def save(self, *args, **kwargs):
super(Curso, self).save(*args, **kwargs)
if not self.id and not self.imagen:
return
image.resize(image.THUMB, self.imagen)
def save(self, *args, **kwargs):
super(Conferencia, self).save(*args, **kwargs)
if not self.id and not self.imagen:
return
image.resize(image.THUMB, self.imagen)
def save(self, *args, **kwargs):
super(VideoInvitado, self).save(*args, **kwargs)
if not self.id and not self.imagen:
return
image.resize((100, 100), self.imagen)
def clicked(letter):
isLetterFound = False
global times
for index,let in enumerate(word): #looping to check if the letter exist
if letter == let:
arry_display[index].config(text = letter)
hidden_word[index] = letter
isLetterFound = True
# If letter was not found, then update the image and increment by one his turn (times)
if isLetterFound == False:
times +=1
image = Image.open(image_src[times])
image = image.resize((150, 150), Image.ADAPTIVE)
imageW = ImageTk.PhotoImage(image)
panel.configure(image = imageW)
panel.image = imageW
if checkIfWin(hidden_word):
messagebox.showinfo("Hang man", "Congratulations You win!")
window.destroy()
if times == 6:
image = Image.open(image_src[7])
image = image.resize((150, 150), Image.ADAPTIVE)
imageW = ImageTk.PhotoImage(image)
panel.configure(image=imageW)
panel.image = imageW
messagebox.showinfo("Hang man", "Too bad, you lost!")
def save(self, *args, **kwargs): super(Curso, self).save(*args, **kwargs) return if not self.id and not self.imagen: return image.resize((666, 430), self.imagen)
def save(self, *args, **kwargs):
super(Curso, self).save(*args, **kwargs)
return
if not self.id and not self.imagen: return
image.resize((666, 430), self.imagen)
def detect_from_folder(folder, face_cascade):
images = fetch_images(folder + "/", "jpg")
for image in images:
while image.shape[1] > 1024 or image.shape[0] > 786:
image.resize(
(int(image.shape[1] * 0.9), int(image.shape[0] * 0.9)))
image.make_greyscale()
image.search_faces(face_cascade)
image.detect_faces()
if image.display(1000) == False:
break
cv2.destroyAllWindows()
def run(self,
img,
iterations,
step_size,
octave,
octave_scale,
max_loss=None):
"""
Runs CreepDream
Runs gradient ascent. First it upscales provided image to particular
scale then it reinjects the detail that was lost at upscaling time back.
Parameters
----------
img : ndarray
Image as a numpy array
iterations : int
Number of iterations
step_size : int
Gradient ascent step size
octave : int
Number of scales at which to run gradient ascent
octave_scale : float
Size ratio between shapes
max_loss : float
Gradient descent max loss
Returns
-------
img : ndarray
Deep dream modified image
"""
# create image shapes of different sizes
shapes = self._shapes(img, octave, octave_scale)
# copy original image - we want to avoid modifying it
orig_img = np.copy(img)
shrunk_orig_img = resize(img, shapes[0])
for shape in shapes:
img = resize(img, shape)
img = self._gradient_ascent(img,
iterations=iterations,
step_size=step_size,
max_loss=max_loss)
# upscale shrunk image: not in first iteration this will do nothing
upscaled_shrunk_orig_img = resize(shrunk_orig_img, shape)
# upscale original image: from the original size back to original
same_size_orig_img = resize(orig_img, shape)
lost_detail = same_size_orig_img - upscaled_shrunk_orig_img
img += lost_detail
shrunk_orig_img = resize(orig_img, shape)
return img
def uploadNoInput(req):
data.clearDatabase()
lastID = data.writeDatabase("auto", "*****@*****.**", "auto", "auto")
last = data.getEntryFromID(lastID)
obj = ""
fileitem = req.form['file']
if fileitem.filename:
# strip leading path from file name to avoid directory traversal attacks
# also prepend DB ID so filenames are unique
fname = str(last['_id'])+"_"+os.path.basename(fileitem.filename)
obj = data.updateFilenameFromID(last['_id'],fname)
# build absolute path to files directory
dir_path = os.path.join(os.path.dirname(req.filename), 'files')
open(os.path.join(dir_path, fname), 'wb').write(fileitem.file.read())
#obj = analyze.histogram(last['_id'])
obj = image.resize(last['_id'])
obj = analyze.histogram(last['_id'], 4)
message = "<HTML>"
message += 'The file "%s" was uploaded, resized, and analyzed successfully' % fname
#message += 'go <a href="#" onClick = "history.back()"> back </a>
message += '\n<img src="'+cfg.imageWebPath+fname+'">'
f = open("/srv/www/htdocs/lighting/app/control.html",'r')
message += f.read()
#message += '<br>debug of DB object: <br>'
#for x in obj:
# message +=(x+": "+str(obj[x])+"<br>")
#message += "<p>"
#message += "</HTML>"
else:
message = 'No file was uploaded'
return message
async def resize_remote(url: str = 'https://s.gravatar.com/avatar/434d67e1ebc4109956d035077ef5adb8', height: int = 250, width: int = 250, image_format: str = 'JPEG', resample: Optional[int] = 1):
"""
Resizes image at URL specified to the specified output resolution and format.
