def human_afterloop(output_directory, pre_time, fle_name, buffer_directory):
start2 = time()
d_c = import_edited(buffer_directory)
rebw = load(open(buffer_directory+'-'+'DO_NOT_TOUCH_ME.dmp','rb'))
seg_dc = (label(d_c,neighbors=4)+1)*d_c
if np.max(seg_dc)<4:
return 'FAILED: mask for %s looks unsegmented' % fle_name
colormap = repaint_culsters(int(np.max(seg_dc)))
segs = len(set(seg_dc.flatten().tolist()))-1
# shows the result before saving the clustering and printing to the user the number of the images
plt.subplot(1,2,1)
plt.title(fle_name)
plt.imshow(rebw, cmap='gray', interpolation='nearest')
plt.subplot(1,2,2)
plt.title('Segmentation - clusters: %s'%str(segs))
plt.imshow(mark_boundaries(rebw, d_c))
plt.imshow(seg_dc, cmap=colormap, interpolation='nearest', alpha=0.3)
plt.show()
plt.imshow(mark_boundaries(rebw, d_c))
plt.imshow(seg_dc, cmap=colormap, interpolation='nearest', alpha=0.3)
plt.savefig(path.join(output_directory, fle_name+'_%s_clusters.png'%str(segs)), dpi=500, bbox_inches='tight', pad_inches=0.0)
return fle_name+'\t clusters: %s,\t total time : %s'%(segs, "{0:.2f}".format(time()-start2+pre_time))
def fun_compare_colorsegmentation_and_display(image_data, number_segments=250, compactness_factor=10):
"""
The function is a copy of what does this link http://scikit-image.org/docs/dev/auto_examples/plot_segmentations.html
"""
segments_fz = felzenszwalb(image_data, scale=100, sigma=0.5, min_size=50)
segments_slic = slic(image_data, n_segments=number_segments, compactness=compactness_factor, sigma=1)
segments_quick = quickshift(image_data, kernel_size=3, max_dist=6, ratio=0.5)
print ("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
print ("Slic number of segments: %d" % len(np.unique(segments_slic)))
print ("Quickshift number of segments: %d" % len(np.unique(segments_quick)))
fig, ax = plt.subplots(1, 3, sharex=True, sharey=True, subplot_kw={"adjustable": "box-forced"})
fig.set_size_inches(8, 3, forward=True)
fig.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)
ax[0].imshow(mark_boundaries(image_data, segments_fz, color=(1, 0, 0)))
ax[0].set_title("Felzenszwalbs's method")
ax[1].imshow(mark_boundaries(image_data, segments_slic, color=(1, 0, 0)))
ax[1].set_title("SLIC")
ax[2].imshow(mark_boundaries(image_data, segments_quick, color=(1, 0, 0)))
ax[2].set_title("Quickshift")
for a in ax:
a.set_xticks(())
a.set_yticks(())
plt.show()
def test_mark_boundaries():
image = np.zeros((10, 10))
label_image = np.zeros((10, 10), dtype=np.uint8)
label_image[2:7, 2:7] = 1
ref = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
[0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
[0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
marked = mark_boundaries(image, label_image, color=white, mode='thick')
result = np.mean(marked, axis=-1)
assert_array_equal(result, ref)
ref = np.array([[0, 2, 2, 2, 2, 2, 2, 2, 0, 0],
[2, 2, 1, 1, 1, 1, 1, 2, 2, 0],
[2, 1, 1, 1, 1, 1, 1, 1, 2, 0],
[2, 1, 1, 2, 2, 2, 1, 1, 2, 0],
[2, 1, 1, 2, 0, 2, 1, 1, 2, 0],
[2, 1, 1, 2, 2, 2, 1, 1, 2, 0],
[2, 1, 1, 1, 1, 1, 1, 1, 2, 0],
[2, 2, 1, 1, 1, 1, 1, 2, 2, 0],
[0, 2, 2, 2, 2, 2, 2, 2, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
marked = mark_boundaries(image, label_image, color=white,
outline_color=(2, 2, 2), mode='thick')
result = np.mean(marked, axis=-1)
assert_array_equal(result, ref)
def test_mark_boundaries():
image = np.zeros((10, 10))
label_image = np.zeros((10, 10))
label_image[2:7, 2:7] = 1
ref = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
result = mark_boundaries(image, label_image, color=(1, 1, 1)).mean(axis=2)
assert_array_equal(result, ref)
ref = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 2, 0],
[0, 0, 1, 2, 2, 2, 2, 1, 2, 0],
[0, 0, 1, 2, 0, 0, 0, 1, 2, 0],
[0, 0, 1, 2, 0, 0, 0, 1, 2, 0],
[0, 0, 1, 2, 0, 0, 0, 1, 2, 0],
[0, 0, 1, 1, 1, 1, 1, 2, 2, 0],
[0, 0, 2, 2, 2, 2, 2, 2, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
result = mark_boundaries(image, label_image, color=(1, 1, 1),
outline_color=(2, 2, 2)).mean(axis=2)
assert_array_equal(result, ref)
def updateParametros(val):
global p_segmentos, p_sigma, p_compactness, segments, image, cuda_python
if(val == "Python SLIC"):
cuda_python = 0
elif(val == "CUDA gSLICr"):
cuda_python = 1
p_segmentos = int("%d" % (slider_segmentos.val))
p_sigma = slider_sigma.val
p_compactness = slider_compactness.val
image = c_image.copy()
if(cuda_python == 0):
start_time = time.time()
segments = slic(img_as_float(image), n_segments=p_segmentos, sigma=p_sigma, compactness=p_compactness)
print("--- Tempo Python skikit-image SLIC: %s segundos ---" % (time.time() - start_time))
else:
start_time = time.time()
gSLICrInterface.process( p_segmentos)
print("--- Tempo C++/CUDA gSLICr: %s segundos ---" % (time.time() - start_time))
segments = cuda_seg
obj.set_data(mark_boundaries(img_as_float(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), segments, outline_color=p_outline))
draw()
def overlay_cells(cells, image, colors):
"Overlay the edges of each individual cell in the provided image"
tmp = color.gray2rgb(image)
for k in cells.keys():
c = cells[k]
if c.selection_state == 1:
col = colors[c.color_i][:3]
for px in c.outline:
x, y = px
tmp[x, y] = col
if c.sept_mask is not None:
try:
x0, y0, x1, y1 = c.box
tmp[x0:x1, y0:y1] = mark_boundaries(tmp[x0:x1, y0:y1],
img_as_int(
c.sept_mask),
color=col)
except IndexError:
c.selection_state = -1
return tmp
def grabcut(img, targetness):
u"""
Segmenting the best target-like region from an targetness map.
"""
mask = np.ones(img.shape[:2], np.uint8) * cv2.GC_BGD
score_th = scoreatpercentile(targetness, 95)
mask[targetness >= score_th] = cv2.GC_PR_FGD
score_th = scoreatpercentile(targetness, 99)
mask[targetness >= score_th] = cv2.GC_FGD
mask = cv2.medianBlur(mask, 15)
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
cv2.grabCut(img, mask, None, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_MASK)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
lab_mask2 = bwlabel(mask2)
lab_list = np.unique(lab_mask2.flatten())[1:]
lab_argmax = np.argmax(
[np.max(targetness[lab_mask2 == i]) for i in lab_list])
mask2[lab_mask2 != lab_list[lab_argmax]] = 0
img2 = img.copy()
img2[mask2 < 1, :] = [0, 43, 54]
img2 = mark_boundaries(img2, mask2)
return img2, mask2
def onclick(event): #globals again global _clickclassi global bounded global _txt #If left mouse button click if event.button == 1: #calculate color for class color = cm(int(_clickclassi*(256.0/maxClasses)))[:3] #create mask containing only selected segment mask = np.zeros(img.shape[:2], dtype = "uint8") mask[segments == segments[int(event.ydata),int(event.xdata)]] = 2 #Highlight segment on image and display bounded = mark_boundaries(bounded,mask,color=color,mode='thick') ax.imshow(bounded) plt.draw() #Add segment index and class to labels labels.append((segments[int(event.ydata),int(event.xdata)],_clickclassi)) #If right mouse button clikc if event.button == 3: #Cycle through classes _clickclassi = (_clickclassi + 1) % maxClasses #Clear plot and redraw to get rid of existing text labels plt.cla() ax.imshow(bounded) _txt = plt.text(0,-20,labelText +str(iclasses[_clickclassi])) plt.draw()
def _apply(self, img_msg, label_msg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(img_msg)
label_img = bridge.imgmsg_to_cv2(label_msg)
# publish only valid label region
applied = img.copy()
applied[label_img == 0] = 0
applied_msg = bridge.cv2_to_imgmsg(applied, encoding=img_msg.encoding)
applied_msg.header = img_msg.header
self.pub_img.publish(applied_msg)
# publish visualized label
if img_msg.encoding in {'16UC1', '32SC1'}:
# do dynamic scaling to make it look nicely
min_value, max_value = img.min(), img.max()
img = (img - min_value) / (max_value - min_value) * 255
img = gray2rgb(img)
label_viz_img = label2rgb(label_img, img, bg_label=0)
label_viz_img = mark_boundaries(label_viz_img, label_img, (1, 0, 0))
label_viz_img = (label_viz_img * 255).astype(np.uint8)
label_viz_msg = bridge.cv2_to_imgmsg(label_viz_img, encoding='rgb8')
label_viz_msg.header = img_msg.header
self.pub_label_viz.publish(label_viz_msg)
# publish mask
if self._publish_mask:
bg_mask = (label_img == 0)
fg_mask = ~bg_mask
bg_mask = (bg_mask * 255).astype(np.uint8)
fg_mask = (fg_mask * 255).astype(np.uint8)
fg_mask_msg = bridge.cv2_to_imgmsg(fg_mask, encoding='mono8')
fg_mask_msg.header = img_msg.header
bg_mask_msg = bridge.cv2_to_imgmsg(bg_mask, encoding='mono8')
bg_mask_msg.header = img_msg.header
self.pub_fg_mask.publish(fg_mask_msg)
