def main():
if (len(sys.argv) != 2):
print("The App arg is invalid")
exit(1)
acl_resource = AclLiteResource()
acl_resource.init()
model = AclLiteModel(MODEL_PATH)
dvpp = AclLiteImageProc(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, "../out")):
os.mkdir(os.path.join(SRC_PATH, "../out"))
image_info = construct_image_info()
for image_file in images_list:
image = AclLiteImage(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, eg: python3.6 classify.py ../data/")
exit(1)
acl_resource = AclLiteResource()
acl_resource.init()
#Instantiation classification detection, incoming om model path, model input width and height parameters
classify = Classify(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, "../out")):
os.mkdir(os.path.join(SRC_PATH, "../out"))
for image_file in images_list:
#preprocess image
resized_image = classify.pre_process(image_file)
print("pre process end")
#inference
result = classify.inference(resized_image)
#post process
classify.post_process(result, image_file)
def main():
"""
acl resource initialization
"""
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
#ACL resource initialization
acl_resource = AclLiteResource()
acl_resource.init()
model = AclLiteModel(model_path)
images_list = [
os.path.join(INPUT_DIR, img) for img in os.listdir(INPUT_DIR)
if os.path.splitext(img)[1] in IMG_EXT
]
for pic in images_list:
print("pic: ", pic)
bgr_img = cv.imread(pic).astype(np.float32)
data = preprocess(bgr_img)
result_list = model.execute([data])
postprocess(result_list, pic, bgr_img)
print("Execute end")
def main():
"""
acl resource initialization
"""
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
#ACL resource initialization
acl_resource = AclLiteResource()
acl_resource.init()
model = AclLiteModel(model_path)
images_list = [
os.path.join(INPUT_DIR, img) for img in os.listdir(INPUT_DIR)
if os.path.splitext(img)[1] in IMG_EXT
]
for pic in images_list:
orig_shape, orig_l, l_data = preprocess(pic)
result_list = model.execute([
l_data,
])
postprocess(result_list, pic, orig_shape, orig_l)
break
print("Execute end")
def main():
"""
main
"""
if (len(sys.argv) != 2):
print("The App arg is invalid")
exit(1)
acl_resource = AclLiteResource()
acl_resource.init()
crowdcount = CrowdCount(MODEL_PATH, MODEL_WIDTH, MODEL_HEIGHT)
ret = crowdcount.init()
if not os.path.isdir(os.path.join(SRC_PATH, "../out")):
os.mkdir(os.path.join(SRC_PATH, "../out"))
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 constants.IMG_EXT
]
for image_file in images_list:
image = AclLiteImage(image_file)
crop_and_paste_image = crowdcount.pre_process(image)
print("pre process end")
result = crowdcount.inference([crop_and_paste_image])
result_img_encode = crowdcount.post_process(crop_and_paste_image,
result, image_file)
return result_img_encode
def main():
"""
Program execution
"""
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
acl_resource = AclLiteResource() # acl intialize
acl_resource.init()
model = AclLiteModel(model_path) # load model
src_dir = os.listdir(INPUT_DIR)
for pic in src_dir:
if not pic.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg',
'.pbm', '.pgm', '.ppm', '.tif', '.tiff')):
print('it is not a picture, %s, ignore this file and continue,' %
pic)
continue
pic_path = os.path.join(INPUT_DIR, pic)
RGB_image, o_h, o_w = pre_process(pic_path) # preprocess
start_time = time.time()
result_list = model.execute([
RGB_image,
]) # inferring
end_time = time.time()
print('pic:{}'.format(pic))
print('pic_size:{}x{}'.format(o_h, o_w))
print('time:{}ms'.format(int((end_time - start_time) * 1000)))
print('\n')
post_process(result_list, pic, o_h, o_w) # postprocess
print('task over')
def main():
"""main"""
#acl init
if (len(sys.argv) != 2):
print("The App arg is invalid")
exit(1)
acl_resource = AclLiteResource()
acl_resource.init()
model = AclLiteModel(MODEL_PATH)
dvpp = AclLiteImageProc(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, "../out")):
os.mkdir(os.path.join(SRC_PATH, "../out"))
#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(model_path, frames_input_src, output_dir):
"""main"""
#initialize acl runtime
acl_resource = AclLiteResource()
acl_resource.init()
## Prepare Model ##
# parameters for model path and model inputs
model_parameters = {