* `url` specifies the image URL.
* The variables `width` and `height` specify the width and height of the output resize.
* The `image_format` variable can be any output format supported by Pillow. See here: https://tinyurl.com/yymmmpwk
* Variable `resample` is used to specify the resample mode used by Pillow. See here: https://tinyurl.com/y3jo6aph
* `Image.NEAREST = 0`
* `Image.LANCZOS = 1`
* `Image.BILINEAR = 2`
* `Image.BICUBIC = 3`
* `Image.BOX = 4`
* `Image.HAMMING = 5`
* Output is a an image file. Defaults to JPEG.
"""
response = requests.get(url)
if response.status_code != 200:
raise HTTPException(status_code=response.status_code,
detail=f'Server returned error {response.status_code}.')
if len(response.content) > 20971520:
raise HTTPException(status_code=413, detail="Content is too large.")
content = BytesIO(response.content)
content.seek(0)
image_buffer = resize(content, width, height, image_format, resample)
content.close()
return Response(image_buffer.getvalue(), status_code=200)
def uploadAndInput(req, name, email, title, comment): data.clearDatabase() lastID = data.writeDatabase(name, email, title, comment) last = data.getEntryFromID(lastID) obj = "" fileitem = req.form['file'] if fileitem.filename: # strip leading path from file name to avoid directory traversal attacks # also prepend DB ID so filenames are unique fname = str(last['_id'])+"_"+os.path.basename(fileitem.filename) obj = data.updateFilenameFromID(last['_id'],fname) # build absolute path to files directory dir_path = os.path.join(os.path.dirname(req.filename), 'files') open(os.path.join(dir_path, fname), 'wb').write(fileitem.file.read()) #obj = analyze.histogram(last['_id']) obj = image.resize(last['_id']) obj = analyze.histogram(last['_id'], 4) message = "<HTML>" message += 'The file "%s" was uploaded, resized, and analyzed successfully' % fname message += '\n<img src="'+cfg.imageWebPath+fname+'">' message += '<br>debug of DB object: <br>' for x in obj: message +=(x+": "+str(obj[x])+"<br>") message += "<p>" message += "</HTML>" else: message = 'No file was uploaded' return message
async def resize_local(data: ResizeImage):
"""
Resizes incoming base64 image.
* `b64_image` is the base64 image of any supported format. See here for supported inputs: https://tinyurl.com/yymmmpwk
* The variables `width` and `height` specify the width and height of the output resize.
* The `image_format` variable can be any output format supported by Pillow. See here: https://tinyurl.com/yymmmpwk
* Variable `resample` is used to specify the resample mode used by Pillow. See here: https://tinyurl.com/y3jo6aph
* `Image.NEAREST = 0`
* `Image.LANCZOS = 1`
* `Image.BILINEAR = 2`
* `Image.BICUBIC = 3`
* `Image.BOX = 4`
* `Image.HAMMING = 5`
* Output is a an image file. Defaults to JPEG.
"""
b64_image = data.get('base64_image')
if get_b64_size(b64_image) > 20971520:
raise HTTPException(status_code=413, detail="Content is too large.")
image_format = data.get('image_format')
width = data.get('width')
height = data.get('height')
resample = data.get('resample')
content = BytesIO(base64.b64decode(b64_image))
content.seek(0)
image_buffer = resize(content, width, height, image_format, resample)
content.close()
return Response(image_buffer.getvalue(), status_code=200)
def upload(req):
fileitem = req.form['file']
accepted = ['jpg', 'png', 'gif']
if fileitem.filename:
extension = os.path.splitext(fileitem.filename)[1][1:]
# ensure it's an image…
if (extension.lower() in accepted):
# strip leading path from file name to avoid directory traversal attacks
# also prepend DB ID so filenames are unique
fname = os.path.basename(fileitem.filename)
# build absolute path to files directory
dir_path = os.path.join(os.path.dirname(req.filename), 'files')
open(os.path.join(dir_path, fname), 'wb').write(fileitem.file.read())
obj = image.resize(fname)
message = '<html>The file "%s" was uploaded successfully' % fname
message += '<br/>Please copy the following, and paste in the text box after hitting <back> button'
message += '<textarea rows="4" cols="50"><img src="' + cfg.imageWebPath + fname + '"/></textarea>'
message += '</html>'
else:
message = 'No file was uploaded - only jpeg, gif, or png accepted'
return message
def upload(req): fileitem = req.form['file'] accepted = ['jpg', 'png', 'gif'] if fileitem.filename: extension = os.path.splitext(fileitem.filename)[1][1:] # ensure it's an image… if (extension.lower() in accepted): # strip leading path from file name to avoid directory traversal attacks # also prepend DB ID so filenames are unique fname = os.path.basename(fileitem.filename) # build absolute path to files directory dir_path = os.path.join(os.path.dirname(req.filename), 'files') open(os.path.join(dir_path, fname), 'wb').write(fileitem.file.read()) obj = image.resize(fname) message = '<html>The file "%s" was uploaded successfully' % fname message += '<br/>Please copy the following, and paste in the text box after hitting <back> button' message += '<textarea rows="4" cols="50"><img src="'+cfg.imageWebPath+fname+'"/></textarea>' message += '</html>' else: message = 'No file was uploaded - only jpeg, gif, or png accepted' return message
