def Read_Input_Images(inputDir, listOfIgnoredSamples, dB, resizedFlag, table,
workplace, spatial_size, channel, objective_flag):
r = w = spatial_size
SubperdB = []
# cross-checking parameter
subperdb_id = []
for sub in sorted([infile for infile in os.listdir(inputDir)]):
VidperSub = []
vid_id = np.empty([0])
for vid in sorted([inrfile for inrfile in os.listdir(inputDir + sub)]):
path = inputDir + sub + '/' + vid + '/' # image loading path
if path in listOfIgnoredSamples:
continue
imgList = readinput(path)
numFrame = len(imgList)
if resizedFlag == 1:
col = w
row = r
else:
img = cv2.imread(imgList[0])
[row, col, _l] = img.shape
## read the label for each input video
collectinglabel(table, sub[3:], vid, workplace + 'Classification/',
dB, objective_flag)
for var in range(numFrame):
img = cv2.imread(imgList[var])
[_, _, dim] = img.shape
if channel == 1:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if resizedFlag == 1:
img = cv2.resize(img, (col, row))
if var == 0:
FrameperVid = img.flatten()
else:
FrameperVid = np.vstack((FrameperVid, img.flatten()))
vid_id = np.append(vid_id, imgList[var]) # <--cross-check
VidperSub.append(FrameperVid)
subperdb_id.append(vid_id) # <--cross-check
SubperdB.append(VidperSub)
return SubperdB
def Read_SMIC_Images(inputDir, listOfIgnoredSamples, dB, resizedFlag, table, workplace, spatial_size): r = w = spatial_size SubperdB = [] for sub in sorted([ int(infile[1:]) for infile in os.listdir(inputDir) ]): sub_str = "s" + str(sub) VidperSub = [] for type_me in sorted([ inrfile for inrfile in os.listdir(inputDir + sub_str + "/micro/") ]): for vids in sorted([ video for video in os.listdir(inputDir + sub_str + "/micro/" + type_me + "/") ]): first_frame = True for item in sorted([image for image in os.listdir(inputDir + sub_str + "/micro/" + type_me + "/" + vids + "/")]): item = inputDir + sub_str + "/micro/" + type_me + "/" + vids + "/" + item img = cv2.imread(item) [_, _, dim] = img.shape if dim == 3: img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if resizedFlag == 1: img = cv2.resize(img, (r, w)) if first_frame: FrameperVid = img.flatten() else: FrameperVid = np.vstack(( FrameperVid, img.flatten() )) first_frame = False VidperSub.append(FrameperVid) # the label in xcel is not the same as in the path if sub < 10: vids = vids[:1] + "0" + vids[1:] collectinglabel(table, sub, vids, workplace + "Classification/", dB) SubperdB.append(VidperSub) return SubperdB
def Read_Input_Images_Apex(inputDir, listOfIgnoredSamples, dB, resizedFlag,
table, workplace, spatial_size, channel,
objective_flag):
r = w = spatial_size
SubperdB = []
# cross-checking parameter
subperdb_id = []
for sub in sorted([infile for infile in os.listdir(inputDir)]):
VidperSub = []
for vid in sorted([inrfile for inrfile in os.listdir(inputDir + sub)]):
path = inputDir + sub + '/' + vid + '/' # image loading path
if path in listOfIgnoredSamples:
print(path)
continue
imgList = readinput(path)
collectinglabel(table, sub[3:],
vid.split(".")[0], workplace + 'Classification/',
dB, objective_flag)
table_df = pd.DataFrame(table)
apexInd = table_df[table_df[0] == sub[-2:]]
apexInd = int(apexInd[apexInd[1] == vid][3])
imgON = cv2.imread(imgList[0])
imgApex = cv2.imread(imgList[apexInd])
if channel == 1:
imgON = cv2.cvtColor(imgON, cv2.COLOR_BGR2GRAY)
imgApex = cv2.cvtColor(imgApex, cv2.COLOR_BGR2GRAY)
if resizedFlag == 1:
imgON = cv2.resize(imgON, (spatial_size, spatial_size))
imgApex = cv2.resize(imgApex, (spatial_size, spatial_size))
FrameperVid = imgON.flatten()
FrameperVid = np.vstack((FrameperVid, imgApex.flatten()))
VidperSub.append(FrameperVid)
SubperdB.append(VidperSub)
return SubperdB
continue
imgList = readinput(path, dB)
numFrame = len(imgList)
if resizedFlag == 1:
col = w
row = r
else:
img = cv2.imread(imgList[0])
[row, col, _l] = img.shape
## read the label for each input video
collectinglabel(table, sub[3:], vid, workplace + 'Classification/', dB)
for var in range(numFrame):
img = cv2.imread(imgList[var])
[_, _, dim] = img.shape
if dim == 3:
# pass
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if resizedFlag == 1:
# in resize function, [col,row]
img = cv2.resize(img, (col, row))
if var == 0:
FrameperVid = img.flatten()
def Read_Input_Images_SAMM_CASME(inputDir, filteredSamples, ignoredSamples, dB,
resizedFlag, table, workplace, spatial_size,
channel, objective_flag):
# r=224; w=224
r = w = spatial_size
SubperdB = []
# cross-checking parameter
subperdb_id = []
for sub in sorted([infile for infile in os.listdir(inputDir)]):
VidperSub = []
vid_id = np.empty([0])
for vid in sorted([inrfile for inrfile in os.listdir(inputDir + sub)]):
path = inputDir + sub + '/' + vid + '/'
# print(len(filteredSamples))
# print(filteredSamples)
# print("bohaha")
# filtered samples are samples needed
if path not in filteredSamples:
# print(path)
continue
# print(dB)
# print(path)
imgList = readinput(path)
numFrame = len(imgList)
# print(numFrame)
if resizedFlag == 1:
col = w
row = r
else:
img = cv2.imread(imgList[0])
[row, col, _l] = img.shape
## ##read the label for each input video
# print(sub[3:])
collectinglabel(table, sub, vid, workplace + 'Classification/', dB,
objective_flag)
for var in range(numFrame):
img = cv2.imread(imgList[var])
[_, _, dim] = img.shape
if channel == 1:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if resizedFlag == 1:
#in resize function, [col,row]
img = cv2.resize(img, (col, row))
if var == 0:
FrameperVid = img.flatten()
else:
FrameperVid = np.vstack((FrameperVid, img.flatten()))
vid_id = np.append(vid_id, imgList[var]) # <--cross-check
VidperSub.append(FrameperVid)
subperdb_id.append(vid_id) # <--cross-check
# print(subperdb_id)
# if len(VidperSub) > 0:
# print(len(VidperSub))
SubperdB.append(VidperSub)
# return SubperdB, vid_id, subperdb_id
return SubperdB
