def frameMABO(dname, redo=False):
d = GetDataset(dname)
dirname = os.path.join( os.path.dirname(__file__), '../results/ACT-detector/', dname)
eval_file = os.path.join(dirname, "frameMABO.pkl")
if os.path.isfile(eval_file) and not redo:
with open(eval_file, 'rb') as fid:
BO = pickle.load(fid)
else:
vlist = d.test_vlist()
BO = {l: [] for l in d.labels} # best overlap
for v in vlist:
gt = d.gttubes(v)
h, w = d.resolution(v)
# load per-frame detections
vdets = {i: np.empty((0,4), dtype=np.float32) for i in range(1, 1+d.nframes(v))}
# load results for each chunk
for i in xrange(1, 1 + d.nframes(v) - K + 1):
resname = os.path.join(dirname, d.frame_format(v,i) + '.pkl')
if not os.path.isfile(resname):
print("ERROR: Missing extracted tubelets " + resname)
sys.exit()
with open(resname, 'rb') as fid:
dets, _ = pickle.load(fid)
for k in xrange(K):
vdets[i+k] = np.concatenate((vdets[i + k], dets[:, 2+4*k:6+4*k]), axis=0)
# for each frame
for i in xrange(1, 1 + d.nframes(v)):
for ilabel in gt:
label = d.labels[ilabel]
for t in gt[ilabel]:
# the gt tube does not cover frame i
if not i in t[:,0]:
continue
gtbox = t[t[:,0] == i, 1:5] # box of gt tube at frame i
if vdets[i].size == 0: # we missed it
BO[label].append(0)
continue
ious = iou2d(vdets[i], gtbox)
BO[label].append( np.max(ious) )
# save file
with open(eval_file, 'wb') as fid:
pickle.dump( BO, fid)
# print MABO results
ABO = {la: 100 * np.mean(np.array(BO[la])) for la in d.labels} # average best overlap
for la in d.labels:
print("{:20s} {:6.2f}".format(la, ABO[la]))
print("{:20s} {:6.2f}".format("MABO", np.mean(np.array(ABO.values()))))
def load_frame_detections(d, vlist, dirname, nms):
if isinstance(d, str):
d = GetDataset(d)
alldets = [] # list of numpy array with <video_index> <frame_index> <ilabel> <score> <x1> <y1> <x2> <y2>
for iv, v in enumerate(vlist):
h,w = d.resolution(v)
# aggregate the results for each frame
vdets = {i: np.empty((0,6), dtype=np.float32) for i in range(1, 1 + d.nframes(v))} # x1, y1, x2, y2, score, ilabel
# load results for each starting frame
for i in xrange(1, 1 + d.nframes(v) - K + 1):
resname = os.path.join(dirname, d.frame_format(v,i) + '.pkl')
if not os.path.isfile(resname):
print("ERROR: Missing extracted tubelets "+resname)
sys.exit()
with open(resname, 'rb') as fid:
dets, _ = pickle.load(fid)
if dets.size == 0:
continue
for k in xrange(K):
vdets[i+k] = np.concatenate( (vdets[i+k],dets[:,np.array([2+4*k,3+4*k,4+4*k,5+4*k,1,0])] ), axis=0)
# Perform NMS in each frame
for i in vdets:
idx = np.empty((0,), dtype=np.int32)
for ilabel in xrange(d.nlabels):
a = np.where(vdets[i][:,5] == ilabel)[0]
if a.size == 0:
continue
idx = np.concatenate((idx, a[nms2d(vdets[i][vdets[i][:, 5] == ilabel, :5], nms)]), axis=0)
if idx.size == 0:
continue
alldets.append(np.concatenate((iv * np.ones((idx.size, 1), dtype=np.float32), i * np.ones((idx.size, 1), dtype=np.float32), vdets[i][idx, :][:, np.array([5, 4, 0, 1, 2, 3], dtype=np.int32)]), axis=1))
return np.concatenate(alldets, axis=0)
def frameMABO(dname, redo=False):
d = GetDataset(dname)
dirname = os.path.join(os.path.dirname(__file__),
'../results/ACT-detector/', dname)
eval_file = os.path.join(dirname, "frameMABO.pkl")
if os.path.isfile(eval_file) and not redo:
with open(eval_file, 'rb') as fid:
BO = pickle.load(fid)
else:
vlist = d.test_vlist()
BO = {l: [] for l in d.labels} # best overlap
for v in vlist:
gt = d.gttubes(v)
h, w = d.resolution(v)
# load per-frame detections
vdets = {
i: np.empty((0, 4), dtype=np.float32)
for i in range(1, 1 + d.nframes(v))
}
# load results for each chunk
for i in range(1, 1 + d.nframes(v) - K + 1):
resname = os.path.join(dirname, d.frame_format(v, i) + '.pkl')
if not os.path.isfile(resname):
print("ERROR: Missing extracted tubelets " + resname)
sys.exit()
with open(resname, 'rb') as fid:
dets, _ = pickle.load(fid)
for k in range(K):
vdets[i + k] = np.concatenate(
(vdets[i + k], dets[:, 2 + 4 * k:6 + 4 * k]), axis=0)
# for each frame
for i in range(1, 1 + d.nframes(v)):
for ilabel in gt:
label = d.labels[ilabel]
for t in gt[ilabel]:
# the gt tube does not cover frame i
if not i in t[:, 0]:
continue
gtbox = t[t[:, 0] == i,
1:5] # box of gt tube at frame i
if vdets[i].size == 0: # we missed it
BO[label].append(0)
continue
ious = iou2d(vdets[i], gtbox)
BO[label].append(np.max(ious))
# save file
with open(eval_file, 'wb') as fid:
pickle.dump(BO, fid)
# print MABO results
ABO = {la: 100 * np.mean(np.array(BO[la]))
for la in d.labels} # average best overlap
for la in d.labels:
print("{:20s} {:6.2f}".format(la, ABO[la]))
print("{:20s} {:6.2f}".format("MABO", np.mean(np.array(ABO.values()))))
def extract_tubelets(dname, gpu=-1, redo=False):
"""Extract the tubelets for a given dataset
args:
- dname: dataset name (example: 'JHMDB')
- gpu (default -1): use gpu given in argument, or use cpu if -1
- redo: wheter or not to recompute already computed files
save a pickle file for each frame
the file contains a tuple (dets, dets_all)
- dets is a numpy array with 2+4*K columns containing the tubelets starting at this frame after per-class nms at 0.45 and thresholding the scores at 0.01
the columns are <label> <score> and then <x1> <y1> <x2> <y2> for each of the frame in the tubelet
- dets_all contains the tubelets obtained after a global nms at 0.7 and thresholding the scores at 0.01
it is a numpy arrray with 4*K + L + 1 containing the coordinates of the tubelets and the scores for all labels
note: this version is inefficient: it is better to estimate the per-frame features once
"""
d = GetDataset(dname)
if gpu >= 0:
caffe.set_mode_gpu()
caffe.set_device(gpu)
model_dir = os.path.join(os.path.dirname(__file__),
'../models/ACT-detector/', dname)
output_dir = os.path.join(os.path.dirname(__file__),
'../results/ACT-detector/', dname)
# load the RGB network
rgb_proto = os.path.join(model_dir, "deploy_RGB.prototxt")
rgb_model = os.path.join(model_dir, "RGB.caffemodel")
net_rgb = caffe.Net(rgb_proto, caffe.TEST, weights=rgb_model)
# load the FLOW5 network
flo_proto = os.path.join(model_dir, "deploy_FLOW5.prototxt")
flo_model = os.path.join(model_dir, "FLOW5.caffemodel")
net_flo = caffe.Net(flo_proto, caffe.TEST, weights=flo_model)
vlist = d.test_vlist()
for iv, v in enumerate(vlist):
print("Processing video {:d}/{:d}: {:s}".format(iv + 1, len(vlist), v))
h, w = d.resolution(v)
# network output is normalized between 0,1 ; so we will multiply it by the following array
resolution_array = np.array([w, h, w, h] * K, dtype=np.float32)