def sample(index):
if index < 0:
return flask.make_response('index cannot be negative', 404)
if index >= len(app.config['sample_offsets']):
return flask.make_response('index cannot be larger than amount of images', 404)
sample_offset = app.config['sample_offsets'][index]
with app.config['input_samples_file_lock']:
input_samples_file = app.config['arguments']['input_samples_file']
input_samples_file.seek(sample_offset[0])
data_bytes = input_samples_file.read(sample_offset[1])
computed_masked_crc32_of_data = masked_crc32c(data_bytes)
masked_crc32_of_data_bytes = input_samples_file.read(4)
masked_crc32_of_data = struct.unpack(
'<L', masked_crc32_of_data_bytes)[0]
if masked_crc32_of_data != computed_masked_crc32_of_data:
raise RuntimeError(
f'CRC integrity check failed for data in input samples file at {sample_offset[0]}')
example = viewer.example_pb2.Example()
example.ParseFromString(data_bytes)
data_shape_feature = example.features.feature['dataShape']
assert data_shape_feature.HasField('int64_list')
data_shape = data_shape_feature.int64_list.value
data_feature = example.features.feature['data']
assert data_feature.HasField('int64_list')
data = np.reshape(
np.array(data_feature.int64_list.value, dtype=np.uint8), tuple(data_shape))
image = np.repeat(data, repeats=3, axis=2)
if 'scale' in flask.request.args:
try:
scale_width = int(
flask.request.args['width']) if 'width' in flask.request.args else None
scale_height = int(
flask.request.args['height']) if 'height' in flask.request.args else None
except ValueError:
return flask.make_response('width or height malformed', 404)
if scale_width is None and scale_height is None:
return flask.make_response('width and height missing', 404)
if scale_width is None:
scale_width = int(scale_height / image.shape[0] * image.shape[1])
if scale_height is None:
scale_height = int(scale_width / image.shape[1] * image.shape[0])
size = np.array([[scale_width],
[scale_height]])
image = img.resize(image, size)
response = flask.make_response(img.encode(image, 'png'))
response.headers['Content-Type'] = 'image/png'
return response
def regenerate(solicitud):
for video in Video.objects.all():
image.resize(image.THUMB, video.imagen)
image.resize(image.SINGLE, video.imagen)
image.resize(image.HOME, video.imagen)
for curso in Curso.objects.all():
image.resize(image.THUMB, curso.imagen)
return redirect('/')
def regenerate(solicitud):
for video in Video.objects.all():
image.resize(image.THUMB, video.imagen)
image.resize(image.SINGLE, video.imagen)
image.resize(image.HOME, video.imagen)
for curso in Curso.objects.all():
image.resize(image.THUMB, curso.imagen)
return redirect('/')
def detect(im, cascade, haar_scale=1.2, min_neighbors=2, min_size=(20,20)):
haar_flags = 0
gray = image.rgb2gray(im)
small = image.resize(gray, by_percent=im_scale)
cv.EqualizeHist(small, small)
objs = cv.HaarDetectObjects(small, cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, cv.CV_HAAR_DO_CANNY_PRUNING, min_size)
return [Rect(r,n,cv.GetSize(im)) for r,n in objs]
def save(self, *args, **kwargs): super(Video, self).save(*args, **kwargs) if not self.id and not self.imagen: return image.resize(image.THUMB, self.imagen) image.resize(image.SINGLE, self.imagen) image.resize(image.HOME, self.imagen)
def save(self, *args, **kwargs):
super(Video, self).save(*args, **kwargs)
if not self.id and not self.imagen: return
image.resize(image.THUMB, self.imagen)
image.resize(image.SINGLE, self.imagen)
image.resize(image.HOME, self.imagen)
def get_resized_image(size, filename):
w, h = size.split("x")
_, ext = filename.split(".")
filename_with_size = "{}_{}x{}.jpg".format(filename, w, h)
file_path = bucket.download(filename_with_size)
if file_path:
return send_file(file_path, mimetype='image/{}'.format(ext))
file_path = bucket.download(filename)
if not file_path:
return abort(404)
resized_file_path = image.resize(file_path, (int(w), int(h)))
bucket.upload(resized_file_path, filename)
return send_file(resized_file_path, mimetype='image/{}'.format(ext))
def preprocess(self, request):
"""
Decode all input images into numpy array.
Note: This implementation doesn't properly handle error cases in batch mode,
If one of the input images is corrupted, all requests in the batch will fail.
