async def get_segmentation_map(file: bytes = File(...)):
"""Get segmentation maps from image file"""
segmented_image = get_segments(model, file)
with tempfile.NamedTemporaryFile(mode="w+b", suffix=".png",
delete=False) as outfile:
segmented_image.save(outfile)
return FileResponse(outfile.name, media_type="image/png")
def start(image_name, output_name):
image = Image.open(image_name)
segments = segmentation.get_segments(image, THRESHOLD, MIN_SIZE)
exp = Expressionist(segments)
exp.manipulate(image.size).save(output_name)
segments.sort(key = lambda s: s.size)
l = len(segments)
def evaluate(datos, ponderaciones, silencios, carga_computacional=1,tipo_clustering="KMEANS", ncluster = 3, metrica = "SIL"):
'''
Dado un clasificador y un conjunto de train y test devuelve su tasa de acierto en test.
'''
global light_segs
fecha = False
if len(ponderaciones) == 0:
return 0.0
ligero = np.sign(carga_computacional) < 0
carga_computacional = abs(carga_computacional)
if carga_computacional < 1:
datos = datos.sample(int(datos.shape[0] * carga_computacional))
datos = datos.sort_index()
carga_computacional = 1
fecha = True
if carga_computacional>=1:
if not ligero:
segmentos, _ = sg.segmentate_data_frame(df=datos, montecarlo=1, min_size=4, silence=silencios,
vector_importancias=ponderaciones, verbose=False)
mean = len(sg.ultra_light_segmentation(datos, fecha=fecha))/2
else:
if len(light_segs) == 0:
light_segs = sg.ultra_light_segmentation(datos, fecha=fecha)
segmentos = light_segs
mean = len(segmentos)/2
#mean = int(datos.shape[0]/100)
if carga_computacional == 1:
segmentados = cl.apply_segmentation(datos, segmentos, silencios, ponderaciones, fecha)
if segmentados.shape[0] <= 6:
return 0.0
else:
std = np.sqrt(mean)
nsegs=[]
for i in range(segmentados.shape[0]):
nsegs.append([i,i+1])
segmentos = sg.join_segments(data=segmentados, o_segments=nsegs, distance=sg.interpretable_distance, threshold=0.5, minimum_size=1,silence=silencios, vector_importancias=ponderaciones)[0]
segmentados = cl.apply_segmentation(segmentados, segmentos, silencios, ponderaciones, fecha)
return cl.hopkins_statistic(cl.filter_numerical(segmentados),m=int(segmentados.shape[0]*0.5))* normal_correction(mean, std, len(segmentados))
elif carga_computacional==2:
if tipo_clustering == "DTW":
segmentados = sg.get_segments(datos, segmentos)
asignaciones = pam.kmedoids(segmentados, n_clus = ncluster)
segments_df = cl.apply_clustering(datos, segmentos, asignaciones[1], asignaciones[2])
elif tipo_clustering == "KMEANS":
segments_df = cl.full_clustering(datos, segmentos, n_clus = ncluster, silencio=silencios, pesos = ponderaciones, normalizar=False)
if metrica == "SIL":
return cl.sil_metric(segments_df[0])
elif metrica == "ENTROPY":
return entropy_metric(segments_df)
rgb = cv2.cvtColor(camera_frame, cv2.COLOR_BGR2BGRA)
cv2.imshow('camera_frame', rgb)
if frame_time - last_frame_time > FRAME_FREQUENCY:
cropped = frame[300:600, 400:900]
gray_cropped = Image.fromarray(cropped).convert('L')
row_avg = numpy.average(numpy.asarray(gray_cropped))
avg = numpy.average(row_avg)
print avg
if avg > 165:
count += 1
if avg > 165 and count > 20:
"""
thread this!
"""
cv2.imwrite('capture.png', cropped)
resized_images, segments, images = get_segments('capture.png')
print segments
segment_boxes = segments
for i in xrange(len(images)):
cv2.imwrite('segment-' + str(i) + '.png',
numpy.asarray(images[i]))
last_frame_time = frame_time
count = 0
net = network.Network([784, 30, 13])
net.load_parameters()
for i in resized_images:
print net.recognize(i)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def get_segmentation_map(file: bytes = File(...)):
"""Get segmentation maps from image file"""
segmented_image = get_segments(model, file)
bytes_io = io.BytesIO()
segmented_image.save(bytes_io, format="PNG")
return Response(bytes_io.getvalue(), media_type="image/png")
def get_segmentation_map(file: bytes = File(...)):
"""Obtenemos el mapa de segmentación de la imagen"""
segmented_image = get_segments(model, file)
bytes_io = io.BytesIO()
segmented_image.save(bytes_io, format="PNG")
return Response(bytes_io.getvalue(), media_type="image/png")