self.pub_bg_mask.publish(bg_mask_msg)
def explain(model, img, topLabels, numSamples, numFeatures, hideRest, hideColor, positiveOnly):
img, oldImg = transform_img_fn(img)
img = img*(1./255)
prediction = model.predict(img)
explainer = lime_image.LimeImageExplainer()
img = np.squeeze(img)
explanation = explainer.explain_instance(img, model.predict, top_labels=topLabels, hide_color=hideColor, num_samples=numSamples)
temp, mask = explanation.get_image_and_mask(getTopPrediction(prediction[0]), positive_only=positiveOnly, num_features=numFeatures, hide_rest=hideRest)
tempMask = mask * 255
temp = Image.fromarray(np.uint8(tempMask))
temp = temp.resize((oldImg.width, oldImg.height))
temp = image.img_to_array(temp)
temp = temp * 1./255
temp = temp.astype(np.int64)
temp = np.squeeze(temp)
oldImgArr = image.img_to_array(oldImg)
oldImgArr = oldImgArr * (1./255)
oldImgArr = oldImgArr.astype(np.float64)
imgExplained = mark_boundaries(oldImgArr, temp)
imgFinal = np.uint8(imgExplained*255)
img = Image.fromarray(imgFinal)
imgByteArr = io.BytesIO()
img.save(imgByteArr, format='JPEG')
imgByteArr = imgByteArr.getvalue()
return imgByteArr
def createFigure4(list_file, list_param, corpus_meta, prob_topic_doc, segment_dir, n_topic, output_dir):
for file_item in list_file:
print file_item
for topic in range(n_topic):
print topic
fig = plt.figure()
img = img_as_float(io.imread(img_dir+file_item+'.ppm'))
ax1 = fig.add_subplot(4,4, 1, axisbg='grey')
ax1.set_xticks(()), ax1.set_yticks(())
ax1.imshow(img)
index=2
for param in list_param:
if 'slic' in param:
# print 'test', index
# print corpus_meta[0][1].split('-')[0]
segment_in_file = [item_corpus for item_corpus in corpus_meta if file_item == item_corpus[1].split('-')[0]]
print len(segment_in_file)
segments_res = csv2Array(segment_dir+'/'+file_item+'/'+file_item+'-'+param+'.sup')
img = img_as_float(io.imread(img_dir+file_item+'.ppm'))
output = np.zeros( (len(img), len(img[0])) )
for segment in segment_in_file:
# print prob_topic_doc[int(segment[0])][topic]
output[segments_res == int(segment[2])] = prob_topic_doc[int(segment[0])][topic]
output = mark_boundaries(output, segments_res)
ax1 = fig.add_subplot(4,4, index, axisbg='grey')
# ax1 = fig.add_subplot(5,10, index, axisbg='grey')
ax1.set_xticks(()), ax1.set_yticks(())
ax1.imshow(output)
index += 1
ensure_path(output_dir+'/'+file_item+'/')
plt.savefig(output_dir+'/'+file_item+'/topic-'+str(topic)+'-'+file_item+'.pdf')
plt.clf()
plt.close()
def remove_background(self): L_b = 3 self.i_original = self.i_original[self.sub[1]:self.sub[3],self.sub[0]:self.sub[2],] segments = slic(self.i_original, n_segments=2, compactness=0.1,enforce_connectivity=False) # segments += 1 temp = self.i_original if sum(sum(segments[:5,:5])) > 10: for ii in range(0,3): temp[:,:,ii] = (np.ones([self.i_original.shape[0],self.i_original.shape[1]])-segments)*self.i_original[:,:,ii] #Haut, Bas, Droite, Gauche temp[:L_b,:,ii] = 0 temp[self.i_original.shape[0]-L_b-1:self.i_original.shape[0]-1,:,ii]=0 temp[:,self.i_original.shape[1]-L_b-1:self.i_original.shape[1]-1,ii] = 0 temp[:,:L_b,ii] = 0 else: for ii in range(0,3): temp[:,:,ii] = segments*self.i_original[:,:,ii] # print "else" #Haut, Bas, Droite, Gauche temp[:L_b,:,ii] = 0 temp[self.i_original.shape[0]-L_b-1:self.i_original.shape[0]-1,:,ii]=0 temp[:,self.i_original.shape[1]-L_b-1:self.i_original.shape[1]-1,ii] = 0 temp[:,:L_b,ii] = 0 # pdb.set_trace() fig, ax = plt.subplots(1, 1) ax.imshow(mark_boundaries(self.i_original,segments)) ax.imshow(temp) plt.show() p2, p98 = np.percentile(temp, (2, 98)) temp = exposure.rescale_intensity(temp, in_range=(p2, p98)) return temp
def plot(self):
self.ax.cla()
if self.segment_label is None:
self.ax.imshow(self.new_image)
else:
boundary_image = segmentation.mark_boundaries(self.new_image, self.segment_label)
self.ax.imshow(boundary_image)
self.ax.imshow(self.background_mask.astype(float), alpha=0.3)
mask = 1 - self.background_mask
content_on_canvas = np.zeros(list(mask.shape) + [4], np.uint8) # Prepare the canvas
for stitch_image in self.stitch_images:
content = stitch_image['content']
x = stitch_image['x']
y = stitch_image['y']
size = stitch_image['size']
print(x, y, size)
resized = misc.imresize(content, size) # Resize the image
# Compute the intersecting length between canvas and content
x_copy_width = mask.shape[1] - stitch_image['x']
if resized.shape[1] < x_copy_width:
x_copy_width = resized.shape[1]
y_copy_width = mask.shape[0] - stitch_image['y']
if resized.shape[0] < y_copy_width:
y_copy_width = resized.shape[0]
print(x_copy_width, y_copy_width)
# Copy content onto canvas and apply mask
new_content = np.zeros(list(mask.shape) + [4], np.uint8)
new_content[y:y+y_copy_width, x:x+x_copy_width, :] = resized[0:y_copy_width, 0:x_copy_width, :]
content_on_canvas = self.merge(content_on_canvas, new_content)
content_on_canvas[:, :, 3] = np.multiply(mask, content_on_canvas[:, :, 3])
self.ax.imshow(content_on_canvas)
self.ax.figure.canvas.draw()
def visualize_pascal(plot_probabilities=False):
data = load_pascal('val')
ds = PascalSegmentation()
for x, y, f, sps in zip(data.X, data.Y, data.file_names, data.superpixels):
fig, ax = plt.subplots(2, 3)
ax = ax.ravel()
image = ds.get_image(f)
y_pixel = ds.get_ground_truth(f)
x_raw = load_kraehenbuehl(f)
boundary_image = mark_boundaries(image, sps)
ax[0].imshow(image)
ax[1].imshow(y_pixel, cmap=ds.cmap)
ax[2].imshow(boundary_image)
ax[3].imshow(np.argmax(x_raw, axis=-1), cmap=ds.cmap, vmin=0, vmax=256)
ax[4].imshow(y[sps], cmap=ds.cmap, vmin=0, vmax=256)
ax[5].imshow(np.argmax(x, axis=-1)[sps], cmap=ds.cmap, vmin=0,
vmax=256)
for a in ax:
a.set_xticks(())
a.set_yticks(())
plt.savefig("figures_pascal_val/%s.png" % f, bbox_inches='tight')
plt.close()
if plot_probabilities:
fig, ax = plt.subplots(3, 7)
for k in range(21):
ax.ravel()[k].matshow(x[:, :, k], vmin=0, vmax=1)
for a in ax.ravel():
a.set_xticks(())
a.set_yticks(())
plt.savefig("figures_pascal_val/%s_prob.png" % f,
bbox_inches='tight')
plt.close()
tracer()
def update_slic(self):
sec = self.auto_submasks_gscene.active_section
t = time.time()
self.slic_labelmaps[sec] = slic(self.contrast_stretched_images[sec].astype(np.float),
sigma=SLIC_SIGMA, compactness=SLIC_COMPACTNESS,
n_segments=SLIC_N_SEGMENTS, multichannel=False, max_iter=SLIC_MAXITER)
sys.stderr.write('SLIC: %.2f seconds.\n' % (time.time() - t)) # 10 seconds, iter=100, nseg=1000;
self.slic_boundary_images[sec] = img_as_ubyte(mark_boundaries(self.contrast_stretched_images[sec],
label_img=self.slic_labelmaps[sec],
background_label=-1, color=(1,0,0)))
self.slic_image_feeder.set_image(sec=sec, numpy_image=self.slic_boundary_images[sec])
self.gscene_slic.update_image(sec=sec)
####
# self.ncut_labelmaps[sec] = normalized_cut_superpixels(self.contrast_stretched_images[sec], self.slic_labelmaps[sec])
self.ncut_labelmaps[sec] = self.slic_labelmaps[sec]
self.sp_dissim_maps[sec] = compute_sp_dissims_to_border(self.contrast_stretched_images[sec], self.ncut_labelmaps[sec])
# self.sp_dissim_maps[sec] = compute_sp_dissims_to_border(self.thresholded_images[sec], self.ncut_labelmaps[sec])
# self.border_dissim_images[sec] = generate_dissim_viz(self.sp_dissim_maps[sec], self.ncut_labelmaps[sec])
# self.dissim_image_feeder.set_image(sec=sec, numpy_image=self.border_dissim_images[sec])
# self.gscene_dissimmap.update_image(sec=sec)
self.selected_dissim_thresholds[sec] = determine_dissim_threshold(self.sp_dissim_maps[sec], self.ncut_labelmaps[sec])
self.ui.slider_dissimThresh.setValue(int(self.selected_dissim_thresholds[sec]/0.01))
######################################################
self.update_init_submasks_image()
def main():
filename = 'mesquitesFloat.png'
img = io.imread(filename)
#CONVERSIONS:
#convert to lab color space
#img = skimage.color.rgb2lab(img)
#img = img_as_float(img)
#io.imsave('mesquitesFloat.png', img)
#print(img.shape)
plt.figure(1)
plt.imshow(img, cmap='gray')
plt.axis('off')
# loop over the number of segments
for numSegments in (100, 200, 300):
# apply SLIC and extract (approximately) the supplied number
# of segments
segments = slic(img, n_segments = numSegments, sigma = 1)
# show the output of SLIC
fig = plt.figure("Superpixels of -- %d segments" % (numSegments))
subplot = fig.add_subplot(1, 1, 1)
subplot.imshow(mark_boundaries(img, segments))
plt.axis("off")
plt.show()
def generateImageWithSuperPixelBoundaries(image, segmentationMask):
# Function returns an image with superpixel boundaries displayed as lines. It is assumed that the image was the source for the segmentation mask.