:param request:
:return:
"""
img_list = []
param_name = self.signature['inputs'][0]['data_name']
input_shape = self.signature['inputs'][0]['data_shape']
for idx, data in enumerate(request):
img = data.get(param_name)
if img is None:
img = data.get("body")
if img is None:
img = data.get("data")
if img is None or len(img) == 0:
self.error = "Empty image input"
return None
# We are assuming input shape is NHWC
[h, w] = input_shape[1:3]
try:
img_arr = image.read(img)
except Exception as e:
logging.warn(e, exc_info=True)
self.error = "Corrupted image input"
return None
img_arr = image.resize(img_arr, w, h)
img_arr = image.transform_shape(img_arr)
img_list.append(img_arr)
#Convert to dict before returning [{name: image}]
img_list = [{param_name: img} for img in img_list]
return img_list
def resizeImage(image, size):
widthDev, heightDev = size
widthImg, heightImg = image.size
if widthImg <= widthDev and heightImg <= heightDev:
return image
ratioImg = float(widthImg) / float(heightImg)
ratioWidth = float(widthImg) / float(widthDev)
ratioHeight = float(heightImg) / float(heightDev)
if ratioWidth > ratioHeight:
widthImg = widthDev
heightImg = int(widthDev / ratioImg)
elif ratioWidth < ratioHeight:
heightImg = heightDev
widthImg = int(heightDev * ratioImg)
else:
widthImg, heightImg = size
return image.resize((widthImg, heightImg), Image.ANTIALIAS)
def resizeImage(image, size):
widthDev, heightDev = size
widthImg, heightImg = image.size
if widthImg <= widthDev and heightImg <= heightDev:
return image
ratioImg = float(widthImg) / float(heightImg)
ratioWidth = float(widthImg) / float(widthDev)
ratioHeight = float(heightImg) / float(heightDev)
if ratioWidth > ratioHeight:
widthImg = widthDev
heightImg = int(widthDev / ratioImg)
elif ratioWidth < ratioHeight:
heightImg = heightDev
widthImg = int(heightDev * ratioImg)
else:
widthImg, heightImg = size
return image.resize((widthImg, heightImg), Image.ANTIALIAS)
def resize_image(data, resize_ratio=image.DEFAULT_RATIO):
return base64.encode(image.resize(base64.b64decode(data)))
def preprocess(listofimages,
inputsize,
mean,
std,
dimorder='chw',
channelorder='rgb',
scale=1.0):
"""
Preprocess a list of images to be used with the neural network.
Automatically loads images from disk if a string is given.
Parameters
----------
listofimages : List of strings or list of unprocessed ndarrays, shape (Height, Width, Channels)
The list may contain image filepaths and image ndarrays.
ndarrays have to be in range between 0 and 1.
inputsize : tuple or list
Target input data dimensionality of same format as dimorder
mean : ndarray
Image mean definition of format (channels, height, width) or (channels, ).
Set to None, to ignore.
std : ndarray
Standard deviation definition of format (channels, height, width) or (channels, )
Set to None, to ignore
dimorder : string
Order of dimensions. Default chw (channel, height, width)
channelorder : string
Order of color channels. Default: rgb (red, green, blue)
scale : float
Scaling of color values.
Returns
-------
output : ndarray
Preprocessed batch of images ready for the forward pass.
"""
# Convert dimorder to tuple
dimmap = {
'h': 0,
'w': 1,
'c': 2,
}
imsize = (inputsize[dimorder.find('h')], inputsize[dimorder.find('w')])
dimorder = [dimmap[c] for c in dimorder]
# Load data in first step from list
data = np.zeros(
(len(listofimages), inputsize[0], inputsize[1], inputsize[2]),
dtype=np.float32)
for i, h in enumerate(listofimages):
if type(h) is str:
im = image.read(h)
elif type(h) is np.ndarray:
im = h.copy()
if im.shape[:2] != imsize:
im = image.resize(im, imsize)
if scale != 1.0:
im *= scale
if mean is not None and mean.ndim == 1:
im -= mean
if std is not None and std.ndim == 1:
im /= std
if channelorder == 'bgr':
im = im[:, :, ::-1]
im = im.transpose(
*dimorder) # resulting order is: channels x height x width
if mean is not None and mean.ndim == 3:
im -= mean
if std is not None and std.ndim == 3:
im /= std
data[i] = im
return data
def main(progname, *args):
parser = OptionParser()
parser.add_option("-f", "--file", dest="filename", default=None,
help="analyze a given FILE ending in .jpg or .jpeg", metavar="FILE")
parser.add_option("-i", "--imageset", dest="imageset", default=None,
help="Runs on a predefined set of algorithms (li,chow,china,custom)")
parser.add_option("-d", "--debug", dest="debug", action="store_true", default=False,
help="Enable visual debugging.")
parser.add_option("-t", "--type", dest="type", default="all",
help="Specifies the type of feature to debug. Defaults to all.")