# now process each frame
for i in range(1, 1 + d.nframes(v) - K + 1):
outfile = os.path.join(output_dir, d.frame_format(v, i) + ".pkl")
# skip if already computed
if os.path.isfile(outfile) and not redo:
continue
# read the frames for the forward
kwargs_rgb = {}
kwargs_flo = {}
for j in range(K):
im = cv2.imread(d.imfile(v, i + j))
if im is None:
print("Image {:s} does not exist".format(d.imfile(
v, i + j)))
return
imscale = cv2.resize(im, (IMGSIZE, IMGSIZE),
interpolation=cv2.INTER_LINEAR)
kwargs_rgb['data_stream' + str(j)] = np.transpose(
imscale - MEAN, (2, 0, 1))[None, :, :, :]
imf = [
cv2.imread(d.flowfile(v, min(d.nframes(v), i + j + iflow)))
for iflow in range(NFLOWS)
]
if np.any(imf) is None:
print("Flow image {:s} does not exist".format(
d.flowfile(v, i + j)))
return
imscalef = [
cv2.resize(im, (IMGSIZE, IMGSIZE),
interpolation=cv2.INTER_LINEAR) for im in imf
]
timscale = [
np.transpose(im - MEAN, (2, 0, 1))[None, :, :, :]
for im in imscalef
]
kwargs_flo['data_stream' + str(j) + 'flow'] = np.concatenate(
timscale, axis=1)
# compute rgb and flow scores
# two forward passes: one for the rgb and one for the flow
net_rgb.forward(
end="mbox_conf_flatten",
**kwargs_rgb) # forward of rgb with confidence and regression
net_flo.forward(
end="mbox_conf_flatten", **
kwargs_flo) # forward of flow5 with confidence and regression
# compute late fusion of rgb and flow scores (keep regression from rgb)
# use net_rgb for standard detections, net_flo for having all boxes
scores = 0.5 * (net_rgb.blobs['mbox_conf_flatten'].data +
net_flo.blobs['mbox_conf_flatten'].data)
net_rgb.blobs['mbox_conf_flatten'].data[...] = scores
net_flo.blobs['mbox_conf_flatten'].data[...] = scores
net_flo.blobs['mbox_loc'].data[
...] = net_rgb.blobs['mbox_loc'].data
# two forward passes, only for the last layer
# dets is the detections after per-class NMS and thresholding (stardard)
# dets_all contains all the scores and regressions for all tubelets
dets = net_rgb.forward(
start='detection_out')['detection_out'][0, 0, :, 1:]
dets_all = net_flo.forward(
start='detection_out_full')['detection_out_full'][0, 0, :, 1:]
# parse detections with per-class NMS
if dets.shape[0] == 1 and np.all(dets == -1):
dets = np.empty((0, dets.shape[1]), dtype=np.float32)
dets[:,
2:] *= resolution_array # network output was normalized in [0..1]
dets[:,
0] -= 1 # label 0 was background, come back to label in [0..nlabels-1]
dets[:, 2::2] = np.maximum(0, np.minimum(w, dets[:, 2::2]))
dets[:, 3::2] = np.maximum(0, np.minimum(h, dets[:, 3::2]))
# parse detections with global NMS at 0.7 (top 300)
# coordinates were normalized in [0..1]
dets_all[:, 0:4 * K] *= resolution_array
dets_all[:, 0:4 * K:2] = np.maximum(
0, np.minimum(w, dets_all[:, 0:4 * K:2]))
dets_all[:, 1:4 * K:2] = np.maximum(
0, np.minimum(h, dets_all[:, 1:4 * K:2]))
idx = nms_tubelets(
np.concatenate(
(dets_all[:, :4 * K],
np.max(dets_all[:, 4 * K + 1:], axis=1)[:, None]),
axis=1), 0.7, 300)
dets_all = dets_all[idx, :]
# save file
if not os.path.isdir(os.path.dirname(outfile)):
os.system('mkdir -p ' + os.path.dirname(outfile))
with open(outfile, 'wb') as fid:
pickle.dump((dets, dets_all), fid)
def load_frame_detections(d, vlist, dirname, nms):
if isinstance(d, str):
d = GetDataset(d)
alldets = [