'model_dir': model_path,
'width': 368, # model input width
'height': 368, # model input height
}
# perpare model instance: init (loading model from file to memory)
# model_processor: preprocessing + model inference + postprocessing
model_processor = ModelProcessor(acl_resource, model_parameters)
## Get Input ##
# Read the image input using OpenCV
img_original = cv2.imread(frames_input_src)
## Model Prediction ##
# model_processor.predict: processing + model inference + postprocessing
# canvas: the picture overlayed with human body joints and limbs
canvas = model_processor.predict(img_original)
# Save the detected results
cv2.imwrite(os.path.join(output_dir, "test_output.jpg"), canvas)
def preandinfer(image_queue, images_list):
acl_start = time.time()
#print('Start preandinfer ')
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
acl_resource = AclLiteResource()
acl_resource.init()
dvpp_ = AclLiteImageProc()
model = AclLiteModel(model_path)
print('------------------------------acl processing time',
time.time() - acl_start)
for pic in images_list:
image = AclLiteImage(pic)
image_dvpp = image.copy_to_dvpp()
yuv_image = dvpp_.jpegd(image_dvpp)
resized_image = dvpp_.resize(yuv_image, MODEL_WIDTH, MODEL_HEIGHT)
result_list = model.execute([
resized_image,
])
data = ProcData(result_list, pic, OUTPUT_DIR)
image_queue.put(data)
post_num = 6
while (post_num):
post_num -= 1
data = "Post process thread exit"
image_queue.put(data)
print('End preandinfer')
print('------------------------------preandinfer time',
time.time() - acl_start)
def main():
"""
acl resource initialization
"""
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
#ACL resource initialization
acl_resource = AclLiteResource()
acl_resource.init()
dvpp_ = AclLiteImageProc()
model = AclLiteModel(model_path)
images_list = [os.path.join(INPUT_DIR, img)
for img in os.listdir(INPUT_DIR)
if os.path.splitext(img)[1] in IMG_EXT]
print(images_list)
start = time.time()
for pic in images_list:
image = AclLiteImage(pic)
l_data = preprocess(image, dvpp_)
result_list = inference(model, [l_data,])
postprocess(result_list, pic, OUTPUT_DIR)
def main():
"""
main
"""
if (len(sys.argv) != 2):
print("The App arg is invalid")
exit(1)
acl_resource = AclLiteResource()
acl_resource.init()
model = AclLiteModel(MODEL_PATH)
data_dir = sys.argv[1]
data_list = [
os.path.join(data_dir, testdata) for testdata in os.listdir(data_dir)
if os.path.splitext(testdata)[1] in ['.bin']
]
#Create a directory to store the inference results
if not os.path.isdir(os.path.join(SRC_PATH, "../out")):
os.mkdir(os.path.join(SRC_PATH, "../out"))
for data_file in data_list:
data_raw = np.fromfile(data_file, dtype=np.float32)
input_data = data_raw.reshape(16, MODEL_WIDTH, MODEL_HEIGHT, 3).copy()
result = model.execute([
input_data,
])
post_process(result, data_file)
print("process end")
def main():
"""
Program execution with picture directory parameters
"""
if (len(sys.argv) != 2):
print("The App arg is invalid")
exit(1)
acl_resource = AclLiteResource()
acl_resource.init()
model = AclLiteModel(MODEL_PATH)
dvpp = AclLiteImageProc(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('../out'):
os.mkdir('../out')
image_info = construct_image_info()
for image_file in images_list:
#read picture
image = AclLiteImage(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 main():
"""
Function description:
Main function
"""
acl_resource = AclLiteResource()
acl_resource.init()
detector = Yolov3(acl_resource, MODEL_PATH, MODEL_WIDTH, MODEL_HEIGHT)
video_decoders, postprocessor = create_threads(detector)
if video_decoders is None:
log_error("Please check the configuration in %s is valid" %
(COCO_DETEC_CONF))
return
while True:
all_process_fin = True
for decoder in video_decoders:
ret, data = decoder.get_data()
if ret == False:
continue
if data:
detect_results = detector.execute(data)
postprocessor.process(data, detect_results)
all_process_fin = False
if all_process_fin:
log_info("all video decoder finish")
break
postprocessor.exit()
log_info("sample execute end")
def main():
acl_resource = AclLiteResource()
acl_resource.init()
server = TcpServer()
perception = ChessStatusPerception(MODEL_PATH)
perception.Init()
while True:
print("Waiting for client connection...")