(options, args) = parser.parse_args(list(args))
if options.imageset:
if options.imageset == 'li':
process(li_dir)
return 0
elif options.imageset == 'chow':
process(chow_dir)
return 0
elif options.imageset == 'china':
process(china_dir)
return 0
elif options.imageset == 'custom':
process()
return 0
if not options.filename:
print "Please specify a file (--file) or image set (--imageset)."
return 1
if not options.debug:
process([load_image(options.filename)])
return 0
if options.filename.startswith('data/'):
options.filename = options.filename[len('data/'):]
tdata = load_image(options.filename)
kind = options.type.lower()
size = None #(320,240,'crop') # (0.5, 0.5, 'resize-p')
if size is None:
im = tdata.load()
elif size[-1] == 'crop':
im = image.random_cropped_region(tdata.load(), size[:2])
elif size[-1] == 'resize':
im = tdata.load(size[:2])
elif size[-1] == 'resize-p':
im = image.resize(tdata.load(), by_percent=size[:2])
else:
raise TypeError, "Invalid image sizing type."
image.show(im, "Image")
#l,u,v = image.split(image.rgb2luv(im))
##cv.Set(l, 128)
##cv.EqualizeHist(l, l)
##cv.EqualizeHist(u, u)
##image.show(image.luv2rgb(image.merge(l,u,v)), "test")
#s = cv.GetSize(im)
#t = image.absDiff(u,v)
#image.show(t, "test")
#print "Test Score:", cv.CountNonZero(t) / float(s[0] * s[1])
##image.show(image.threshold(image.And(u,v), threshold=1), "LUV")
# noise
if kind in ('all','noise'):
noise_img, score = noise.measure(im, debug=True)
#image.show(noise_img, "Noise Result")
print 'Noise Score:', score, noise.boolean(score)
# contrast
if kind in ('all','contrast'):
contrast_img, score = contrast.measure(im, debug=True)
#image.show(contrast_img, "Contrast Result")
print 'Contrast Score:', score, contrast.boolean(score)
# blur
if kind in ('all','blur','composition'):
focused, score = blur.measure(im, debug=kind in ('all','blur'))
#image.show(focused, "Blur Result")
print 'Blur Score:', score, blur.boolean(score)
# composition
if kind in ('all','composition'):
composition_img, score = composition.measure(im,
(focused,score, blur.boolean(score)), debug=True)
print 'Composition Score:', score, composition.boolean(score)
if kind in ('faces',):
result, score = faces.measure(im,debug=True)
print "Face Score:", score, faces.boolean(faces)
#win = CornerTweaker(im)
#win.show()
#_, sat, _ = image.split(image.rgb2hsv(im))
#arr = image.cv2array(sat)
#print arr.mean(), arr.std()
# faces
#im, score = faces.measure(im, debug=True)
#print score, faces.boolean(score)
# composition
#noise_img, score = noise.measure(im, debug=False)
##n = (noise_img, score, noise.boolean(score))
#hulls, score = blur.measure(im, debug=False)
#b = (hulls, score, blur.boolean(score))
#cimg, score = composition.measure(im, b, debug=True)
#print score, composition.boolean(score)
# BLUR
#from time import time
#start = time()
##im2 = image.threshold(image.laplace(im), threshold=75, type=cv.CV_THRESH_TOZERO)
#hulls, score = blur.measure(im, debug=True)
##blur_img, score = blur.measure(im, debug=True)
#end = time()
#print "Time:", (end - start), "seconds"
#image.show(im, "image")
##image.show(noise_img, "Noise Image")
#print score, blur.boolean(score)
#CONTRAST
#_, score = contrast.measure(im, debug=True)
#image.show(im, "Image")
#print score, contrast.boolean(score)
"""
#BLUR
#im2 = image.threshold(image.laplace(im), threshold=75, type=cv.CV_THRESH_TOZERO)
im3, score = blur.measure(im, debug=True)
image.show(im, "image")
image.show(im3, "Focus Mask")
print score, blur.boolean(score)
#plt.show()
"""
#NOISE
#noise_img, score = noise.measure(im, debug=True)
#image.show(noise_img, "Noise")
#print score, noise.boolean(score)
"""
#hwin = ColorHistograms(im)
#hwin.show()
hwin = HistogramWindow(image.rgb2gray(im))
hwin.show()
print cv.GetSize(im), cv.GetSize(im2)
print 'blur', papers.blurry_histogram(im)
#print papers.blurry_histogram(im2)
wind = DerivativeTweaker(im, title="image derivative")
wind.show()
win = EdgeThresholdTweaker(im, title="image edges")
win.show(50)#edge_threshold(im))
#win2 = EdgeThresholdTweaker(im2, title="image resized edges")
#win2.show(edge_threshold(im2))
"""
cv.WaitKey()
cv.DestroyAllWindows()
return 0
from image import load_image, resize
from facial import facecrop
import cv2
import numpy as np
a = lambda *k: np.array(k)
jeff = load_image('jeff.jpg')
jeff = facecrop(jeff)
jeff = resize(jeff, 512)
height, width = jeff.shape[0:2]
bw = jeff[:, :, 1:2]
bw = cv2.equalizeHist(bw)
# lowpass
from cv2tools import vis, filt
fbw = np.divide(bw, 255, dtype='float32')
# lp = vis.lanczos_filter(fbw, 2,2, a=2)
lp = vis.lanczos_filter(fbw**2.2, 2, 2, a=2)
c = np.full_like(jeff, 255)
def italic_iteration(refimg, callback, angle=25, step=4.0, vstep=4.0):
# signature for callback: (x, y, linebegin=False)
t = theta = angle / 180 * np.pi
xf = a([np.cos(t), -np.sin(t)], [np.sin(t), np.cos(t)])
def stretchImage(image, size):
widthDev, heightDev = size
return image.resize((widthDev, heightDev), Image.ANTIALIAS)
def main(progname, *args):
parser = OptionParser()
parser.add_option("-f",
"--file",
dest="filename",
default=None,
help="analyze a given FILE ending in .jpg or .jpeg",
metavar="FILE")
parser.add_option(
"-i",
"--imageset",
dest="imageset",
default=None,
help="Runs on a predefined set of algorithms (li,chow,china,custom)")
parser.add_option("-d",
"--debug",
dest="debug",
action="store_true",
default=False,
help="Enable visual debugging.")