# See [http://scikit-image.org/docs/dev/api/skimage.segmentation.html?highlight=slic#skimage.segmentation.mark_boundaries]
# Function signature: skimage.segmentation.mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=(0, 0, 0))
superPixelImage = mark_boundaries(image, segmentationMask)
return superPixelImage
def showPredictionOutput(self):
image_withText = self.image.copy()
# show the output of the prediction with text
for (i, segVal) in enumerate(np.unique(self.segments)):
CORD = self.centerList[i]
if self.predictionList[i] == "other":
colorFont = (255, 0, 0) # "Blue color for other"
else:
colorFont = (0, 0, 255) # "Red color for ocean"
#textOrg = CORD
#textOrg = tuple(numpy.subtract((10, 10), (4, 4)))
testOrg = (40,40) # need this for the if statment bellow
# for some yet unknown reason CORD does sometime contain somthing like this [[[210 209]] [[205 213]] ...]
# the following if statemnet is to not get a error becouse of this
if len(CORD) == len(testOrg):
#textOrg = tuple(np.subtract(CORD, (12, 0)))
textOrg = CORD
cv2.putText(self.image, self.predictionList[i], textOrg, cv2.FONT_HERSHEY_SIMPLEX, 0.1, colorFont, 3)
markedImage = mark_boundaries(img_as_float(cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)), self.segments)
else:
pass
cv2.imshow("segmented image", markedImage)
cv2.waitKey(0)
def superpixels(image):
""" given an input image, create super pixels on it
"""
# we could try to limit the problem of holes in boundary by first over segmenting the image
import matplotlib.pyplot as plt
from skimage.segmentation import felzenszwalb, slic, quickshift
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float
jac_float = img_as_float(image)
plt.imshow(jac_float)
#segments_fz = felzenszwalb(jac_float, scale=100, sigma=0.5, min_size=50)
segments_slic = slic(jac_float, n_segments=600, compactness=0.01, sigma=0.001
#, multichannel = False
, max_iter=50)
fig, ax = plt.subplots(1, 1, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
fig.set_size_inches(8, 3, forward=True)
fig.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)
#ax[0].imshow(mark_boundaries(jac, segments_fz))
#ax[0].set_title("Felzenszwalbs's method")
ax.imshow(mark_boundaries(jac_float, segments_slic))
ax.set_title("SLIC")
return segments_slic
def intensity_range2superpixels(im, superpixels, intMinT=0.95, intMaxT=1.05, debug=False, intMin=0, intMax=255):#, fromInt=0, toInt=255):
superseeds = np.zeros_like(superpixels)
#if not intMin and not intMax:
# hist, bins = skexp.histogram(im)
#
# #zeroing values that are lower/higher than fromInt/toInt
# toLow = np.where(bins < fromInt)
# hist[toLow] = 0
# toHigh = np.where(bins > toInt)
# hist[toHigh] = 0
#
# max_peakIdx = hist.argmax()
# intMin = intMinT * bins[max_peakIdx]
# intMax = intMaxT * bins[max_peakIdx]
sp_means = np.zeros(superpixels.max()+1)
for sp in range(superpixels.max()+1):
values = im[np.where(superpixels==sp)]
mean = np.mean(values)
sp_means[sp] = mean
idxs = np.argwhere(np.logical_and(sp_means>=intMin, sp_means<=intMax))
for i in idxs:
superseeds = np.where(superpixels==i[0], 1, superseeds)
if debug:
plt.figure(), plt.gray()
plt.imshow(im), plt.hold(True), plt.imshow(mark_boundaries(im, superseeds, color=(1,0,0)))
plt.axis('image')
plt.show()
return superseeds
def main():
from pascal.pascal_helpers import load_pascal
from datasets.pascal import PascalSegmentation
from utils import add_edges
from scipy.misc import imsave
from skimage.segmentation import mark_boundaries
ds = PascalSegmentation()
data = load_pascal("train1")
data = add_edges(data, independent=False)
# X, Y, image_names, images, all_superpixels = load_data(
# "train", independent=False)
for x, name, sps in zip(data.X, data.file_names, data.superpixels):
segments = get_km_segments(x, ds.get_image(name), sps, n_segments=25)
boundary_image = mark_boundaries(mark_boundaries(ds.get_image(name), sps), segments[sps], color=[1, 0, 0])
imsave("hierarchy_sp_own_25/%s.png" % name, boundary_image)
def fig_label_segments(fig, image, segments, label):
labels, _ = ndimage.label(segments)
image_label_overlay=label2rgb(labels, image=image)
image_label_overlay=mark_boundaries(image, segments)
fig.set_title(label)
fig.axis('off')
fig.imshow(image_label_overlay)
print ("%s number of segments: %d" % (label, len(np.unique(segments))))
def showPlots(im_name, image, numSuperpixels, superpixels):
# show sample test mean image
fig = plt.figure("Superpixels -- %d im_sp" % (numSuperpixels))
ax = fig.add_subplot(1, 1, 1)
ax.imshow(mark_boundaries(image, superpixels))
#bugged
#ax.imsave("output/000%d.png" % (im_name),mark_boundaries(image, superpixels))
plt.axis("off")
plt.show()
def visualize_segments():
pascal = PascalSegmentation()
train_files = pascal.get_split()
for image_file in train_files:
print(image_file)
image = pascal.get_image(image_file)
segments, superpixels = superpixels_segments(image_file)
new_regions, correspondences = merge_small_sp(image, superpixels)
clean_regions = morphological_clean_sp(image, new_regions, 4)
new_regions, correspondences = merge_small_sp(image, superpixels)
clean_regions = morphological_clean_sp(image, new_regions, 4)
marked = mark_boundaries(image, clean_regions)
edges = create_segment_sp_graph(segments, clean_regions)
edges = np.array(edges)
n_segments = segments.shape[2]
segment_centers = [regionprops(segments.astype(np.int)[:, :, i],
['Centroid'])[0]['Centroid'] for i in
range(n_segments)]
segment_centers = np.vstack(segment_centers)[:, ::-1]
superpixel_centers = get_superpixel_centers(clean_regions)
grr = min(n_segments, 10)
fig, axes = plt.subplots(3, grr // 3, figsize=(30, 30))
for i, ax in enumerate(axes.ravel()):
ax.imshow(mark_boundaries(marked, segments[:, :, i], (1, 0, 0)))
ax.scatter(segment_centers[:, 0], segment_centers[:, 1],
color='red')
ax.scatter(superpixel_centers[:, 0], superpixel_centers[:, 1],
color='blue')
this_edges = edges[edges[:, 1] == i]
for edge in this_edges:
ax.plot([superpixel_centers[edge[0]][0],
segment_centers[edge[1]][0]],
[superpixel_centers[edge[0]][1],
segment_centers[edge[1]][1]], c='black')
ax.set_xlim(0, superpixels.shape[1])
ax.set_ylim(superpixels.shape[0], 0)
ax.set_xticks(())
ax.set_yticks(())
#plt.show()
#imsave("segments_test/%s.png" % image_file, marked)
plt.savefig("segments_test/%s.png" % image_file)
plt.close()
def visualise_mask(image, mask):
output = skseg.mark_boundaries(image, mask)
rng = output.max()
alpha = 0.75
mask_color = [0.5, 0, 0]
for c_idx in range(output.shape[2]):
output[..., c_idx] = np.where(mask != 0,
output[..., c_idx],
alpha*mask_color[c_idx] + (1.0-alpha)*output[..., c_idx])
return output
def partitioning(self):
self.clean_colored_pixel()
self.superpixel_dbscan(12)
#figure pour montrer l'effet du clustering
self.fig_cluster = plt.figure('segmentation et clustering')
#on y lie la fonction qui permet de faire le coloriage
self.cid2 = self.fig_cluster.canvas.mpl_connect('button_press_event', self.onmouseclicked)
self.affichage(mark_boundaries(self.img,self.clusterized))
def slicSegmentation(image, num_segments=600, sigma=5):
C_8U = rgb(image)
C_32F = to32F(C_8U)
segments = slic(C_32F, n_segments=num_segments, sigma=sigma)
fig = plt.figure("Superpixels -- %d segments" % (num_segments))
ax = fig.add_subplot(1, 1, 1)
ax.imshow(mark_boundaries(C_32F, segments))
plt.axis("off")
plt.show()
def overlay_mask_base_image(self):
""" Creates a new image with an overlay of the mask
over the base image"""
x0, y0, x1, y1 = self.clip
self.base_w_mask = mark_boundaries(self.base_image[x0:x1, y0:y1],
img_as_uint(self.mask),
color=(1, 0, 1),
outline_color=None)
def plotLabel(img, labeledImgT, outStr=''):
fig, ax = plt.subplots(1, 2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'},
figsize=(14, 6))
ax[0].imshow(mark_boundaries(img, labeledImgT))
ax[1].imshow(labeledImgT, cmap=cm.cubehelix)
ax[0].set_title("Classification based on training set of multiple images")
for a in ax:
a.set_xticks(())
a.set_yticks(())
plt.savefig('./labelledImage'+outStr+'.png')