conn, addr = server.s.accept()
while True:
print("Waiting for receive message...")
length = server.recvall(conn, 16) # get the length of img file
if length is None:
conn.close()
break
stringData = server.recvall(
conn, int(length)) # according to the length, get the img file
data = np.frombuffer(stringData, np.uint8)
decimg = cv2.imdecode(data,
cv2.IMREAD_COLOR) # decode data to image
print("Received Image Shape: ", decimg.shape)
chessStatus, chessStatus_real = perception.Process(decimg)
# ideal chess coordinate
chessStatusStr = '#'.join([str(item) for item in chessStatus])
# real chess coordinate
chessStatus_realStr = '#'.join(
[str(item) for item in chessStatus_real])
sendmsg = chessStatusStr + "&" + chessStatus_realStr
print("Send Message: ", sendmsg)
conn.sendall(sendmsg.encode())
def main():
# check param
if (len(sys.argv) != 3):
print("The App arg is invalid")
exit(1)
# get all pictures
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
]
acl_resource = AclLiteResource()
acl_resource.init()
# instantiation Cartoonization object
cartoonization = Cartoonization(MODEL_PATH, MODEL_WIDTH, MODEL_HEIGHT)
ret = cartoonization.init()
utils.check_ret("Cartoonization.init ", ret)
# create dir to save result
if not os.path.isdir('../out'):
os.mkdir('../out')
for image_file in images_list:
# preprocess
image = AclLiteImage(image_file)
test_img = cartoonization.pre_process(image)
# inference
y_pred = cartoonization.inference([
test_img,
])
# postprocess
cartoonization.post_process(y_pred, image_file)
def main(image_dirs, masks_dirs):
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
#acl init
acl_resource = AclLiteResource()
acl_resource.init()
stream, ret = acl.rt.create_stream()
check_ret("acl.rt.create_stream", ret)
#load model
model = AclLiteModel(MODEL_PATH)
paths_img, paths_mask = get_imgs_masks_file_list(image_dirs, masks_dirs)
for i in range(len(paths_img)):
print('==========')
raw_img, raw_mask = readimages(paths_img[i], paths_mask[i])
print("file: % s, shape= % s" % (paths_img[i], raw_img.shape))
(img_large, mask_large, img_512, mask_512) = pre_process(raw_img, raw_mask)
inpainted_512, attention, mask_512_new = inference(model,[img_512, mask_512,])
# post-processing
res_raw_size = post_process(raw_img, img_large, \
mask_large, inpainted_512[0], img_512, mask_512_new[0], attention[0], stream)
filename = os.path.join(OUTPUT_DIR, 'outpaint_' + os.path.basename(paths_img[i]))
cv2.imwrite(filename, res_raw_size)
print("Execute end")
def test(args):
# Step 1: initialize ACL and ACL runtime
acl_resource = AclLiteResource()
# 1.2: one line of code, call the 'init' function of the AclLiteResource object, to initilize ACL and ACL runtime
acl_resource.init()
# Step 2: Load models
mot_model = AclLiteModel('../model/mot_v2.om')
dataloader = loader.LoadImages(args.test_img)
# initialize tracker
tracker = JDETracker(args, mot_model, frame_rate=30)
timer = Timer()
results = []
# img: h w c; 608 1088 3
# img0: c h w; 3 608 1088
for frame_id, (path, img, img0) in enumerate(dataloader):
if frame_id % 20 == 0 and frame_id != 0:
print('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking, start tracking timer
timer.tic()
# list of Tracklet; see multitracker.STrack
online_targets = tracker.update(np.array([img]), img0)
# prepare for drawing, get all bbox and id
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > args.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