] # list of numpy array with <video_index> <frame_index> <ilabel> <score> <x1> <y1> <x2> <y2>
for iv, v in enumerate(vlist):
h, w = d.resolution(v)
# aggregate the results for each frame
vdets = {
i: np.empty((0, 6), dtype=np.float32)
for i in range(1, 1 + d.nframes(v))
} # x1, y1, x2, y2, score, ilabel
# load results for each starting frame
for i in range(1, 1 + d.nframes(v) - K + 1):
resname = os.path.join(dirname, d.frame_format(v, i) + '.pkl')
if not os.path.isfile(resname):
print("ERROR: Missing extracted tubelets " + resname)
sys.exit()
with open(resname, 'rb') as fid:
dets, _ = pickle.load(fid)
if dets.size == 0:
continue
for k in range(K):
vdets[i + k] = np.concatenate(
(vdets[i + k],
dets[:,
np.array([
2 + 4 * k, 3 + 4 * k, 4 + 4 * k, 5 + 4 * k, 1, 0
])]),
axis=0)
# Perform NMS in each frame
for i in vdets:
idx = np.empty((0, ), dtype=np.int32)
for ilabel in range(d.nlabels):
a = np.where(vdets[i][:, 5] == ilabel)[0]
if a.size == 0:
continue
idx = np.concatenate((idx, a[nms2d(
vdets[i][vdets[i][:, 5] == ilabel, :5], nms)]),
axis=0)
if idx.size == 0:
continue
alldets.append(
np.concatenate(
(iv * np.ones(
(idx.size, 1), dtype=np.float32), i * np.ones(
(idx.size, 1), dtype=np.float32), vdets[i][idx, :]
[:, np.array([5, 4, 0, 1, 2, 3], dtype=np.int32)]),
axis=1))
return np.concatenate(alldets, axis=0)
def extract_tubelets(dname, gpu=-1, redo=False):
"""Extract the tubelets for a given dataset
args:
- dname: dataset name (example: 'JHMDB')
- gpu (default -1): use gpu given in argument, or use cpu if -1
- redo: wheter or not to recompute already computed files
save a pickle file for each frame
the file contains a tuple (dets, dets_all)
- dets is a numpy array with 2+4*K columns containing the tubelets starting at this frame after per-class nms at 0.45 and thresholding the scores at 0.01
the columns are <label> <score> and then <x1> <y1> <x2> <y2> for each of the frame in the tubelet
- dets_all contains the tubelets obtained after a global nms at 0.7 and thresholding the scores at 0.01
it is a numpy arrray with 4*K + L + 1 containing the coordinates of the tubelets and the scores for all labels
note: this version is inefficient: it is better to estimate the per-frame features once
"""
d = GetDataset(dname)
if gpu >= 0:
caffe.set_mode_gpu()
caffe.set_device(gpu)
model_dir = os.path.join(os.path.dirname(__file__), '../models/ACT-detector/', dname)
output_dir = os.path.join(os.path.dirname(__file__), '../results/ACT-detector/', dname)
# load the RGB network
rgb_proto = os.path.join(model_dir, "deploy_RGB.prototxt")
rgb_model = os.path.join(model_dir, "../generated_AVA_iter_118662.caffemodel")
net_rgb = caffe.Net(rgb_proto, caffe.TEST, weights=rgb_model)
# load the FLOW5 network
flo_proto = os.path.join(model_dir, "deploy_FLOW5.prototxt")
flo_model = os.path.join(model_dir, "../generated_AVA_iter_59463.caffemodel")
net_flo = caffe.Net(flo_proto, caffe.TEST, weights=flo_model)
vlist = d.test_vlist()
for iv, v in enumerate(vlist):
print("Processing video {:d}/{:d}: {:s}".format( iv+1, len(vlist), v))
h, w = d.resolution(v)
# network output is normalized between 0,1 ; so we will multiply it by the following array
resolution_array = np.array([w,h,w,h]*K, dtype=np.float32)
