def main():
"""main"""
#acl init
if (len(sys.argv) != 2):
print("The App arg is invalid")
exit(1)
acl_resource = AclResource()
acl_resource.init()
model = Model(MODEL_PATH)
dvpp = Dvpp(acl_resource)
#From the parameters of the picture storage directory, reasoning by a picture
image_dir = sys.argv[1]
images_list = [
os.path.join(image_dir, img) for img in os.listdir(image_dir)
if os.path.splitext(img)[1] in const.IMG_EXT
]
if not os.path.isdir(os.path.join(SRC_PATH, "../outputs")):
os.mkdir(os.path.join(SRC_PATH, "../outputs"))
#infer picture
for pic in images_list:
#get pic data
orig_shape, l_data = preprocess(pic)
#inference
result_list = model.execute([l_data])
#postprocess
postprocess(result_list, pic, orig_shape, pic)
print("Execute end")
def main():
if (len(sys.argv) != 2):
print("The App arg is invalid")
exit(1)
acl_resource = AclResource()
acl_resource.init()
model = Model(MODEL_PATH)
dvpp = Dvpp(acl_resource)
image_dir = sys.argv[1]
images_list = [
os.path.join(image_dir, img) for img in os.listdir(image_dir)
if os.path.splitext(img)[1] in const.IMG_EXT
]
#Create a directory to store the inference results
if not os.path.isdir(os.path.join(SRC_PATH, "../outputs")):
os.mkdir(os.path.join(SRC_PATH, "../outputs"))
image_info = construct_image_info()
for image_file in images_list:
image = AclImage(image_file)
resized_image = pre_process(image, dvpp)
print("pre process end")
result = model.execute([
resized_image,
])
post_process(result, image_file)
print("process " + image_file + " end")
def main():
"""
Program execution with picture directory parameters
"""
if (len(sys.argv) != 2):
print("The App arg is invalid")
exit(1)
acl_resource = AclResource()
acl_resource.init()
model = Model(MODEL_PATH)
dvpp = Dvpp(acl_resource)
#From the parameters of the picture storage directory, reasoning by a picture
image_dir = sys.argv[1]
images_list = [os.path.join(image_dir, img)
for img in os.listdir(image_dir)
if os.path.splitext(img)[1] in const.IMG_EXT]
#Create a directory to store the inference results
if not os.path.isdir('../outputs'):
os.mkdir('../outputs')
image_info = construct_image_info()
for image_file in images_list:
#read picture
image = AclImage(image_file)
#preprocess image
resized_image = pre_process(image, dvpp)
print("pre process end")
#reason pictures
result = model.execute([resized_image, image_info])
#process resresults
post_process(result, image, image_file)
def __init__(self, acl_resource, model_width, model_height):
self._acl_resource = acl_resource
self._model_width = model_width
self._model_height = model_height
#Use dvpp to process images, when using opencv or PIL,
# you don't need to create a dvpp instance
self._dvpp = Dvpp(acl_resource)
def init(self):
"""
init
"""
self._dvpp = Dvpp()
self._model = Model(self._model_path)
return constants.SUCCESS
def init(self):
"""
Initialize
"""
self._dvpp = Dvpp()
# Load model
self._model = Model(self._model_path)
return const.SUCCESS
def _thread_entry(self, args_list):
self._context, ret = acl.rt.create_context(0)
utils.check_ret("acl.rt.create_context", ret)
self._cap = video.AclVideo(self._stream_name)
self._dvpp = Dvpp()
self._status = STATUS_PREPROC_RUNNING
frame_cnt = 0
while self._status == STATUS_PREPROC_RUNNING:
ret, image = self._cap.read()
if ret or (image is None):
if ret == const.VIDEO_DECODE_FINISH:
log_info("Video %s decode finish"%(self._stream_name))
self._status = STATUS_PREPROC_EXIT
else:
log_info("Video %s decode failed"%(self._stream_name))
self._status = STATUS_PREPROC_ERROR
break
if (int(frame_cnt) % 5 == 0):
self._process_frame(image)
time.sleep(0.0)
self._thread_exit()
def main():
"""main"""
if (len(sys.argv) != 2):
print("The App arg is invalid")
exit(1)
acl_resource = AclResource()
acl_resource.init()
model = Model(MODEL_PATH)
dvpp = Dvpp(acl_resource)
image_dir = sys.argv[1]
images_list = [
os.path.join(image_dir, img) for img in os.listdir(image_dir)
if os.path.splitext(img)[1] in const.IMG_EXT
]
# Create a directory to save inference results
if not os.path.isdir('./outputs'):
os.mkdir('./outputs')
# Create a directory to save the intermediate results of the large image detection
if not os.path.isdir('./bigpic'):
os.mkdir('./bigpic')
# Create a directory to save the results of the big picture cropping inference
outCrop = os.path.join('./bigpic', 'output')
if not os.path.isdir(outCrop):
os.mkdir(outCrop)
# Create a directory, save the large and cropped pictures
cropImg = os.path.join('./bigpic', 'cropimg')
if not os.path.isdir(cropImg):
os.mkdir(cropImg)
image_info = construct_image_info()
for image_file in images_list:
imagename = get_file_name(image_file)
tempfile = os.path.splitext(imagename)[0]
imgdic = {}
imgdic['name'] = imagename
obj_res = []
img = cv2.imread(image_file, -1)
(width, height, depth) = img.shape
if width > 1000 and height > 1000:
# Create a directory to save the divided pictures of each big picture
crop_target = os.path.join(cropImg, tempfile)
if not os.path.isdir(crop_target):
os.mkdir(crop_target)
# Create a directory to save the inference results of each large image
out_target = os.path.join(outCrop, tempfile)
if not os.path.isdir(out_target):
os.mkdir(out_target)
# Large image clipping function
crop_picture(image_file, crop_target)
cropimg_list = [
os.path.join(crop_target, imgs)
for imgs in os.listdir(crop_target)
if os.path.splitext(imgs)[1] in const.IMG_EXT
]
# After the execution of the crop function is over,
# the small picture after the big picture crop should be saved in a folder crop_target
for cropimg_file in cropimg_list:
print("the crop filename is :\t", cropimg_file)
image = AclImage(cropimg_file)
resized_image = pre_process(image, dvpp)
result = model.execute([resized_image, image_info])
resdic = post_process_big(result, image, cropimg_file,
out_target)
obj_res.extend(resdic)
imgdic['object_result'] = obj_res
merge_picture(out_target, tempfile)
# Read in the picture, if the picture size is less than 1000x1000,
# it will be read in and processed normally
else:
print("detect the small picture")
image = AclImage(image_file)
resized_image = pre_process(image, dvpp)
print("pre process end")
result = model.execute([resized_image, image_info])
resdic = post_process(result, image, image_file)
obj_res.extend(resdic)
imgdic['object_result'] = obj_res