def fuse_radio_ir_4_pred(radio_fn, ir_fn, out_dir='/tmp', model='D4'):
"""
return the full path to the generated fused file
"""
if (model != 'D5'):
nsz = None
if ('D3' == model):
mask_ir = False
else:
mask_ir = True
else:
nsz = cfg.TEST.SCALES[0] #i.e. 600
mask_ir = True
return fuse(radio_fn, ir_fn, out_dir, new_size=nsz, mask_ir=mask_ir)
def demo(sess,
net,
im_file,
vis_file,
fits_fn,
conf_thresh=0.8,
eval_class=True):
"""
Detect object classes in an image using pre-computed object proposals.
im_file: The "fused" image file path
vis_file: The background image file on which detections are laid.
Normallly, this is just the IR image file path
fits_fn: The FITS file path
eval_class: True - use traditional per class-based evaluation style
False - use per RoI-based evaluation
"""
show_img_size = cfg.TEST.SCALES[0]
if (not os.path.exists(im_file)):
print(('%s cannot be found' % (im_file)))
return -1
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
image_name = osp.basename(im_file)
scores, boxes = im_detect(sess,
net,
im,
save_vis_dir=None,
img_name=os.path.splitext(image_name)[0])
boxes *= float(show_img_size) / float(im.shape[0])
timer.toc()
sys.stdout.write('Done in {:.3f} secs'.format(timer.total_time))
sys.stdout.flush()
print(scores)
im = cv2.imread(vis_file)
my_dpi = 100
fig = plt.figure()
fig.set_size_inches(show_img_size / my_dpi, show_img_size / my_dpi)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.set_xlim([0, show_img_size])
ax.set_ylim([show_img_size, 0])
#ax.set_aspect('equal')
im = cv2.resize(im, (show_img_size, show_img_size))
im = im[:, :, (2, 1, 0)]
ax.imshow(im, aspect='equal')
if (fits_fn is not None):
patch_contour = fuse(fits_fn,
im,
None,
sigma_level=4,
mask_ir=False,
get_path_patch_only=True)
ax.add_patch(patch_contour)
NMS_THRESH = cfg.TEST.NMS #cfg.TEST.RPN_NMS_THRESH # 0.3
tt_vis = 0
bbox_img = []
bscore_img = []
num_sources = 0
#if (eval_class):
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])) #.astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
num_sources += vis_detections(im, cls, dets, ax, thresh=conf_thresh)
#dets = np.hstack((dets, np.ones([dets.shape[0], 1]) * cls_ind))
# if (dets.shape[0] > 0):
# bbox_img.append(dets)
# bscore_img.append(np.reshape(dets[:, -2], [-1, 1]))
# else:
# for eoi_ind, eoi in enumerate(boxes):
# eoi_scores = scores[eoi_ind, 1:] # skip background
# cls_ind = np.argmax(eoi_scores) + 1 # add the background index back
# cls_boxes = boxes[eoi_ind, 4 * cls_ind : 4 * (cls_ind + 1)]
# cls_scores = scores[eoi_ind, cls_ind]
# dets = np.hstack((np.reshape(cls_boxes, [1, -1]),
# np.reshape(cls_scores, [-1, 1])))#.astype(np.float32)
# dets = np.hstack((dets, np.ones([dets.shape[0], 1]) * cls_ind))
# bbox_img.append(dets)
# bscore_img.append(np.reshape(dets[:, -2], [-1, 1]))
#
# boxes_im = np.vstack(bbox_img)
# scores_im = np.vstack(bscore_img)
#
# #if (not eval_class):
# # a numpy float is a C double, so need to use float32
# keep = nms(boxes_im[:, :-1].astype(np.float32), NMS_THRESH)
# boxes_im = boxes_im[keep, :]
# scores_im = scores_im[keep, :]
#
# keep_indices = range(boxes_im.shape[0])
#num_sources = vis_detections(im, None, boxes_im[keep_indices, :], ax, thresh=conf_thresh)
print((', found %d sources' % num_sources))
return 0
def demo(sess,
net,
im_file,
vis_file,
fits_fn,
conf_thresh=0.8,
eval_class=True,
extra_vis_png=False,
plot=False):
"""
Detect object classes in an image using pre-computed object proposals.
im_file: The "fused" image file path
vis_file: The background image file on which detections are laid.
Normallly, this is just the IR image file path
fits_fn: The FITS file path
eval_class: True - use traditional per class-based evaluation style
False - use per RoI-based evaluation
"""
show_img_size = cfg.TEST.SCALES[0]
if (not os.path.exists(im_file)):
print('%s cannot be found' % (im_file))
return -1
# Add ground truth values to dictionary
im = cv2.imread(im_file)
im_file_name = im_file[5:26]
add_to_dict(im_file_name)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
image_name = osp.basename(im_file)
scores, boxes = im_detect(sess,
net,
im,
save_vis_dir=None,
img_name=os.path.splitext(image_name)[0])
boxes *= float(show_img_size) / float(im.shape[0])
timer.toc()
sys.stdout.write('Done in {:.3f} secs'.format(timer.total_time))
sys.stdout.flush()
im = cv2.imread(vis_file)
my_dpi = 100
fig = plt.figure()
if plot:
fig.set_size_inches(show_img_size / my_dpi * 2, show_img_size / my_dpi)
fig.suptitle(im_file_name, fontsize=18)
ax = fig.add_subplot(1, 2, 1)
else:
fig.set_size_inches(show_img_size / my_dpi, show_img_size / my_dpi)
ax = plt.Axes(fig, [0., 0., 1., 1.])