def overlay_mask_optional_image(self):
"""Creates a new image with an overlay of the mask over the fluor
image"""
optional_image = color.rgb2gray(self.optional_image)
optional_image = exposure.rescale_intensity(optional_image)
optional_image = img_as_float(optional_image)
self.optional_w_mask = mark_boundaries(optional_image, img_as_uint(
self.mask), color=(1, 0, 1), outline_color=None)
print(img_path)
img = imread(img_path)
path, img_name = os.path.split(img_path)
# segments_EM = EM_method(img_path=img_path, sp_met='felzenszwalb', num_cuts=3, EM_iter=4, K=100)
segments_EM = EM_method(img_path=img_path,
sp_met='felzenszwalb',
num_cuts=3,
EM_iter=4,
K=100,
dist_hist=True)
# save segments label & boundry
cv2.imwrite(join(label_dir, img_name), np.uint64(segments_EM))
boundary = segmentation.mark_boundaries(img, segments_EM, (1, 0, 0))
imsave(join(boundary_dir, img_name), boundary)
# save segments label & boundry
# cv2.imwrite(join(label_dir, img_name), np.uint64(segments))
# boundary = segmentation.mark_boundaries(img, segments, (1, 0, 0))
# imsave(join(boundary_dir, img_name), boundary)
if show_segmentation:
plt.figure()
plt.imshow(boundary)
plt.show()
if (num > 20):
break
def write_predictions(self, output):
output_dir = output + '.offset'
os.makedirs(output_dir, exist_ok=True)
LOG.info('\nWriting offset predictions to {}'.format(output_dir))
for key, value in self.offset_vis.items():
if value is None:
continue
filename = os.path.join(output_dir, str(key) + '.png')
image = value.astype(np.uint8)
for bbox in self.bbox_vis[key]:
pt1 = (int(bbox[0]), int(bbox[1]))
pt2 = (int(bbox[0]) + int(bbox[2]),
int(bbox[1]) + int(bbox[3]))
cv2.rectangle(image, pt1, pt2, (0, 0, 255))
pred = Image.fromarray(image)
pred.save(filename)
output_dir = output + '.human'
os.makedirs(output_dir, exist_ok=True)
LOG.info('\nWriting human segmentation predictions to {}'.format(
output_dir))
for key, value in self.human_vis.items():
if value is None:
continue
filename = os.path.join(output_dir, str(key) + '.png')
image = value.astype(np.uint8)
pred = Image.fromarray(image)
pred.putpalette(palette)
pred.save(filename)
ins_output_dir = output + '.instance'
os.makedirs(ins_output_dir, exist_ok=True)
LOG.info('\nWriting instance parsing predictions to {}'.format(
ins_output_dir))
for key, value in self.instance_vis.items():
if value is None:
continue
filename = os.path.join(ins_output_dir, str(key) + '.png')
image = value.astype(np.uint8)
pred = Image.fromarray(image)
pred.putpalette(palette)
pred.save(filename)
# save confidence
conf_file = open(os.path.join(ins_output_dir,
str(key) + '.txt'), 'w')
confs = self.confs_vis[key]
for conf in confs:
conf_file.write('{} {}\n'.format(conf[0], conf[1]))
gt_ins_output_dir = output + '.instance.gt'
os.makedirs(gt_ins_output_dir, exist_ok=True)
LOG.info('\nWriting instance parsing predictions to {}'.format(
gt_ins_output_dir))
for key, value in self.gt_instance_vis.items():
if value is None:
continue
filename = os.path.join(gt_ins_output_dir, str(key) + '.png')
image = value.astype(np.uint8)
pred = Image.fromarray(image)
pred.putpalette(palette)
pred.save(filename)
# save confidence
conf_file = open(
os.path.join(gt_ins_output_dir,
str(key) + '.txt'), 'w')
confs = self.gt_confs_vis[key]
for conf in confs:
conf_file.write('{} {} {}\n'.format(conf[0], conf[1], conf[2]))
output_dir = output + '.superpixel'
os.makedirs(output_dir, exist_ok=True)
LOG.info('\nWriting superpixel predictions to {}'.format(output_dir))
for key, value in self.superpixel_vis.items():
if value is None:
continue
filename = os.path.join(output_dir, str(key) + '.png')
pred = mark_boundaries(value[0][:, :, ::-1].astype(np.float),
value[1])
cv2.imwrite(filename, pred)
self.ins_output_dir = ins_output_dir
self.gt_ins_output_dir = gt_ins_output_dir
def explain_ddsm():
print("Entering the explain ddsm ..... ")
global v1model
global v2model
filestr = request.files['file'].read()
model_ver = request.form['model_ver']
print("Explain ddsm")
if (model_ver == 'v1' and v1model is None):
v1model = build_model(model_ver)
elif (model_ver == 'v2' and v2model is None):
v2model = build_model(model_ver)
img = decodeImage(filestr)
h, w, c = img.shape
print("----------------------", img)
image_array = enhance_images(img)
image_array = image_array / 255.
image_array = resize(image_array, (224, 224, 3))
v = np.expand_dims(image_array, axis=0)
imagenet_mean = np.array([0.449])
imagenet_std = np.array([0.226])
batch_x_non = (v - imagenet_mean) / imagenet_std
print(batch_x_non.shape)
explainer = lime_image.LimeImageExplainer(
feature_selection='lasso_path'
) #object to explain predictions on Image data.
if (model_ver == 'v1'):
model = v1model
else:
model = v2model
explanation_non_covid = explainer.explain_instance(batch_x_non[0],
model.predict,
top_labels=5,
hide_color=0,
num_samples=400)
print('Explaination is obtained!')
temp_non_1, mask_non_1 = explanation_non_covid.get_image_and_mask(
explanation_non_covid.top_labels[0],
positive_only=True,
num_features=10,
hide_rest=True)
temp_non_2, mask_non_2 = explanation_non_covid.get_image_and_mask(
explanation_non_covid.top_labels[0],
positive_only=False,
num_features=10,
hide_rest=False)
print("the prediction for non covid", explanation_non_covid.top_labels[0])
class_names = [
'Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration', 'Mass',
'Nodule', 'Pneumonia', 'Pneumothorax', 'Consolidation', 'Edema',
'Emphysema', 'Fibrosis', 'Pleural_Thickening', 'Hernia', 'Covid'
]
print("Predicted class: ",
class_names[explanation_non_covid.top_labels[0]])
exp_image = cv2.cvtColor(
np.asarray(
mark_boundaries(
((temp_non_2 * imagenet_std) + imagenet_mean) * 255,
mask_non_2), 'uint8'), cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (w, h))
img = Image.fromarray(exp_image.astype("uint8"))
rawBytes = io.BytesIO()
img.save(rawBytes, "JPEG")
rawBytes.seek(0)
img_base64 = base64.b64encode(rawBytes.read())
return jsonify({'status': str(img_base64)})
sp = SP(img, step, nc)
sp.__init__(img, step, nc)
#print(r)
sp.generateSuperPixels()
r = sp.createConnectivity()
#cv2.imshow("cluster", slic.clusters.astype(np.uint8))
#slic.displayContours()
sp.displayContours((252, 0, 0))
#sp.getSPHCsegments(contours,img)
#cv2.imshow("superpixels", img)
SPHCsegm_grid = getSPHCsegments(r, imlab)
plt.imshow(img)
plt.show()
fig = plt.figure("%d Segments Merged" % 400)
ax = fig.add_subplot(1, 1, 1)
ax.imshow(mark_boundaries(imge, SPHCsegm_grid))
plt.axis("off")
plt.show()
#cv2.imshow("color",color)
#cv2.waitKey(0)
#cv2.imshow("SLICimg.jpg", slic.img)
#plt.imshow(SPHCsegm_grid)
#plt.show()
#cv2.waitKey(0)
for filename in file_list:
# load the image and convert it to a floating point data type
image = img_as_float(io.imread(os.path.join(folder,filename)))
# loop over the number of segments
numSegments = [100, 200, 300]
# apply SLIC and extract (approximately) the supplied number
# of segments
segments = slic(image, n_segments = numSegments[1], sigma = 5)
for (i, segVal) in enumerate(np.unique(segments)):
mask = np.zeros(image.shape[:2], dtype = "uint8")
mask[segments == segVal] = 255
_, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
#pts = np.where((segments==segVal))
for pt in kp:
if cv2.pointPolygonTest(contours[0], pt, False):
fg.append(segVal)
# show the output of SLIC
#fig = plt.figure("Superpixels -- %d segments" % (numSegments))
#ax = fig.add_subplot(1, 1, 1)
cv2.imshow('re',mark_boundaries(image, segments))
#plt.axis("off")
cv2.waitKey(0)
# show the plots
#plt.show()
def checkSlic():
data = loadData()
# Error List
error = []
for idx in range(159, 160):
# Display
print('Processing Image #', idx + 1, sep='')
# Load Image and Ground Truth Image as Float
img = img_as_float(io.imread('data/images/' + str(idx + 1) + '.png'))
img_gt = img_as_float(
io.imread('data/gt_images/' + str(idx + 1) + '.png'))
# Perform SLIC
img_seg = slic(img,
n_segments=1000,
compactness=8,
convert2lab=True,
min_size_factor=0.3)
img_sample = np.zeros(img_gt.shape[:-1])
img_data = data[np.where(data[:, 65] == (idx + 1))]