parser.add_option(
"-t",
"--type",
dest="type",
default="all",
help="Specifies the type of feature to debug. Defaults to all.")
(options, args) = parser.parse_args(list(args))
if options.imageset:
if options.imageset == 'li':
process(li_dir)
return 0
elif options.imageset == 'chow':
process(chow_dir)
return 0
elif options.imageset == 'china':
process(china_dir)
return 0
elif options.imageset == 'custom':
process()
return 0
if not options.filename:
print "Please specify a file (--file) or image set (--imageset)."
return 1
if not options.debug:
process([load_image(options.filename)])
return 0
if options.filename.startswith('data/'):
options.filename = options.filename[len('data/'):]
tdata = load_image(options.filename)
kind = options.type.lower()
size = None #(320,240,'crop') # (0.5, 0.5, 'resize-p')
if size is None:
im = tdata.load()
elif size[-1] == 'crop':
im = image.random_cropped_region(tdata.load(), size[:2])
elif size[-1] == 'resize':
im = tdata.load(size[:2])
elif size[-1] == 'resize-p':
im = image.resize(tdata.load(), by_percent=size[:2])
else:
raise TypeError, "Invalid image sizing type."
image.show(im, "Image")
#l,u,v = image.split(image.rgb2luv(im))
##cv.Set(l, 128)
##cv.EqualizeHist(l, l)
##cv.EqualizeHist(u, u)
##image.show(image.luv2rgb(image.merge(l,u,v)), "test")
#s = cv.GetSize(im)
#t = image.absDiff(u,v)
#image.show(t, "test")
#print "Test Score:", cv.CountNonZero(t) / float(s[0] * s[1])
##image.show(image.threshold(image.And(u,v), threshold=1), "LUV")
# noise
if kind in ('all', 'noise'):
noise_img, score = noise.measure(im, debug=True)
#image.show(noise_img, "Noise Result")
print 'Noise Score:', score, noise.boolean(score)
# contrast
if kind in ('all', 'contrast'):
contrast_img, score = contrast.measure(im, debug=True)
#image.show(contrast_img, "Contrast Result")
print 'Contrast Score:', score, contrast.boolean(score)
# blur
if kind in ('all', 'blur', 'composition'):
focused, score = blur.measure(im, debug=kind in ('all', 'blur'))
#image.show(focused, "Blur Result")
print 'Blur Score:', score, blur.boolean(score)
# composition
if kind in ('all', 'composition'):
composition_img, score = composition.measure(
im, (focused, score, blur.boolean(score)), debug=True)
print 'Composition Score:', score, composition.boolean(score)
if kind in ('faces', ):
result, score = faces.measure(im, debug=True)
print "Face Score:", score, faces.boolean(faces)
#win = CornerTweaker(im)
#win.show()
#_, sat, _ = image.split(image.rgb2hsv(im))
#arr = image.cv2array(sat)
#print arr.mean(), arr.std()
# faces
#im, score = faces.measure(im, debug=True)
#print score, faces.boolean(score)
# composition
#noise_img, score = noise.measure(im, debug=False)
##n = (noise_img, score, noise.boolean(score))
#hulls, score = blur.measure(im, debug=False)
#b = (hulls, score, blur.boolean(score))
#cimg, score = composition.measure(im, b, debug=True)
#print score, composition.boolean(score)
# BLUR
#from time import time
#start = time()
##im2 = image.threshold(image.laplace(im), threshold=75, type=cv.CV_THRESH_TOZERO)
#hulls, score = blur.measure(im, debug=True)
##blur_img, score = blur.measure(im, debug=True)
#end = time()
#print "Time:", (end - start), "seconds"
#image.show(im, "image")
##image.show(noise_img, "Noise Image")
#print score, blur.boolean(score)
#CONTRAST
#_, score = contrast.measure(im, debug=True)
#image.show(im, "Image")
#print score, contrast.boolean(score)
"""
#BLUR
#im2 = image.threshold(image.laplace(im), threshold=75, type=cv.CV_THRESH_TOZERO)
im3, score = blur.measure(im, debug=True)
image.show(im, "image")
image.show(im3, "Focus Mask")
print score, blur.boolean(score)
#plt.show()
"""
#NOISE
#noise_img, score = noise.measure(im, debug=True)
#image.show(noise_img, "Noise")
#print score, noise.boolean(score)
"""
#hwin = ColorHistograms(im)
#hwin.show()
hwin = HistogramWindow(image.rgb2gray(im))
hwin.show()
print cv.GetSize(im), cv.GetSize(im2)
print 'blur', papers.blurry_histogram(im)
#print papers.blurry_histogram(im2)
wind = DerivativeTweaker(im, title="image derivative")
wind.show()
win = EdgeThresholdTweaker(im, title="image edges")
win.show(50)#edge_threshold(im))
#win2 = EdgeThresholdTweaker(im2, title="image resized edges")
#win2.show(edge_threshold(im2))