im = cv2.resize(im, (show_img_size, show_img_size))
im = im[:, :, (2, 1, 0)]
ax.axis('off')
ax.set_axis_off()
fig.add_axes(ax)
ax.set_xlim([0, show_img_size])
ax.set_ylim([show_img_size, 0])
ax.imshow(im, aspect='equal')
if ((fits_fn is not None) and (not extra_vis_png)):
patch_contour = fuse(fits_fn,
im,
None,
sigma_level=4,
mask_ir=False,
get_path_patch_only=True)
ax.add_patch(patch_contour)
NMS_THRESH = cfg.TEST.NMS #cfg.TEST.RPN_NMS_THRESH # 0.3
tt_vis = 0
bbox_img = []
bscore_img = []
num_sources = 0
#if (eval_class):
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])) #.astype(np.float32)
#get copy of dets for plotting purposes
bboxs = np.empty(np.shape(dets))
np.copyto(bboxs, dets)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
if plot:
num_sources += plt_detections(im,
cls,
dets,
ax,
bboxs,
thresh=conf_thresh)
fig.subplots_adjust(top=0.85)
else:
num_sources += vis_detections(im,
cls,
dets,
ax,
thresh=conf_thresh)
#add_to_csv(scores)
#dets = np.hstack((dets, np.ones([dets.shape[0], 1]) * cls_ind))
# if (dets.shape[0] > 0):
# bbox_img.append(dets)
# bscore_img.append(np.reshape(dets[:, -2], [-1, 1]))
# else:
# for eoi_ind, eoi in enumerate(boxes):
# eoi_scores = scores[eoi_ind, 1:] # skip background
# cls_ind = np.argmax(eoi_scores) + 1 # add the background index back
# cls_boxes = boxes[eoi_ind, 4 * cls_ind : 4 * (cls_ind + 1)]
# cls_scores = scores[eoi_ind, cls_ind]
# dets = np.hstack((np.reshape(cls_boxes, [1, -1]),
# np.reshape(cls_scores, [-1, 1])))#.astype(np.float32)
# dets = np.hstack((dets, np.ones([dets.shape[0], 1]) * cls_ind))
# bbox_img.append(dets)
# bscore_img.append(np.reshape(dets[:, -2], [-1, 1]))
#
# boxes_im = np.vstack(bbox_img)
# scores_im = np.vstack(bscore_img)
#
# #if (not eval_class):
# # a numpy float is a C double, so need to use float32
# keep = nms(boxes_im[:, :-1].astype(np.float32), NMS_THRESH)
# boxes_im = boxes_im[keep, :]
# scores_im = scores_im[keep, :]
#
# keep_indices = range(boxes_im.shape[0])
#num_sources = vis_detections(im, None, boxes_im[keep_indices, :], ax, thresh=conf_thresh)
print(', found %d sources' % num_sources)
#If no sources detected plot average position of detection boxes
if num_sources == 0:
boxes = np.reshape(boxes, (cfg.TEST.RPN_POST_NMS_TOP_N, 7, 4))
bboxs = np.average(boxes, axis=1)
#Draw ground truth boxes
for i in range(len(ground_truth['act_xmin'])):
ax.add_patch(
plt.Rectangle(
(ground_truth['act_xmin'][i], ground_truth['act_ymin'][i]),
ground_truth['act_xmax'][i] - ground_truth['act_xmin'][i],
ground_truth['act_ymax'][i] - ground_truth['act_ymin'][i],
fill=False,
edgecolor='gray',
linewidth=2.0,
linestyle='--'))
#Draw proposed boxes
for a_box in range(np.shape(bboxs)[0]):
bbox = bboxs[a_box, :4]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1],
fill=False,
edgecolor=colors_2[a_box],
linewidth=1.0))
##Plot scores for each box as a grouped histogram
if plot:
axa = fig.add_subplot(1, 2, 2)
barwidth = (1 - 0.1) / cfg.TEST.RPN_POST_NMS_TOP_N
axa.set_ylim([0, 1])
r1 = np.arange(len(scores[0, :]))
for i in range(0, int(np.shape(scores)[0])):
axa.bar(r1,
scores[i, :],
color=colors_2[i],
width=barwidth,
label='det_' + str(i + 1))
r1 = [x + barwidth for x in r1]
axa.set_xticks([r + 0.45 for r in range(len(scores[0, :]))])
axa.set_xticklabels(CLASSES[:], fontsize=8)
#Draw horizontal line at threshold, add legend and title
axa.axhline(conf_thresh, linestyle='--', color='black', linewidth=1.0)
plt.legend(loc='upper right', fontsize='x-small')
axa.set_title('Class Scores for each Detection')
# Generate text string for ClaRAN's prediction
claran_text = ''
for p in range(len(predict['pred_class'])):
claran_text += str(predict['pred_class'][p].replace('_', 'C_') +
'P ' +
"{:.2f}".format(predict['score'][p])) + " "
ax.text(5,
20,
'{:s}'.format('ClaRAN: ' + claran_text),
bbox=dict(facecolor='None', alpha=0.4, edgecolor='None'),
fontsize=8,
color='black')
# Generate text string for ground truth
gt_text = ''
for q in ground_truth['act_class']:
gt_text += q.replace('_', 'C_') + 'P '
ax.text(5,
40,
'{:s}'.format('Ground Truth: ' + gt_text),
bbox=dict(facecolor='None', alpha=0.4, edgecolor='None'),
fontsize=8,
color='black')
plt.tight_layout()
fig.subplots_adjust(top=0.85)
plt.draw()
# save results to CSV if ClaRAN has found a single source
#if num_sources == 1:
#dict_to_csv( dict(ground_truth.items() + predict.items()) )
return 0