big = np.amax(img_seg)
roi = (img_seg == big)
roi_idx = np.nonzero(roi)
biglabel = img_gt[int(np.mean(roi_idx[0])),
int(np.mean(roi_idx[1])), 2]
for row in range(img_gt.shape[0]):
for col in range(img_gt.shape[1]):
label = img_seg[row, col]
if label == big:
img_sample[row, col] == biglabel
elif img_data[label][64] == 1:
img_sample[row, col] = 1
curerror = sum(sum(
img_sample != img_gt[:, :, 2])) / img_gt.shape[0] / img_gt.shape[1]
error.append(curerror)
plt.figure(1)
fig1 = plt.imshow(mark_boundaries(img, img_seg))
fig1.axes.get_xaxis().set_visible(False)
fig1.axes.get_yaxis().set_visible(False)
plt.figure(2)
fig2 = plt.imshow(mark_boundaries(img_gt, img_seg))
fig2.axes.get_xaxis().set_visible(False)
fig2.axes.get_yaxis().set_visible(False)
plt.figure(3)
fig2 = plt.imshow(img)
fig2.axes.get_xaxis().set_visible(False)
fig2.axes.get_yaxis().set_visible(False)
plt.figure(4)
fig2 = plt.imshow(img_gt)
fig2.axes.get_xaxis().set_visible(False)
fig2.axes.get_yaxis().set_visible(False)
plt.figure(5)
fig2 = plt.imshow(img_sample)
fig2.axes.get_xaxis().set_visible(False)
fig2.axes.get_yaxis().set_visible(False)
return error
fname = 'images/' + name + '_image.npy'
image = np.load(fname)
fname = 'images/' + name + '_seg.npy'
segments = np.load(fname)
return image, segments
def save_results(name, labelled_image):
np.save('labelled/' + name + '_labels', labelled_image)
if __name__ == "__main__":
# Load image to label
name = 'DJI_0820'
image, segments = load_image_segments(name)
labelled_image = np.full(image[:, :, 0].shape, np.nan)
# Set up plot
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
base_im = ax.imshow(
mark_boundaries(image, segments, color=(1, 0, 0), mode='thick'))
palette = copy(plt.cm.viridis)
palette.set_bad(alpha=0.0)
im = ax.imshow(labelled_image, alpha=0.5, vmin=1, vmax=2, cmap=palette)
plt.show(block=False)
# Label image, updating plot until user quits
label_image()
#get data from bars
numSegments = cv2.getTrackbarPos('Number of Segments', 'slic')
numSigma = cv2.getTrackbarPos('Sigma', 'slic')
#segment
start_time = time.time() # save curr time to report duration
segments = slic(img.astype(np.float64) / 256,
n_segments=numSegments,
sigma=numSigma)
duration = time.time() - start_time # calculate time elapsed
print("slic elapsed: ", duration)
#mark segments in image
start_time = time.time() # save curr time to report duration
img = mark_boundaries(img, segments)
duration = time.time() - start_time # calculate time elapsed
print("mark boundaries elapsed: ", duration)
#make a mask
mask = np.zeros(img.shape)
mask = mark_boundaries(mask, segments)
if display_mode == 0:
cv2.imshow('slic', img)
else:
cv2.imshow('slic', mask)
ch = cv2.waitKey(1)
if ch == 27:
break
compact = [15, 25, 35, 45, 25, 25, 25, 25, 25, 25, 25, 25]
segment = [250, 250, 250, 250, 50, 150, 250, 350, 250, 250, 250, 250]
thresh = [45, 45, 45, 45, 45, 45, 45, 45, 25, 35, 45, 55]
for j in range(12):
# Region Adjacency Graph
labels = segmentation.slic(img, compactness=compact[j], n_segments=segment[j], start_label=1)
g = graph.rag_mean_color(img, labels)
labels2 = graph.merge_hierarchical(labels, g, thresh=thresh[j], rag_copy=False,
in_place_merge=True,
merge_func=merge_mean_color,
weight_func=_weight_mean_color)
for ii in range(np.max(labels2)):
if np.logical_or(np.sum(labels2 == ii) > 1000, np.sum(labels2 == ii) < 150):
labels2[labels2 == ii] = 0
out = color.label2rgb(labels2, img, kind='avg', bg_label=0)
out = segmentation.mark_boundaries(out, labels2, (0, 0, 0))
out = np.uint8(out * 255)
centers = []
for ii in range(np.max(labels2) - 1):
xInd, yInd = np.where(labels2 == ii + 1)
if len(xInd) == 0:
continue
xCenter = int(np.mean(xInd))
yCenter = int(np.mean(yInd))
w = max(xInd) - min(xInd)
h = max(yInd) - min(yInd)
# New filters, based on sizes
# if w < 20 or w > 40:
# continue
def display_superpixels(rgb, superpixels):
fig = plt.figure("SuperPixels")
ax = fig.add_subplot(1, 1, 1)
ax.imshow(mark_boundaries(rgb, superpixels))
plt.axis("off")
plt.show()
im_val = []
im_val = 0.299 * im[:, :, 0] + 0.587 * im[:, :, 1] + 0.114 * im[:, :, 2]
im_val_temp = im_val
im_val = np.array(im_val).flatten()
#每个点的像素值（一维）
im_val = []
im_val = 0.299 * im[:, :, 0] + 0.587 * im[:, :, 1] + 0.114 * im[:, :, 2]
im_val = np.array(im_val).flatten()
# flez
labels = segmentation.felzenszwalb(im,
scale=args.scale,
sigma=0.8,
min_size=args.min_size)
boundary = segmentation.mark_boundaries(im, labels, (1, 0, 0))
# imsave('boundary_%d.png'%args.num_superpixels, boundary)
labels_temp = labels
# ave_position,red_average,green_average,blue_average,position = function.init_sp(im,labels)
labels = labels.reshape(im.shape[0] * im.shape[1]) #分割后每个超像素的Sk值
u_labels = np.unique(labels) #将Sk作为标签
l_inds = [] #每i行表示Si超像素中每个像素的编号
for i in range(len(u_labels)):
l_inds.append(np.where(labels == u_labels[i])[0])
mean_list = []
var_list = []
std_list = []
std_m_v = []
for i in range(len(l_inds)):
labels_per_sp = im_val[l_inds[i]]
'''
# load model
model = resnets_shift.resnet18(True).cuda()
optimizer = optimizers.optimfn(args.optim, model) # unused
model, _, _ = networktools.continue_train(model, optimizer,
args.eval_model_pth, True)
model.eval()
# generate dataset from points
iterator_val = GenerateIterator_eval(metadata)
# pass through dataset
pred_mask = np.zeros_like(labels)
with torch.no_grad():
for batch_it, (images, tile_ids) in enumerate(iterator_val):
images = images.cuda()
pred_ensemble = model(images)
pred_ensemble = torch.argmax(pred_ensemble, 1).cpu().numpy()
for tj, tile_id in enumerate(tile_ids.numpy()):
pred_mask[metadata[tile_id]
['foreground_indices']] = pred_ensemble[tj]
pred_mask_rgb = np.eye(4)[pred_mask][..., 1:]
pred_mask_rgb = Image.fromarray(pred_mask_rgb.astype(np.uint8) * 255)
pred_mask_rgb = pred_mask_rgb.resize((x // us, y // us))
pred_mask_rgb.save('slic_out_mask.png')
slic_out = mark_boundaries(image, labels, color=(0, 0, 0))
Image.fromarray((255 * slic_out).astype(np.uint8)).save('slic_out.png')
yp = patch_coords[1][j]
patch_pixels.append(image[xp, yp, 1])
all_patch_pixels.append(patch_pixels)
bins = 30
features = []
for i in range(0, len(all_patch_pixels)):
h_inter = np.histogram(all_patch_pixels[i], bins)
features.append(h_inter[1].tolist())
fake_class = (np.array(zerolistmaker(len(features))) * [2]).tolist()
p_label, accuracy, p_val = svm_predict(fake_class, features, model)
# p_lane_labels=np.where(np.asarray(p_label)==1)
p_lane_labels = np.where(np.asarray(p_val) > p_threshold)
p_lane_labels = p_lane_labels[0].tolist()
lane_segments = segments * 0
for i in range(0, len(p_lane_labels)):
x, y = (np.where(segments == p_lane_labels[i]))
lane_segments[x, y] = i + 1
fig = plt.figure("Superpixels")
ax = fig.add_subplot(1, 1, 1)
ax.imshow(mark_boundaries(image, lane_segments))
plt.axis("off")
plt.show()
centers, colors_hists, segments, neighbors = superpixels_histograms_neighbors(
img)
fg_segments, bg_segments = find_superpixels_under_marking(
img_marking, segments)
# get cumulative BG/FG histograms, before normalization
fg_cumulative_hist = cumulative_histogram_for_superpixels(
fg_segments, colors_hists)
bg_cumulative_hist = cumulative_histogram_for_superpixels(
bg_segments, colors_hists)
norm_hists = normalize_histograms(colors_hists)
graph_cut = do_graph_cut((fg_cumulative_hist, bg_cumulative_hist),
(fg_segments, bg_segments), norm_hists, neighbors)
plt.subplot(1, 2, 2), plt.xticks([]), plt.yticks([])
plt.title('segmentation')
segmask = pixels_for_segment_selection(segments, np.nonzero(graph_cut))
cv2.imwrite("img/output_segmentation.png", np.uint8(segmask * 255))
plt.imshow(segmask)
plt.subplot(1, 2, 1), plt.xticks([]), plt.yticks([])
img = mark_boundaries(img, segments)
img[img_marking[:, :, 0] != 255] = (1, 0, 0)
img[img_marking[:, :, 2] != 255] = (0, 0, 1)
plt.imshow(img)
plt.title("SLIC + markings")
plt.savefig("img/segmentation.png", bbox_inches='tight', dpi=96)
Segmentation contours
=====================
Visualize segmentation contours on original grayscale image.