"""
cv.WaitKey()
cv.DestroyAllWindows()
return 0
def load_reference_image(self, path):
loaded = load_image(path)
loaded = artistic_enhance(loaded)
loaded = resize(loaded, self.pixelsize)
loaded = np.clip(loaded * 255, 0, 255)
self.set_reference_image(loaded.astype('uint8'))
shellRecipe2 = recipe2['shell']['recipe']
# creating a variable for recipe 3, a string that will be the title of the recipe, pulling info from the recipe index
recipe3String = recipe3['base_layer']['name'] + ' with ' + recipe3['mixin'][
'name'] + ' and ' + recipe3['condiment']['name']
# creating variables that will contain the recipe for each part of the taco, grabbing positions from the json data keys
seasoningRecipe3 = recipe3['seasoning']['recipe']
condimentRecipe3 = recipe3['condiment']['recipe']
mixinRecipe3 = recipe3['mixin']['recipe']
base_layerRecipe3 = recipe3['base_layer']['recipe']
shellRecipe3 = recipe3['shell']['recipe']
# the following image processes use the pillow library
# creating a variable to open the taco pic, resizing the pic, then saving the resized pic
image = Image.open('tacopic.jpg')
image = image.resize((400, 400))
image.save('tacopic.jpg')
# opening the taco pic again, using the ImageDraw to write on the picture, setting the font, font size, color, and location of the text, then saving the edited pic
image = Image.open('tacopic.jpg')
imgDraw = ImageDraw.Draw(image)
font = ImageFont.truetype('DejaVuSans.ttf', 20)
imgDraw.text([0, 350], 'Random Taco Cookbook', fill='black', font=font)
image.save('tacopic2.jpg')
# creating a new word document using the docx library
document = docx.Document()
# adding a heading (title), on the first line, and setting the font size
newHeading = document.add_heading(level=0)
writeHeading = newHeading.add_run('Random Taco Cookbook')
writeHeading.font.size = Pt(24)
if __name__ == "__main__":
path = []
if len(sys.argv) > 1:
for i in range(1, len(sys.argv)):
path.append(sys.argv[i])
else:
# path = ["p1.jpg", "p2.jpg"]
# path = ["p1.jpg", "p2.jpg", "p3.jpg"]
path = ["p1.jpg", "p2.jpg", "p3.jpg", "p4.jpg"]
images = []
for p in path:
raw = cv2.imread(p)
images.append(Image(resize(raw)))
result = images[0].raw
for i in range(1,len(images)):
img1 = Image(result)
img2 = images[i]
visualizeMatch(img1, img2)
M = transform(img1, img2)
# print(M)
warpImg = cv2.warpPerspective(img2.raw, np.linalg.inv(M), (img1.raw.shape[1]+img2.raw.shape[1], img1.raw.shape[0]))
result = stitch(img1.raw, warpImg)
cv2.imshow("Panorama", result)
cv2.waitKey(0)
cv2.imwrite("result.jpg", result)
def save(self, *args, **kwargs):
super(CursoDocente, self).save(*args, **kwargs)
if not self.id and not self.imagen: return
image.resize((67, 67), self.imagen)
thickness=8,
lineType=cv2.LINE_AA,
shift=0,
)
def blurit(map):
blur_r = int(map.shape[0] / 35)
for i in range(3):
map = cv2.blur(map, (blur_r, blur_r))
map.shape += 1,
return map
map = blurit(map)
return np.divide(map, 255, dtype='float32')
if __name__ == '__main__':
jeff = load_image('jeff.jpg')
jeff = facecrop(jeff)
jeff = resize(jeff, 256)
map = heatmap(jeff)
cv2.imshow('jeff', jeff)
cv2.imshow('map', map)
cv2.imshow('blended', (jeff * map).astype('uint8'))
cv2.waitKey(0)
def refresh_image(self): img = self._project.get_image_paint(self._current_dataset,self._selectedItem,self._selectedContour) qim = to_qt(resize(img,(780,480))) pix = QtGui.QPixmap.fromImage(qim) self.imgLbl.setPixmap(pix)
def getEmail():
detach_dir = cfg.emailImagePath # directory where to save attachments
user = cfg.emailUser
pwd = cfg.emailPass
# connecting to the gmail imap server
m = imaplib.IMAP4_SSL("imap.gmail.com")
m.login(user,pwd)
m.select(cfg.emailLabel) # here you a can choose a mail box like INBOX instead
# use m.list() to get all the mailboxes
#print m.list()
resp, items = m.search(None, "ALL")
# you could filter using the IMAP rules here (check http://www.example-code.com/csharp/imap-search-critera.asp)
#resp, items = m.search(None, '(FROM "ucla.edu")')
authorName = ""
authorEmail = ""
authorTitle = ""
authorDescription = ""
items = items[0].split()
if(items):
for emailid in items:
resp, data = m.fetch(emailid, "(RFC822)")
#"`(RFC822)`" means "get the whole stuff", but you can ask for headers only, etc