timer.toc()
# draw bbox and id
online_im = vis.plot_tracking(img0,
online_tlwhs,
online_ids,
frame_id=frame_id,
fps=1. / timer.average_time)
cv2.imwrite(os.path.join('../data', 'test_output.jpg'), online_im)
# verify if result is expected
result = image_contrast('../data/test_output.jpg', args.verify_img)
print(result)
if (result > 420 or result < 0):
print("Similarity Test Fail!")
sys.exit(1)
else:
print("Similarity Test Pass!")
sys.exit(0)
def main(model_path):
"""main"""
## Initialization ##
#initialize acl runtime
acl_resource = AclLiteResource()
acl_resource.init()
## Prepare Model ##
# parameters for model path and model inputs
model_parameters = {
'model_dir': model_path,
'width': 368, # model input width
'height': 368, # model input height
}
# perpare model instance: init (loading model from file to memory)
# model_processor: preprocessing + model inference + postprocessing
model_processor = ModelProcessor(acl_resource, model_parameters)
## Get Input ##
# Initialize Camera
cap = CameraCapture(camera_id=0, fps=10)
## Set Output ##
# open the presenter channel
chan = presenter_channel.open_channel(BODYPOSE_CONF)
if chan is None:
print("Open presenter channel failed")
return
while True:
## Read one frame from Camera ##
img_original = cap.read()
if not img_original:
print('Error: Camera read failed')
break
# Camera Input (YUV) to RGB Image
img_original = img_original.byte_data_to_np_array()
img_original = YUVtoRGB(img_original)
# img_original = cv2.flip(img_original, 1)
## Model Prediction ##
# model_processor.predict: processing + model inference + postprocessing
# canvas: the picture overlayed with human body joints and limbs
canvas = model_processor.predict(img_original)
## Present Result ##
# convert to jpeg image for presenter server display
_, jpeg_image = cv2.imencode('.jpg', canvas)
# construct AclLiteImage object for presenter server
jpeg_image = AclLiteImage(jpeg_image, img_original.shape[0],
img_original.shape[1], jpeg_image.size)
# send to presenter server
chan.send_detection_data(img_original.shape[0], img_original.shape[1],
jpeg_image, [])
# release the resources
cap.release()
def main():
"""
Program execution with picture directory parameters
"""
if (len(sys.argv) != 2):
print("The App arg is invalid")
exit(1)
acl_resource = AclLiteResource()
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('../out'):
os.mkdir('../out')
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 = AclLiteImage(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)
def main():
"""
acl resource initialization
"""
acl_resource = AclLiteResource()
acl_resource.init()
#load model
model = AclLiteModel(model_path)
chan = presenter_channel.open_channel(COLORIZATION_CONF)
if chan is None:
print("Open presenter channel failed")
return
lenofUrl = len(sys.argv)
if lenofUrl <= 1:
print("[ERROR] Please input mp4/Rtsp URL")
exit()
elif lenofUrl >= 3:
print("[ERROR] param input Error")
exit()
URL = sys.argv[1]
URL1 = re.match('rtsp://', URL)
URL2 = re.search('.mp4', URL)
if URL1 is None and URL2 is None:
print("[ERROR] should input correct URL")
exit()
cap = cv.VideoCapture(URL)
#Gets the total frames
frames_num = cap.get(7)
currentFrames = 0
while True:
#read image
ret, frame = cap.read()
if ret is not True:
print("read None image, break")