"""
from skimage import data, segmentation
from skimage import filters
import matplotlib.pyplot as plt
import numpy as np
coins = data.coins()
mask = coins > filters.threshold_otsu(coins)
clean_border = segmentation.clear_border(mask).astype(np.int)
coins_edges = segmentation.mark_boundaries(coins, clean_border)
plt.figure(figsize=(8, 3.5))
plt.subplot(121)
plt.imshow(clean_border, cmap='gray')
plt.axis('off')
plt.subplot(122)
plt.imshow(coins_edges)
plt.axis('off')
plt.tight_layout()
plt.show()
import os
print(os.getcwd())
def segment(self):
n = array.array('i')
s = array.array('f')
#n.append(466)
#n.append(500)
n.append(100)
n.append(600)
n.append(566)
n.append(600)
n.append(666)
if pixels < 1000000:
s.append(1.7)
s.append(2.7)
s.append(3.3)
s.append(3.9)
s.append(4.9)
elif pixels < 7000000:
s.append(2.275 - 1.5 + 0.0000009166667 * pixels +
0.0000000000001083333 * pow(pixels, 2))
s.append(2.275 - 1 + 0.0000009166667 * pixels +
0.0000000000001083333 * pow(pixels, 2))
s.append(2.275 + 0.0000009166667 * pixels +
0.0000000000001083333 * pow(pixels, 2))
s.append(2.275 + 1 + 0.0000009166667 * pixels +
0.0000000000001083333 * pow(pixels, 2))
s.append(2.275 + 1.5 + 0.0000009166667 * pixels +
0.0000000000001083333 * pow(pixels, 2))
elif pixels > 7000000:
#s.append(10)
#s.append(13)
s.append(18)
s.append(8)
s.append(14)
s.append(15)
s.append(18)
self.imArray = []
for i in range(0, 5): # runs 3 times
#numSegments = 566
#mySigma = 6
ts = time.time()
st = datetime.datetime.fromtimestamp(ts).strftime(
'%Y-%m-%d_%H-%M-%S')
#self.out_files.append( str(n[i])+'seg_sigma'+str(s[i])+"_datetime"+st )
self.out_files.append(str(n[i]) + "_datetime" + st)
newIm = self.im
#cv2.imwrite("/home/madison/Documents/41x/IMG_SET6/" + self.out_files[i] +"_origin_elaine.jpg", newIm)
# apply SLIC and extract (approximately) the supplied number of segments
segments = slic(self.im, n_segments=n[i], sigma=s[i])
b = segments.tolist() # nested lists with same data, indices
#with open('/home/madison/Documents/41x/IMG_SET6/' + self.out_files[i] +'.json', 'w') as outfile:
# json.dump(b, outfile, indent=2)
#self.segm_lists.append(json.dumps(b, indent=2))
self.segm_lists.append(b)
# show the output of SLICs
fig = plt.figure("Superpixels -- %d segments" % (n[i]))
ax = fig.add_subplot(1, 1, 1)
newIm = mark_boundaries(
self.im, segments,
color=(52, 205,
195)) # fn normalises img bw 1 and 0 apparently
#ax.imshow(self.im)
plt.axis("off")
#cv2.waitKey(0)
newIm = (newIm * 255.0).astype('u1')
self.imArray.append(newIm)
def precision_recall():
im1 = cv2.imread("data/2-0.jpg", cv2.IMREAD_COLOR)
im2 = cv2.imread("data/2-15.jpg", cv2.IMREAD_COLOR)
im3 = cv2.imread("data/2-30.jpg", cv2.IMREAD_COLOR)
im4 = cv2.imread("data/2-45.jpg", cv2.IMREAD_COLOR)
gt = cv2.imread("gt2.jpg", 0)
# small image for test only
## im1 = cv2.imread("0.bmp", cv2.IMREAD_COLOR)
## im2 = cv2.imread("15.bmp", cv2.IMREAD_COLOR)
## im3 = cv2.imread("30.bmp", cv2.IMREAD_COLOR)
## im4 = cv2.imread("45.bmp", cv2.IMREAD_COLOR)
## gt = cv2.imread("gt2.jpg", 0)
thres = 3 # threshold of distance between edge pixel and g.t. edge pixel
# Multi-channel SLIC, our method
# print ("begin multi-channel slic")
# img = [im1, im2, im3, im4]
# L = mslic.mslic(img, 300, 5, 1, 5)
# L = regions.distinct_label(L)
# print ("after multi-channel slic, there are " + str(np.max(L)) + " superpixels")
# L = clusters.merge_tiny_regions(im1, L, 200)
# print ("after merging tiny regions, there are " + str(np.max(L)) + " superpixels left")
# bond_L = segmentation.find_boundaries(L)
# cv2.imwrite("after mslic.jpg", bond_L*255)
# print (performance.boundary_recall(gt, bond_L, thres))
# print (performance.precision(gt, bond_L, thres))
# L = clusters.merge_regions(img, L, gt, iteration = 100)
# cv2.imwrite("it=309.jpg", contour * 255)
# print (performance.boundary_recall(gt, bond_L, thres))
# print (performance.precision(gt, bond_L, thres))
# QuickShift method, ECCV 2008
## print "begin quickshift"
## ratio = 1.0
## kernel_size = 10
## max_dist = 5
## return_tree = False
## sigma = 3
## convert2lab = True
## random_seed = 42
## L_qs = segmentation.quickshift(max_im, ratio, kernel_size, max_dist, return_tree, sigma, convert2lab, random_seed)
## print "after Quickshift, there are " + str(np.max(L_qs)) + " superpixels"
## L = clusters.merge_tiny_regions(im1, L_qs, 200)
## print "after merging tiny regions, there are " + str(np.max(L)) + " superpixels left"
## bond_qs = segmentation.find_boundaries(L)
## print performance.boundary_recall(gt, bond_qs, thres)
## print performance.precision(gt, bond_qs, thres)
## img = [im1, im2, im3, im4]
## L = clusters.merge_regions(img, L, gt, iteration = 300)
## bond_L = segmentation.find_boundaries(L, mode='thick')
## print performance.boundary_recall(gt, bond_L, thres)
## print performance.precision(gt, bond_L, thres)
# Efficient graph-based image segmentation, IJCV 2004
# print "begin Graph-based segmentation"
# scale = 80
# sigma = 2
# min_size = 5
# L_fh = segmentation.felzenszwalb(max_im, scale, sigma, min_size)
# L = regions.distinct_label(L_fh)
# print "after Graph-based segmentation, there are " + str(np.max(L)) + " superpixels"
# contour = segmentation.mark_boundaries(max_im, L, (0, 0, 1))
# cv2.imwrite("contour_fh.jpg", contour*255)
# cv2.waitKey(0)
## L = clusters.merge_tiny_regions(im1, L, 200)
## print "after merging tiny regions, there are " + str(np.max(L)) + " superpixels left"
## bond_fh = segmentation.find_boundaries(L)
## print performance.boundary_recall(gt, bond_fh, thres)
## print performance.precision(gt, bond_fh, thres)
## img = [im1, im2, im3, im4]
## L = clusters.merge_regions(img, L, gt, iteration = 200)
## bond_L = segmentation.find_boundaries(L, mode='thick')
## print performance.boundary_recall(gt, bond_L, thres)
## print performance.precision(gt, bond_L, thres)
# SLIC superpixel method, TPAMI 2012
print("begin slic")
im = Image("data/2-0.jpg")
ns = 300
comp = 20
L_slic = segmentation.slic(im1,
n_segments=ns,
compactness=comp,
sigma=3,
convert2lab=True)
L = regions.distinct_label(L_slic)
L = clusters.merge_tiny_regions(im1, L, 500)
print("after merging tiny regions, there are " + str(np.max(L)) +
" superpixels left")
contour = segmentation.mark_boundaries(im1, L, (0, 0, 1))
# cv2.imshow("hehe", contour)
# cv2.waitKey(0)
print("start dbscan..............")
L = dbscan.merge_region(im, L, [14, 1])
print("after dbscan, there are " + str(np.max(L)) + " superpixels left")
contour = segmentation.mark_boundaries(im1, L, (0, 0, 1))
cv2.imshow("hehe", contour)
cv2.imwrite("feldspar_dbscan.jpg", contour * 255)
cv2.waitKey(0)
# Display images.
cv2.imwrite('grey.jpg',thresh)
# cv2.imshow("Floodfilled Image", im_out)
# cv2.waitKey(0)
# load the image and apply SLIC and extract (approximately)
# the supplied number of segments
image = cv2.imread('grey.jpg')
segments = slic(img_as_float(image), n_segments = 500, compactness=10,sigma = 5)
# show the output of SLIC
fig = plt.figure("Superpixels")
ax = fig.add_subplot(1, 1, 1)
ax.imshow(mark_boundaries(img_as_float(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), segments))
plt.axis("off")
plt.show()
counter_red = 0
lower_blue = np.array([110, 50, 50])
# loop over the unique segment values
for (i, segVal) in enumerate(np.unique(segments)):
# construct a mask for the segment
mask = np.zeros(image.shape[:2], dtype = "uint8")
mask[segments == segVal] = 125
print i
image_mask=cv2.bitwise_or(image, image, mask = mask)
def crazy_visual():
dataset = NYUSegmentation()
# load training data
data = load_nyu(n_sp=500)
data = add_edges(data)
for x, image_name, superpixels, y in zip(data.X, data.file_names,
data.superpixels, data.Y):
print(image_name)
if int(image_name) != 11:
continue
image = dataset.get_image(image_name)
plt.figure(figsize=(20, 20))
bounary_image = mark_boundaries(image, superpixels)
plt.imshow(bounary_image)
gridx, gridy = np.mgrid[:superpixels.shape[0], :superpixels.shape[1]]
edges = x[1]
points_normals = dataset.get_pointcloud_normals(image_name)
centers2d = get_superpixel_centers(superpixels)
centers3d = [
np.bincount(superpixels.ravel(), weights=c.ravel())
for c in points_normals[:, :, :3].reshape(-1, 3).T
]
centers3d = (np.vstack(centers3d) / np.bincount(superpixels.ravel())).T
sp_normals = get_sp_normals(points_normals[:, :, 3:], superpixels)
offset = centers3d[edges[:, 0]] - centers3d[edges[:, 1]]
offset = offset / np.sqrt(np.sum(offset**2, axis=1))[:, np.newaxis]
#mean_normal = (sp_normals[edges[:, 0]] + sp_normals[edges[:, 1]]) / 2.
mean_normal = sp_normals[edges[:, 0]]
#edge_features = np.arccos(np.abs((offset * mean_normal).sum(axis=1))) * 2. / np.pi
edge_features = 1 - np.abs((offset * mean_normal).sum(axis=1))
no_normals = (np.all(sp_normals[edges[:, 0]] == 0, axis=1) +
np.all(sp_normals[edges[:, 1]] == 0, axis=1))
edge_features[no_normals] = 0 # nan normals
if True:
coords = points_normals[:, :, :3].reshape(-1, 3)
perm = np.random.permutation(superpixels.max() + 1)
mv.points3d(coords[:, 0],
coords[:, 1],
coords[:, 2],
perm[superpixels.ravel()],
mode='point')
#mv.points3d(centers3d[:, 0], centers3d[:, 1], centers3d[:, 2], scale_factor=.04)
mv.quiver3d(centers3d[:, 0], centers3d[:, 1], centers3d[:, 2],
sp_normals[:, 0], sp_normals[:, 1], sp_normals[:, 2])
mv.show()
from IPython.core.debugger import Tracer
Tracer()()
for i, edge in enumerate(edges):
e0, e1 = edge
#color = (dataset.colors[y[e0]] + dataset.colors[y[e1]]) / (2. * 255.)
#f = edge_features[i]
#if f < 0:
#e0, e1 = e1, e0
#f = -f
#plt.arrow(centers[e0][0], centers[e0][1],
#centers[e1][0] - centers[e0][0], centers[e1][1] - centers[e0][1],
#width=f * 5
#)
color = "black"
plt.plot([centers2d[e0][0], centers2d[e1][0]],
[centers2d[e0][1], centers2d[e1][1]],
c=color,
linewidth=edge_features[i] * 5)
plt.scatter(centers2d[:, 0], centers2d[:, 1], s=100)
plt.tight_layout()
plt.xlim(0, superpixels.shape[1])
plt.ylim(superpixels.shape[0], 0)
plt.axis("off")
plt.savefig("figures/normal_relative/%s.png" % image_name,
bbox_inches="tight")
plt.close()
def create_segmented_image(image, segments):
segmented_image = mark_boundaries(img_as_float(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), segments)
return segmented_image
def interpret(self, data_, visualization=True, save_outdir=None):
if self.normlime_weights is None:
raise ValueError(
"Not find the correct precomputed NormLIME result. \n"
"\t Try to call compute_normlime_weights() first or load the correct path."