# now process each frame
for i in xrange(1, 1 + d.nframes(v) - K + 1):
outfile = os.path.join(output_dir, d.frame_format(v,i) + ".pkl")
# skip if already computed
if os.path.isfile(outfile) and not redo:
continue
# read the frames for the forward
kwargs_rgb = {}
kwargs_flo = {}
for j in xrange(K):
cap = cv2.VideoCapture(d.vidfile(v,0))
#print(frame)
#print(int(cap.get(7)))
cap.set(1,i + j - 1)
im = cap.read()[1]
cap.release()
#im = cv2.imread(d.imfile(v, i + j))
if im is None:
print "Image {:s} does not exist".format(d.imfile(v, i+j))
return
imscale = cv2.resize(im, (IMGSIZE, IMGSIZE), interpolation=cv2.INTER_LINEAR)
kwargs_rgb['data_stream' + str(j)] = np.transpose(imscale-MEAN, (2, 0, 1))[None, :, :, :]
imf = [cv2.imread(d.flowfile(v.split(".")[0], min(d.nframes(v), i + j + iflow))) for iflow in xrange(NFLOWS)]
if np.any(imf) is None:
print "Flow image {:s} does not exist".format(d.flowfile(v, i+j))
return
imscalef = [cv2.resize(im, (IMGSIZE, IMGSIZE), interpolation=cv2.INTER_LINEAR) for im in imf]
timscale = [np.transpose(im-MEAN, (2, 0, 1))[None, :, :, :] for im in imscalef]
kwargs_flo['data_stream' + str(j) + 'flow'] = np.concatenate(timscale, axis=1)
# compute rgb and flow scores
# two forward passes: one for the rgb and one for the flow
net_rgb.forward(end="mbox_conf_flatten", **kwargs_rgb) # forward of rgb with confidence and regression
net_flo.forward(end="mbox_conf_flatten", **kwargs_flo) # forward of flow5 with confidence and regression
# compute late fusion of rgb and flow scores (keep regression from rgb)
# use net_rgb for standard detections, net_flo for having all boxes
scores = 0.5 * (net_rgb.blobs['mbox_conf_flatten'].data + net_flo.blobs['mbox_conf_flatten'].data)
net_rgb.blobs['mbox_conf_flatten'].data[...] = scores
net_flo.blobs['mbox_conf_flatten'].data[...] = scores
net_flo.blobs['mbox_loc'].data[...] = net_rgb.blobs['mbox_loc'].data
# two forward passes, only for the last layer
# dets is the detections after per-class NMS and thresholding (stardard)
# dets_all contains all the scores and regressions for all tubelets
dets = net_rgb.forward(start='detection_out')['detection_out'][0, 0, :, 1:]
dets_all = net_flo.forward(start='detection_out_full')['detection_out_full'][0, 0, :, 1:]
# parse detections with per-class NMS
if dets.shape[0] == 1 and np.all(dets == -1):
dets = np.empty((0, dets.shape[1]), dtype=np.float32)
dets[:, 2:] *= resolution_array # network output was normalized in [0..1]
dets[:, 0] -= 1 # label 0 was background, come back to label in [0..nlabels-1]
dets[:, 2::2] = np.maximum(0, np.minimum(w, dets[:, 2::2]))
dets[:, 3::2] = np.maximum(0, np.minimum(h, dets[:, 3::2]))
# parse detections with global NMS at 0.7 (top 300)
# coordinates were normalized in [0..1]
dets_all[:, 0:4*K] *= resolution_array
dets_all[:, 0:4*K:2] = np.maximum(0, np.minimum(w, dets_all[:, 0:4*K:2]))
dets_all[:, 1:4*K:2] = np.maximum(0, np.minimum(h, dets_all[:, 1:4*K:2]))
idx = nms_tubelets(np.concatenate((dets_all[:, :4*K], np.max(dets_all[:, 4*K+1:], axis=1)[:, None]), axis=1), 0.7, 300)
dets_all = dets_all[idx, :]
# save file
if not os.path.isdir(os.path.dirname(outfile)):
os.system('mkdir -p ' + os.path.dirname(outfile))
with open(outfile, 'wb') as fid:
pickle.dump((dets, dets_all), fid)
