str(intHeight)
if intWidth > (intMaxDesiredLineWidth - (X * 2)):
#print "Drawing Text loop: " + tmpLine
#draw.text((X, Y), tmpLine, font=font, fill=TEXTCOLOR)
strLines.append(tmpLine)
#print "100 %s, %s", X, Y
#Y += 33 # intHeight * 1.1
#print "110"
#print "120"
strLine = word
tmpLine = strLine
else:
tmpLine = strLine
#print "Drawing Text"
#print "tmpLine = " + tmpLine
if not tmpLine == "":
#draw.text((X, Y), tmpLine, font=font, fill=TEXTCOLOR)
strLines.append(tmpLine)
else:
print "Nothing To Print"
if True: #False:
#Print On The Opacity Layer behind the text
#print "making opacity background"
#intLineEstimate = len(strLines)
intRectWidth = intMaxDesiredLineWidth # intLineEstimate * 33 # 250 # calced by length of text
intGradWidth = 40
intGradMaxOpacity = 210
sngGradVal = intGradMaxOpacity
intX = 1920
intY = 1080
bgimg = PILImage.new('RGBA', (intX, intY), OPCOLOR)
gradient = PILImage.new('L', (intX, 1))
#make veritical into horizontal
for x in range(intX):
if (x < (intRectWidth)): # + intGradWidth)):
gradient.putpixel((x, 0), intGradMaxOpacity)
#elif (x < (intRectWidth + intGradWidth)):
# gradient.putpixel((x,0),int(sngGradVal))
# sngGradVal = sngGradVal - ((intGradMaxOpacity * 1.0) / intGradWidth)
else:
gradient.putpixel((x, 0), 0)
# resize the gradient to the size of im...
alpha = gradient.resize(bgimg.size)
# put alpha in the alpha band of im...
bgimg.putalpha(alpha)
#draw bgimg onto im
im.paste(bgimg, (0, 0), bgimg)
#print on the text
Y = max(10, ((imgHeight - 10) - (len(strLines) * 33)) / 2)
if Y < 50:
print "Y = " + str(Y)
for strLine in strLines:
if strLine <> "":
bLines = True
draw.text((X, Y), strLine, font=font, fill=TEXTCOLOR)
Y += 33
#print strLine
if bLines == True:
print "Saving File " + strAPODPicsWithText + strAPODFileName
im.save(strAPODPicsWithText + strAPODFileName)
print "Done Building Cache Image With Text"
except Exception as e:
print str(e)
def base_image(width: int = IMAGE_SIZE, height: int = IMAGE_SIZE) -> Tuple[Image.Image, ImageDraw.ImageDraw]:
image = Image.new('RGBA', (width, height))
draw = ImageDraw.Draw(image)
return image, draw
def save_qtype(qtype_list, save_filename, mode):
if mode == 'val':
savepath = os.path.join('./eval', save_filename)
# TODO
img_pre = '/tempspace/zwang6/VQA/Images/mscoco/val2014'
elif mode == 'test-dev':
savepath = os.path.join('./test-dev', save_filename)
# TODO
img_pre = '/tempspace/zwang6/VQA/Images/mscoco/test2015'
elif mode == 'test':
savepath = os.path.join('./test', save_filename)
# TODO
img_pre = '/tempspace/zwang6/VQA/Images/mscoco/test2015'
else:
raise Exception('Unsupported mode')
if os.path.exists(savepath): shutil.rmtree(savepath)
if not os.path.exists(savepath): os.makedirs(savepath)
for qt in qtype_list:
count = 0
for t_question in stat_list:
#print count, t_question
if count < 40/len(qtype_list):
t_question_list = t_question['q_list']
saveflag = False
#print 'debug****************************'
#print qt
#print t_question_list
#print t_question_list[0] == qt[0]
#print t_question_list[1] == qt[1]
if t_question_list[0] == qt[0] and t_question_list[1] == qt[1]:
saveflag = True
else:
saveflag = False
if saveflag == True:
t_iid = t_question['iid']
if mode == 'val':
t_img = Image.open(os.path.join(img_pre, \
'COCO_val2014_' + str(t_iid).zfill(12) + '.jpg'))
elif mode == 'test-dev' or 'test':
t_img = Image.open(os.path.join(img_pre, \
'COCO_test2015_' + str(t_iid).zfill(12) + '.jpg'))
# for caption
#print t_iid
#annIds = caps.getAnnIds(t_iid)
#anns = caps.loadAnns(annIds)
#cap_list = [ann['caption'] for ann in anns]
ans_list = t_question['ans_list']
draw = ImageDraw.Draw(t_img)
for i in range(len(ans_list)):
try:
draw.text((10,10*i), str(ans_list[i]))
except:
pass
ans = t_question['answer']
pred = t_question['pred']
if ans == -1:
pre = ''
elif ans == pred:
pre = 'correct '
else:
pre = 'failure '
#print ' aaa ', ans, pred
ans = re.sub( '/', ' ', str(ans))