)
g_weights = self.preparation_normlime(data_)
lime_weights = self._lime.lime_interpreter.local_weights
if visualization or save_outdir is not None:
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
l = self.labels[0]
ln = l
if self.label_names is not None:
ln = self.label_names[l]
psize = 5
nrows = 4
weights_choices = [0.6, 0.7, 0.75, 0.8, 0.85]
nums_to_show = []
ncols = len(weights_choices)
plt.close()
f, axes = plt.subplots(nrows,
ncols,
figsize=(psize * ncols, psize * nrows))
for ax in axes.ravel():
ax.axis("off")
axes = axes.ravel()
axes[0].imshow(self.image)
prob_str = "{%.3f}" % (self.predicted_probability)
axes[0].set_title("label {}, proba: {}".format(ln, prob_str))
axes[1].imshow(
mark_boundaries(self.image,
self._lime.lime_interpreter.segments))
axes[1].set_title("superpixel segmentation")
# LIME visualization
for i, w in enumerate(weights_choices):
num_to_show = auto_choose_num_features_to_show(
self._lime.lime_interpreter, l, w)
nums_to_show.append(num_to_show)
temp, mask = self._lime.lime_interpreter.get_image_and_mask(
l,
positive_only=True,
hide_rest=False,
num_features=num_to_show)
axes[ncols + i].imshow(mark_boundaries(temp, mask))
axes[ncols + i].set_title(
"LIME: first {} superpixels".format(num_to_show))
# NormLIME visualization
self._lime.lime_interpreter.local_weights = g_weights
for i, num_to_show in enumerate(nums_to_show):
temp, mask = self._lime.lime_interpreter.get_image_and_mask(
l,
positive_only=True,
hide_rest=False,
num_features=num_to_show)
axes[ncols * 2 + i].imshow(mark_boundaries(temp, mask))
axes[ncols * 2 + i].set_title(
"NormLIME: first {} superpixels".format(num_to_show))
# NormLIME*LIME visualization
combined_weights = combine_normlime_and_lime(
lime_weights, g_weights)
self._lime.lime_interpreter.local_weights = combined_weights
for i, num_to_show in enumerate(nums_to_show):
temp, mask = self._lime.lime_interpreter.get_image_and_mask(
l,
positive_only=True,
hide_rest=False,
num_features=num_to_show)
axes[ncols * 3 + i].imshow(mark_boundaries(temp, mask))
axes[ncols * 3 + i].set_title(
"Combined: first {} superpixels".format(num_to_show))
self._lime.lime_interpreter.local_weights = lime_weights
if save_outdir is not None:
save_fig(data_, save_outdir, 'normlime', self.num_samples)
if visualization:
plt.show()
plt.subplot(1, 5, plot_index + 1)
plt.imshow(X_eval[good_index])
plt.title(' Predicted: {}, Actual: {} '.format(labels_index[cnn_pred[good_index][0]],
labels_index[y_eval[good_index]]), fontsize = 18)
plt.savefig('results/cnn/correctly_classified.png', dpi=400)
# lime explanation of correctly classified images
plt.figure(figsize=(50,10))
#shuffle(correctly_classified_indices)
for plot_index, good_index in enumerate(correctly_classified_indices[0:5]):
plt.subplot(1, 5, plot_index + 1)
explainer = lime_image.LimeImageExplainer()
explanation = explainer.explain_instance(X_eval[good_index], cnn_classifier.predict_classes, top_labels=2, hide_color=0, num_samples=1000)
temp, mask = explanation.get_image_and_mask(0, positive_only=False, num_features=1000, hide_rest=False)
#temp, mask = explanation.get_image_and_mask(0, positive_only=True, num_features=1000, hide_rest=True)
x = mark_boundaries(temp / 2 + 0.5, mask)
plt.imshow(x, interpolation='none')
plt.title(' Predicted: {}, Actual: {} '.format(labels_index[cnn_pred[good_index][0]],
labels_index[y_eval[good_index]]), fontsize = 18)
plt.savefig('results/cnn/lime_correctly_classified.png', dpi=400)
# misclassified images
plt.figure(figsize=(50,10))
#shuffle(misclassified_indices)
for plot_index, bad_index in enumerate(misclassified_indices[0:5]):
plt.subplot(1, 5, plot_index + 1)
plt.imshow(X_eval[bad_index])
plt.title(' Predicted: {}, Actual: {} '.format(labels_index[cnn_pred[bad_index][0]],
labels_index[y_eval[bad_index]]), fontsize = 18)
plt.savefig('results/cnn/mis_classified.png', dpi=400)
for i in range(0, heightL):
for j in range(0, widthL):
imgL3D[i, j, k] = 255 - imgL[i, j]
imgR3D[i, j, k] = 255 - imgR[i, j]
segL = felzenszwalb(imgL3D, scale=300, sigma=0.5, min_size=100)
segR = felzenszwalb(imgR3D, scale=300, sigma=0.5, min_size=100)
fig, ax = plt.subplots(2,
2,
figsize=(10, 10),
sharex=True,
sharey=True,
subplot_kw={'adjustable': 'box-forced'})
ax[0, 0].imshow(mark_boundaries(imgL3D, segL))
ax[0, 1].imshow(mark_boundaries(imgR3D, segR))
plt.tight_layout()
plt.show()
TCol = 10
TGrad = 2
#colour absolute difference
def Ctadc(x, y, d):
c = min(abs(imgL[y, x] * 1.0 - imgR[y, x - d] * 1.0), TCol)
return c
train_gen = make_image_gen(balanced_train_df)
train_x, train_y = next(train_gen)
print('x', train_x.shape, train_x.min(), train_x.max())
print('y', train_y.shape, train_y.min(), train_y.max())
# In[ ]:
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(30, 10))
batch_rgb = montage_rgb(train_x)
batch_seg = montage(train_y[:, :, :, 0])
ax1.imshow(batch_rgb)
ax1.set_title('Images')
ax2.imshow(batch_seg)
ax2.set_title('Segmentations')
ax3.imshow(mark_boundaries(batch_rgb, batch_seg.astype(int)))
ax3.set_title('Outlined Ships')
fig.savefig('overview.png')
# # Make the Validation Set
# In[ ]:
valid_x, valid_y = next(make_image_gen(valid_df, VALID_IMG_COUNT))
print(valid_x.shape, valid_y.shape)
# # Augment Data
# In[ ]:
from keras.preprocessing.image import ImageDataGenerator
def classifier(self, req, zero_out):
expected_items = []
for i in req.expected_labels:
expected_items.append(i.data)
try:
hd_image = self.cv_bridge.imgmsg_to_cv2(
req.color, desired_encoding="passthrough")
except CvBridgeError as e:
rospy.logerr(e)
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
# t0 = datetime.datetime.now()
seg_net = net.create_infer(CLASSNUM, WORKSPACE)
mod = mx.module.Module(seg_net,
data_names=('data', ),
label_names=(),
context=self.ctx)
# t1 = datetime.datetime.now()
# rospy.logerr('seg_net creation took %s' % (t1 - t0))
# t0 = datetime.datetime.now()
mod.bind(data_shapes=[("data", (1, 3, 640, 640))],
for_training=False,
grad_req='null')
# t1 = datetime.datetime.now()
# rospy.logerr('module bind took %s' % (t1 - t0))
# t0 = datetime.datetime.now()
mod.init_params(arg_params=self.arg_dict,
aux_params=self.aux_dict,
allow_missing=True)
# t1 = datetime.datetime.now()
# rospy.logerr('module parameter init took %s' % (t1 - t0))
rgb_mean = 128
hd_image = hd_image.astype(np.float32)
im = hd_image - rgb_mean
im = np.swapaxes(im, 0, 2)
im = np.swapaxes(im, 1, 2)
im = np.expand_dims(im, axis=0)
im, orig_size = misc.pad_image(im, 32)
# t0 = datetime.datetime.now()
mod.reshape([("data", im.shape)])
# t1 = datetime.datetime.now()
# rospy.logerr('module reshape took %s' % (t1 - t0))
# t0 = datetime.datetime.now()
mod.bind(data_shapes=[("data", (1, 3, 640, 640))],
for_training=False,
grad_req='null')
# t1 = datetime.datetime.now()
# rospy.logerr('module bind took %s' % (t1 - t0))
# t0 = datetime.datetime.now()
mod.forward(
mx.io.DataBatch(data=[mx.nd.array(im)],
label=None,
pad=None,
index=None))
# t1 = datetime.datetime.now()
# rospy.logerr('module forward took %s' % (t1 - t0))
# t0 = datetime.datetime.now()
pred = mod.get_outputs()[0].asnumpy().squeeze()
# t1 = datetime.datetime.now()
# rospy.logerr('module prediction took %s' % (t1 - t0))
pred = misc.crop_pred(pred, orig_size)
# t0 = datetime.datetime.now()
if zero_out:
for i in self.item_list[1:]:
j = self.item_list.index(i)
if i in expected_items or j < 1 or j >= CLASSNUM:
continue
if j < pred.shape[0]:
pred[j] = np.zeros_like(pred[j])
continue
pred_label = pred.argmax(0)
# t1 = datetime.datetime.now()
# rospy.logerr('bad prediction elimination took %s' % (t1 - t0))
# s0 = datetime.datetime.now()
best_scores_map, second_best_scores_map, diff_conf_map = misc.confidence_maps(
pred)
# s1 = datetime.datetime.now()
# rospy.logerr('confidence map creation took %s' % (s1 - s0))
mean_conf_map = np.zeros_like(diff_conf_map)
ccs, num_ccs = measure.label(pred_label, return_num=True)
for cc in range(1, num_ccs + 1):
mean_conf = diff_conf_map[np.where(ccs == cc)].mean()
mean_conf_map[np.where(ccs == cc)] = mean_conf
mean_conf_map = (mean_conf_map * 255).astype(np.uint8)
mean_conf_map[mean_conf_map == 0] = 1
# t0 = datetime.datetime.now()
binmask = np.zeros_like(pred_label)
segment_certainties = []
empty_img = np.zeros_like(mean_conf_map)
conf_map = np.zeros_like(mean_conf_map)
pred_label_unique = np.unique(pred_label)
if zero_out:
for c in pred_label_unique:
# -3 for storage, tote, and because zero indexing
if c < 1 or c > (CLASSNUM - 3):
continue
binmask = pred_label == c
cnts = cv2.findContours(binmask.astype(np.uint8),
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)[1]
if len(cnts) != 0:
max_cnt = -1
max_certainty = 0