break
if currentFrames == frames_num - 1:
currentFrames = 0
cap.set(1, 0)
currentFrames += 1
#Gets the L channel value
orig_shape, orig_l, l_data = preprocess(frame)
result_list = model.execute([
l_data,
])
result_jpeg = postprocess(result_list, orig_shape, orig_l)
chan.send_image(orig_shape[0], orig_shape[1], result_jpeg)
def main():
acl_resource = AclLiteResource()
acl_resource.init()
detect = VggSsd(acl_resource, MODEL_WIDTH, MODEL_HEIGHT)
model = AclLiteModel(MODEL_PATH)
chan = presenter_channel.open_channel(MASK_DETEC_CONF)
if chan is None:
print("Open presenter channel failed")
return
lenofUrl = len(sys.argv)
if lenofUrl <= 1:
print("[ERROR] Please input h264/Rtsp URL")
exit()
elif lenofUrl >= 3:
print("[ERROR] param input Error")
exit()
URL = sys.argv[1]
URL1 = re.match('rtsp://', URL)
URL2 = re.search('.h264', URL)
if URL1 is None and URL2 is None:
print("[ERROR] should input correct URL")
exit()
cap = video.VideoCapture(URL)
while True:
# Read a frame
ret, image = cap.read()
if (ret != 0) or (image is None):
print("read None image, break")
break
#pre process
model_input = detect.pre_process(image)
if model_input is None:
print("Pre process image failed")
break
# inference
result = model.execute(model_input)
if result is None:
print("execute mode failed")
break
# post process
jpeg_image, detection_list = detect.post_process(result, image)
if jpeg_image is None:
print("The jpeg image for present is None")
break
chan.send_detection_data(CAMERA_FRAME_WIDTH, CAMERA_FRAME_HEIGHT,
jpeg_image, detection_list)
def test_200dk():
"""
Test the output of ChessStatusPerception module.
"""
acl_resource = AclLiteResource()
acl_resource.init()
perception = ChessStatusPerception(MODEL_PATH)
perception.Init()
# load input
print("input_dir = ", INPUT_DIR)
img = os.path.join(INPUT_DIR, "test.jpg")
image = cv2.imread(img)
# execute the chessboard status perception function
chessStatus, chessStatus_real = perception.Process(image)
return chessStatus
def main():
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
#ACL resource initialization
acl_resource = AclLiteResource()
acl_resource.init()
#load model
model = AclLiteModel(MODEL_PATH)
images_list = [os.path.join(INPUT_DIR, img)
for img in os.listdir(INPUT_DIR)
if os.path.splitext(img)[1] in const.IMG_EXT]
#Read images from the data directory one by one for reasoning
for pic in images_list:
#read image
bgr_img = cv.imread(pic)
#preprocess
data, orig = preprocess(pic)
#Send into model inference
result_list = model.execute([data,])
#Process inference results
result_return = post_process(result_list, orig)
print("result = ", result_return)
#Process lane line
frame_with_lane = preprocess_frame(bgr_img)
distance = np.zeros(shape=(len(result_return['detection_classes']), 1))
for i in range(len(result_return['detection_classes'])):
box = result_return['detection_boxes'][i]
class_name = result_return['detection_classes'][i]
confidence = result_return['detection_scores'][i]
distance[i] = calculate_position(bbox=box, transform_matrix=perspective_transform,
warped_size=WARPED_SIZE, pix_per_meter=pixels_per_meter)
label_dis = '{} {:.2f}m'.format('dis:', distance[i][0])
cv.putText(frame_with_lane, label_dis, (int(box[1]) + 10, int(box[2]) + 15),
cv.FONT_ITALIC, 0.6, colors[i % 6], 1)
cv.rectangle(frame_with_lane, (int(box[1]), int(box[0])), (int(box[3]), int(box[2])), colors[i % 6])