pred = re.sub( '/', ' ', str(pred))
img_title = pre + str(' '.join(t_question_list)) + '. a_' + \
str(ans) + ' p_' + str(pred) + '.png'
count += 1
write_log(os.path.join(savepath,img_title), 'visualize_log.txt')
t_img.save(os.path.join(savepath,img_title))
from PIL import Image, ImageDraw, ImageFont
import matplotlib.font_manager
image = Image.new('RGB', (640, 360), (255, 255, 255))
drawer = ImageDraw.Draw(image)
font_list = matplotlib.font_manager.fontManager.ttflist
def size_to_line_height(fontpath, size):
font_sample = ImageFont.truetype(fontpath, size)
sample_size = drawer.textsize("Hello python language", font=font_sample)
offset = font_sample.getoffset("Hello python language")
print(sample_size, offset)
return sample_size[1] + offset[1]
def drawText(text, x, y, font):
size = drawer.textsize(text, font=font)
offset = font.getoffset(text)
print("size", size)
print("offset", offset)
print("size+offest", size[0] + offset[0], size[1] + offset[1])
drawer.text((x, y), text, fill='black', font=font)
drawer.rectangle(
(x, y, x + size[0] + offset[0], y + size[1] + offset[1]), outline='black')
# drawing text
font_size = size_to_line_height('/Library/Fonts/ヒラギノ角ゴ Pro W6.otf', 40)
def text_mode(cam):
text = ""
word = ""
count_same_frame = 0
while True:
img = cam.read()[1]
img = cv2.resize(img, (640, 480))
img, contours, thresh = get_img_contour_thresh(img)
old_text = text
if len(contours) > 0:
contour = max(contours, key = cv2.contourArea)
if cv2.contourArea(contour) > 10000:
text = get_pred_from_contour(contour, thresh)
if old_text == text:
count_same_frame += 1
else:
count_same_frame = 0
if count_same_frame > 20:
word = word + text
count_same_frame = 0
elif cv2.contourArea(contour) < 1000:
text = ""
blackboard = np.zeros((480, 640, 3), dtype=np.uint8)
b,g,r,a = 255,255,255,0
img_pil = Image.fromarray(blackboard)
draw = ImageDraw.Draw(img_pil)
draw.text((180,50), " ", font =ImageFont.truetype('malgun.ttf', 36), fill=(255, 0,0, 0))
draw.text((30, 100), "Predicted text-" + text, font = ImageFont.truetype('malgun.ttf', 36), fill = (255, 255, 0, 0))
line_break_idx = word.rfind("\n") #Line breaks on blackboard
if line_break_idx==-1:
if len(word) > 13:
word += "\n"
else:
if len(word)-line_break_idx > 14:
word += "\n"
y0 = 150
dy = 50
for i, line in enumerate(word.split('\n')):
y = y0 + i*dy
line = join_jamos(line)
draw.text((30, y), line, font=ImageFont.truetype('malgun.ttf', 36), fill=(255, 255, 255, 0))
blackboard = np.array(img_pil)
cv2.putText(blackboard, " ", (450, 440), cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 127, 0))
cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,0), 2)
res = np.hstack((img, blackboard))
cv2.imshow("Recognizing gesture", res)
cv2.imshow("thresh", thresh)
keypress = cv2.waitKey(1)
if keypress == ord('q') or keypress == ord('c'):
break
if keypress == 13:
word = word + text
count_same_frame = 0
if keypress == 8:
word = word[:-1]
if keypress == ord('c'):
return 2
else:
return 0
from PIL import Image, ImageDraw, ImageFont
import math, os, sys, random
from HelperFunctions import *
# Parameters
img_name = 'Armenian'
font_size = (18, 24, 32)
font_color = (0, 0, 0)
font_path = os.path.join(os.getcwd(), 'DejaVuSans.ttf')
folder_path = os.path.join(os.getcwd(), 'Unicode-Values', 'Armenian')
# Create base image and draw object
base_image = Image.new('RGB', (500, 500), (255, 255, 255))
base_draw = ImageDraw.Draw(base_image)
# Load Font and get language data
dejavu_font_s = ImageFont.truetype(font_path, font_size[0])
dejavu_font_m = ImageFont.truetype(font_path, font_size[1])
dejavu_font_l = ImageFont.truetype(font_path, font_size[2])
Armenian = readPath(folder_path)
displayLangObject(Armenian)
base_draw.text((250, 25),
img_name,
font=dejavu_font_l,
fill=font_color,
align='center',
anchor='mm')
x, y = 10, 30
for key in Armenian:
# to chromedriver in your computer
driver = webdriver.Chrome('chromedriver',chrome_options=options)
driver.get("https://web.whatsapp.com/")
wait = WebDriverWait(driver, 100)
print("Whatsapp connected")
prev="1"
while True:
element1 = driver.find_elements_by_xpath('//*[@id="main"]/header/div[2]/div[1]')
if(len(element1)>0):
target=element1[0].text
a=target.split(' ')
reciever=a[0]
image = Image.open('w.png')
draw = ImageDraw.Draw(image)
font = ImageFont.truetype('Charm-Regular', size=32)
font2= ImageFont.truetype('Acme-Regular', size=26)
(x, y) = (50, 50)
message = "Wishing " + reciever.upper() + " and Family "
color = 'rgb(255,58,249)'
draw.text((x, y), message, fill=color, font=font)
message="a very Happy New Year "
(x, y) = (60, 87)
draw.text((x, y), message, fill=color, font=font)
(x,y) =(50,132)
message="May Light always surround you"
draw.text((x, y), message, fill=color, font=font)
(x, y) = (500,300)
name = ' - - - Tanay Mishra'
color = 'rgb(18, 20, 234)'
def show_frame(self, set_number, seq_number, frame_number):
self.load_annotations() # Will be needed
# Check the frame was prepared
if not self.is_frame_prepared(set_number, seq_number, frame_number):
self.prepare_frame(set_number, seq_number, frame_number)
if CaltechDataset.USE_CROPPING:
image = Image.open(self.dataset_location +
'/images-cropped/set{:02d}/V{:03d}.seq/{}.jpg'.
format(set_number, seq_number, frame_number))
transform = np.load(
self.dataset_location +
'/images-cropped/set{:02d}/V{:03d}.seq/{}.transform.npy'.
format(set_number, seq_number, frame_number))
else:
image = Image.open(self.dataset_location +
'/images/set{:02d}/V{:03d}.seq/{}.jpg'.format(
set_number, seq_number, frame_number))
dr = ImageDraw.Draw(image)
# Retrieve objects for that frame in annotations
try:
objects = self.annotations['set{:02d}'.format(set_number)][
'V{:03d}'.format(seq_number)]['frames']['{}'.format(
frame_number)]
except KeyError as e:
objects = None # Simply no objects for that frame
if objects:
for o in objects:
pos = (o['pos'][1], o['pos'][0], o['pos'][3], o['pos'][2]
) # Convert to (y, x, h, w)
if CaltechDataset.USE_CROPPING:
pos = transform_cropped_pos(pos, transform)
if o['lbl'] in ['person']:
good = True
# Remove objects with very small width (are they errors in labeling?!)
if pos[3] < CaltechDataset.MINIMUM_WIDTH:
good = False
if o['occl'] == 1:
if type(o['posv']) == int:
good = False
else:
visible_pos = (o['posv'][1], o['posv'][0],
o['posv'][3], o['posv'][2]
) # Convert to (y, x, h, w)
if CaltechDataset.USE_CROPPING:
visible_pos = transform_cropped_pos(
visible_pos, transform)
if visible_pos[2] * visible_pos[
3] < CaltechDataset.MINIMUM_VISIBLE_RATIO * pos[
2] * pos[3]:
good = False
pos = visible_pos
if good:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='blue')
else:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='pink')
else:
dr.rectangle(
(pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='black')
clas_negative = np.load(
self.dataset_location +
'/prepared/set{:02d}/V{:03d}.seq/{}.negative.npy'.format(
set_number, seq_number, frame_number))
for i in range(clas_negative.shape[1]):
y, x, anchor_id = clas_negative[:, i]
dr.rectangle(
(CaltechDataset.OUTPUT_CELL_SIZE * x,
CaltechDataset.OUTPUT_CELL_SIZE * y,
CaltechDataset.OUTPUT_CELL_SIZE *
(x + 1) - 1, CaltechDataset.OUTPUT_CELL_SIZE * (y + 1) - 1),
outline='red')
clas_positive = np.load(
self.dataset_location +
'/prepared/set{:02d}/V{:03d}.seq/{}.positive.npy'.format(
set_number, seq_number, frame_number))
for i in range(clas_positive.shape[1]):
y, x, anchor_id = clas_positive[:, i]
pos = self.get_anchor_at(anchor_id, y, x)
dr.rectangle(
(CaltechDataset.OUTPUT_CELL_SIZE * x,
CaltechDataset.OUTPUT_CELL_SIZE * y,
CaltechDataset.OUTPUT_CELL_SIZE *
(x + 1) - 1, CaltechDataset.OUTPUT_CELL_SIZE * (y + 1) - 1),
outline='green')
dr.rectangle((pos[1], pos[0], pos[1] + pos[3], pos[0] + pos[2]),
outline='green')
image.show()