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
empty_img = empty_img * 0
for cc in range(len(cnts)):
col = cc + 1
cv2.drawContours(image=empty_img,
contours=cnts,
contourIdx=cc,
color=col,
thickness=-1)
certainty = (mean_conf_map * (empty_img == col)).max()
if certainty > max_certainty:
max_certainty = certainty
max_cnt = col
segment_certainties.append(max_certainty)
mask = empty_img == max_cnt
conf_map[np.where(mask)] = max_certainty
pred_label[np.where(conf_map == 0)] = 0
pred_label_unique = np.unique(pred_label)
labels = []
for i in self.item_list[1:]:
j = self.item_list.index(i)
if i in expected_items and j in pred_label_unique:
labels.append(i)
# t1 = datetime.datetime.now()
# rospy.logerr('Took %s' % (t1 - t0))
out_lab = np.uint8(np.copy(pred_label))
out_lab = Image.fromarray(out_lab)
out_lab_raw = out_lab.copy()
out_lab.putpalette(self.cmap)
out_lab = out_lab.convert('RGB')
rgb_img = hd_image
rgb_img_arr = np.array(rgb_img, dtype=np.float32) / 255
bnd_img = mark_boundaries(rgb_img_arr, pred_label, mode='thick')
bnd_img = Image.fromarray((bnd_img * 255).astype('uint8'))
out_img = Image.blend(bnd_img, out_lab, 0.6)
# t1 = datetime.datetime.now()
# rospy.logerr('output image creation took %s' % (t1 - t0))
# Stay at 1 because state machine code uses 1 indexing because historical reasons
k = 1
for c in pred_label_unique:
# -3 for storage, tote, and because zero indexing
if c < 1 or c > (CLASSNUM - 3):
continue
binmask = pred_label == c
pred_label[np.where(pred_label == c)] = k
k += 1
com = ndimage.measurements.center_of_mass(binmask)
fs = 14
draw = ImageDraw.Draw(out_img)
draw.text((com[1], com[0]), 'O', (0, 0, 255), font=self.font)
draw.text((com[1] - len(self.item_list[c]) / 2 * fs / 2, com[0]),
self.item_list[c], (255, 255, 0),
font=self.font)
draw = ImageDraw.Draw(out_img)
# t1 = datetime.datetime.now()
# rospy.logerr('output image creation overall took %s' % (t1 - t0))
# t0 = datetime.datetime.now()
res = rgbd_object_proposalResponse()
try:
res.classification = self.cv_bridge.cv2_to_imgmsg(
pred_label.astype(np.uint8), 'mono8')
except CvBridgeError as e:
rospy.logerr(e)
try:
oi = np.array(out_img)
oi = cv2.cvtColor(oi, cv2.COLOR_BGR2RGB)
res.overlay_img = self.cv_bridge.cv2_to_imgmsg(oi, 'rgb8')
except CvBridgeError as e:
rospy.logerr(e)
try:
res.confidence_map = self.cv_bridge.cv2_to_imgmsg(
mean_conf_map, 'mono8')
except CvBridgeError as e:
rospy.logerr(e)
for i in labels:
res.label.append(String(i))
for i in segment_certainties:
res.segment_certainties.append(UInt8(i))
# t1 = datetime.datetime.now()
# rospy.logerr('response creation took %s' % (t1 - t0))
return res
seg_slice = seg.shape[0] // 8
ind = 0
seg_slice = seg.shape[0] // 4
for i in range(seg_slice, seg.shape[0] + 1, seg_slice):
for j in range(seg_slice, seg.shape[1] + 1, seg_slice):
seg[i - seg_slice:i, j - seg_slice:j] = ind
ind += 1
# import pdb; pdb.set_trace()
fig, ax = pl.subplots(2, 2, figsize=(10, 10), sharex=True, sharey=True)
ax[0, 0].imshow(image.array_to_img(img_orig))
# ax[0, 0].set_title("Felzenszwalbs's method")
# import pdb; pdb.set_trace()
ax[0, 1].imshow(mark_boundaries(img, seg.astype('int64')))
ax[0, 1].set_title('SLIC')
# ax[1, 0].imshow(mark_boundaries(img, segments_quick))
# ax[1, 0].set_title('Quickshift')
# ax[1, 1].imshow(mark_boundaries(img, segments_watershed))
# ax[1, 1].set_title('Compact watershed')
pl.show()
# import pdb; pdb.set_trace()
# define a function that depends on a binary mask representing if an image region is hidden
def mask_image(zs, segmentation, image, background=None):
if background is None:
background = image.mean((0, 1))
out = np.zeros(
(zs.shape[0], image.shape[0], image.shape[1], image.shape[2]))
def get_explanation(self, label, num_features=20, which_exp='positive'):
# the explanation to display:
'''
:param label: label to explain
:param num_features: how many top features to display
:param positive: want features that encourage or discourage the dicision (label)
:return:
this_exp: raw feature and weight values
readable_exp: a text description of the explanation
txt_exp_list: text part of the explanation, ready to display
temp, mask: image part of the explanation, ready to display
'''
this_exp = np.array(self.local_exp[label])
if which_exp == 'positive':
positives = this_exp[this_exp[:, 1] >= 0]
elif which_exp == 'negative':
positives = this_exp[this_exp[:, 1] < 0]
if positives.shape[0] < num_features:
num_features = positives.shape[0]
top_exp = positives[:num_features]
top_exp_unique, top_idx = np.unique(top_exp[:, 0], return_index=True)
txt_exp = top_exp[top_exp[:, 0] < self.n_txt_features]
n_txt_exp = txt_exp.shape[0]
txt_top_idx = []
for txt_feature in txt_exp:
txt_top_idx.append(top_idx[top_exp_unique == txt_feature[0]] + 1)
img_exp = top_exp[top_exp[:, 0] >= self.n_txt_features]
n_img_exp = img_exp.shape[0]
img_top_idx = []
for img_feature in img_exp:
img_top_idx.append(top_idx[top_exp_unique == img_feature[0]] + 1)
readable_exp = f"among the top 10 features, {n_txt_exp} are from the text (the top"
for i in txt_top_idx:
readable_exp += str(i)
readable_exp += f"), {n_img_exp} are from the image (the top"
for j in img_top_idx:
readable_exp += str(j)
readable_exp += f"), displayed as follows"
txt_exp_list = np.array(self.as_list(label), dtype='object')
# return explanations upon request
if which_exp == 'positive':
txt_list = txt_exp_list[txt_exp_list[:, 1] >= 0]
temp, mask = self.get_image_and_mask(label, num_features=n_img_exp)
else:
txt_list = txt_exp_list[txt_exp_list[:, 1] < 0]
temp, mask = self.get_image_and_mask(label,
num_features=n_img_exp,
positive_only=False,
negative_only=True)
txt_list = txt_list[:n_txt_exp]
img_boundary = mark_boundaries(temp, mask)
im = Image.fromarray(np.uint8(img_boundary * 255))
return this_exp, readable_exp, txt_list, im
def mark_boundaries(self, pixel_buffer=50):
return segmentation.mark_boundaries(self.rgb(pixel_buffer), self.binary(pixel_buffer))
def train_on_batch(self, batch, **extras):
self.train()
images = batch["images"].cuda()
counts = float(batch["counts"][0])
logits = self.model_base(images)
if self.exp_dict['model'].get('loss') == 'lcfcn':
loss = lcfcn.compute_lcfcn_loss(logits, batch["points"].cuda(),
None)
probs = F.softmax(logits, 1)
mask = probs.argmax(
dim=1).cpu().numpy().astype('uint8').squeeze() * 255
# img_mask=hu.save_image('tmp2.png',
# hu.denormalize(images, mode='rgb'), mask=mask, return_image=True)
# hu.save_image('tmp2.png',np.array(img_mask)/255. , radius=3,
# points=batch["points"])
elif self.exp_dict['model'].get('loss') == 'glance':
pred_counts = logits[:, 1].mean()
loss = F.mse_loss(pred_counts.squeeze(), counts.float().squeeze())
elif self.exp_dict['model'].get('loss') == 'att_lcfcn':
probs = logits.sigmoid()
# get points from attention
att_dict = self.att_model.get_attention_dict(
images_original=torch.FloatTensor(
hu.denormalize(batch['images'], mode='rgb')),
counts=batch['counts'][0],
probs=probs.squeeze(),
return_roi=True)
if 1:
blobs = lcfcn.get_blobs(probs.squeeze().detach().cpu().numpy())
org_img = hu.denormalize(images.squeeze(), mode='rgb')
rgb_labels = label2rgb(
blobs,
hu.f2l(org_img),
bg_label=0,
bg_color=None,
)
res1 = mark_boundaries(rgb_labels, blobs)
img_mask = hu.save_image('tmp2.png',
org_img,
return_image=True)
res2 = hu.save_image('tmp.png',
np.array(img_mask) / 255.,
points=att_dict['points'],
radius=1,
return_image=True)
hu.save_image('tmp_blobs.png',
np.hstack([res1, np.array(res2) / 255.]))
loss = lcfcn.compute_loss(
probs=probs,
# batch["points"].cuda(),
points=att_dict['points'].cuda(),
roi_mask=att_dict['roi_mask'])
# loss += .5 * F.cross_entropy(logits,
# torch.from_numpy(1 -
# att_dict['mask_bg']).long().cuda()[None],
# ignore_index=1)
self.opt.zero_grad()
loss.backward()
if self.exp_dict['optimizer'] == 'sps':
self.opt.step(loss=loss)
else:
self.opt.step()
return {"train_loss": float(loss)}
##
i = 'horse1.jpg'
img = imread(i, as_gray=False)
show_img(img)
#
labels = segmentation.slic(img, compactness=60, n_segments=4000) #400
labels = labels + 1 # So that no labelled region is 0 and ignored by regionprops
regions = regionprops(labels)
label_rgb = color.label2rgb(labels, img, kind='avg')
show_img(label_rgb)
imsave("horse3.jpg", label_rgb)
label_rgb = segmentation.mark_boundaries(label_rgb, labels, (0, 0, 0))
show_img(label_rgb)
imsave("horse4.jpg", label_rgb)
####################################
rag = graph.rag_mean_color(img, labels)
for region in regions:
rag.node[region['label']]['centroid'] = region['centroid']
edges_drawn_all = display_edges(label_rgb, rag, np.inf)
show_img(edges_drawn_all)
imsave("horse_graph.jpg", edges_drawn_all)
