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()
#Instantiation classification detection, incoming om model path, model input width and height parameters
classify = Classify(acl_resource, MODEL_PATH, MODEL_WIDTH, MODEL_HEIGHT)
#Get the picture storage directory from the parameters, and infer picture by 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 IMG_EXT
]
#Create a directory and save the infer results
if not os.path.isdir(os.path.join(SRC_PATH, "../outputs")):
os.mkdir(os.path.join(SRC_PATH, "../outputs"))
for image_file in images_list:
#read the picture
image = AclImage(image_file)
image_dvpp = image.copy_to_dvpp()
#preprocess image
resized_image = classify.pre_process(image_dvpp)
print("pre process end")
#inference
result = classify.inference(resized_image)
#post process
classify.post_process(result, image_file)
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()
#Instance classification detection, pass into the OM model storage path, model input width and height parameters
classify = Classify(acl_resource, MODEL_PATH, MODEL_WIDTH, MODEL_HEIGHT)
#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 IMG_EXT
]
#Create a directory to store the inference results
if not os.path.isdir('../outputs'):
os.mkdir('../outputs')
resized_image_list = []
batch_image_files = []
num = 0
batch_amount = len(images_list) // BATCH
left = len(images_list) % BATCH
for image_file in images_list:
num += 1
#Read the pictures
image = AclImage(image_file)
image_dvpp = image.copy_to_dvpp()
#preprocess image
resized_image = classify.pre_process(image_dvpp)
print("pre process end")
batch_image_files.append(image_file)
resized_image_list.append(resized_image)
if batch_amount > 0:
#Each set of BATCH pictures, reasoning and post-processing
if num == BATCH:
#Reasoning pictures
result = classify.inference(resized_image_list, BATCH)
#process inference results
classify.post_process(result, batch_image_files, BATCH)
batch_amount -= 1
num = 0
batch_image_files = []
resized_image_list = []
else:
#remaining images are inferred and post-processed
if num == left:
#Reasoning pictures
result = classify.inference(resized_image_list, BATCH)
#The inference results are processed
classify.post_process(result, batch_image_files, left)