email_body = data[0][1] # getting the mail content
mail = email.message_from_string(email_body) # parsing the mail content to get a mail object
#Check if any attachments at all
if mail.get_content_maintype() != 'multipart':
continue
#print mail["From"] +":" + mail["Subject"]
authorTitle = mail["Subject"]
authorName = mail["From"].split('<',2)[0]
#print authorName
authorEmail = mail["From"].split('<',2)[1].rstrip(">")
#print authorEmail
for part in mail.walk():
# multipart are just containers, so we skip them
if part.get_content_maintype() == 'multipart':
continue
if part.get_content_maintype() == 'text':
message = part.get_payload()
#print "body :"+ message
authorDescription = message
#print "Name :"+ authorName
#print "eMail:"+ authorEmail
#print "title:"+authorTitle
#print "desc :"+ authorDescription
continue
# is this part an attachment ?
if part.get('Content-Disposition') is None:
continue
filename = part.get_filename()
counter = 1
# if there is no filename, we create one with a counter to avoid duplicates
if not filename:
filename = 'part-%03d%s' % (counter, 'bin')
counter += 1
att_path = os.path.join(detach_dir, authorEmail+"_"+filename)
#Check if its already in the email directory
if not os.path.isfile(att_path) :
# new data, save info to database
lastID = db.writeDatabase(authorName, authorEmail,authorTitle, authorDescription)
last = db.getEntryFromID(lastID)
#write to normal image directory w/ UID prefix
imgPath = os.path.join(cfg.imageFilePath, str(last['_id'])+"_"+filename)
obj = db.updateFilenameFromID(last['_id'],str(last['_id'])+"_"+filename)
eImgPath = os.path.join(cfg.emailImagePath, authorEmail+"_"+filename)
# write to email image directory, so we don't re-insert same image later
fp = open(eImgPath, 'wb')
fp.write(part.get_payload(decode=True))
fp.close()
fp = open(imgPath, 'wb')
fp.write(part.get_payload(decode=True))
fp.close()
obj = img.resize(last['_id'])
obj = analyze.histogram(last['_id'], cfg.analysisMode)
def save(self, *args, **kwargs): super(CursoDocente, self).save(*args, **kwargs) if not self.id and not self.imagen: return image.resize((67, 67), self.imagen)
def image(index):
if index < 0:
return flask.make_response('index cannot be negative', 404)
if index >= len(app.config['annotations'].keys()):
return flask.make_response('index cannot be larger than amount of images', 404)
image = sorted(app.config['annotations'].keys())[index]
image = img.read(image)
image = img.convert_color_space(
image,
source_color_space=app.config['arguments']['color_space'],
target_color_space='RGB',
)
if 'crop' in flask.request.args:
try:
center_x = float(flask.request.args['centerX'])
center_y = float(flask.request.args['centerY'])
radius = float(flask.request.args['radius'])
except (KeyError, ValueError):
return flask.make_response('centerX, centerY, or radius missing or malformed', 404)
upper_left = np.array([[center_x - radius],
[center_y - radius]])
lower_right = np.array([[center_x + radius],
[center_y + radius]])
image = img.crop(
image,
upper_left,
lower_right,
[
app.config['arguments']['default_gray'],
app.config['arguments']['default_gray'],
app.config['arguments']['default_gray'],
],
)
if 'scale' in flask.request.args:
try:
scale_width = int(
flask.request.args['width']) if 'width' in flask.request.args else None
scale_height = int(
flask.request.args['height']) if 'height' in flask.request.args else None
except ValueError:
return flask.make_response('width or height malformed', 404)
if scale_width is None and scale_height is None:
return flask.make_response('width and height missing', 404)
if scale_width is None:
scale_width = int(scale_height / image.shape[0] * image.shape[1])
if scale_height is None:
scale_height = int(scale_width / image.shape[1] * image.shape[0])
size = np.array([[scale_width],
[scale_height]])
image = img.resize(image, size)
response = flask.make_response(img.encode(image, 'png'))
response.headers['Content-Type'] = 'image/png'
return response
def loadImage(self, filename, resize=None):
image = Image.open(filename)
if resize is not None:
image = image.resize(resize, Image.ANTIALIAS)
return ImageTk.PhotoImage(image)
def stretchImage(image, size):
widthDev, heightDev = size
return image.resize((widthDev, heightDev), Image.ANTIALIAS)