p3 = (max(int(box[1]), 15), max(int(box[0]), 15))
out_label = class_name
cv.putText(frame_with_lane, out_label, p3, cv.FONT_ITALIC, 0.6, colors[i % 6], 1)
output_file = os.path.join(OUTPUT_DIR, "out_" + os.path.basename(pic))
print("output:%s" % output_file)
cv.imwrite(output_file, frame_with_lane)
print("Execute end")
def main():
"""
main
"""
image_dir = os.path.join(currentPath, "data")
images_list = [
os.path.join(image_dir, img) for img in os.listdir(image_dir)
if os.path.splitext(img)[1] in constants.IMG_EXT
]
acl_resource = AclLiteResource()
acl_resource.init()
hpa = Hpa(MODEL_PATH, MODEL_WIDTH, MODEL_HEIGHT)
ret = hpa.init()
utils.check_ret("hpa init ", ret)
# Create a directory to save inference results
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
for image_file in images_list:
image_name = os.path.join(image_dir, os.path.basename(image_file))
print('====' + image_name + '====')
# read image
im = Image.open(image_name)
if len(im.split()) != 3:
print('warning: "{}" is not a color image and will be ignored'.
format(image_name))
continue
# Preprocess the picture
resized_image = hpa.pre_process(im)
# Inferencecd
result = hpa.inference([
resized_image,
])
# # Post-processing
hpa.post_process(result, image_name)
def main():
"""
main
"""
image_dir = os.path.join(currentPath, "data")
if not os.path.exists(OUT_DIR):
os.mkdir(OUT_DIR)
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
if not os.path.exists(MASK_DIR):
os.mkdir(MASK_DIR)
acl_resource = AclLiteResource()
acl_resource.init()
seg = Seg(MODEL_PATH, MODEL_WIDTH, MODEL_HEIGHT)
ret = seg.init()
utils.check_ret("seg.init ", ret)
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
]
for image_file in images_list:
image_name = os.path.basename(image_file)
if image_name != 'background.jpg':
print('====' + image_name + '====')
# read image
image = AclLiteImage(image_file)
# Preprocess the picture
resized_image = seg.pre_process(image)
# Inference
result = seg.inference([
resized_image,
])
# Post-processing
mask = seg.post_process(result, image_name)
# Fusion of segmented portrait and background image
background_replace(os.path.join(image_dir, 'background.jpg'), \
image_file, os.path.join(MASK_DIR, image_name))
def main():
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
#ACL resource initialization
acl_resource = AclLiteResource()
acl_resource.init()
#load model
model = AclLiteModel(MODEL_PATH)
images_list = [
os.path.join(INPUT_DIR, img) for img in os.listdir(INPUT_DIR)
if os.path.splitext(img)[1] in const.IMG_EXT
]
#Read images from the data directory one by one for reasoning
for pic in images_list:
#read image
bgr_img = cv.imread(pic)
#preprocess
data, orig = preprocess(pic)
#Send into model inference
result_list = model.execute([
data,
])
#Process inference results
result_return = post_process(result_list, orig)
print("result = ", result_return)
for i in range(len(result_return['detection_classes'])):
box = result_return['detection_boxes'][i]
class_name = result_return['detection_classes'][i]
confidence = result_return['detection_scores'][i]
cv.rectangle(bgr_img, (int(box[1]), int(box[0])),
(int(box[3]), int(box[2])), colors[i % 6])
p3 = (max(int(box[1]), 15), max(int(box[0]), 15))
out_label = class_name
cv.putText(bgr_img, out_label, p3, cv.FONT_ITALIC, 0.6,
colors[i % 6], 1)
output_file = os.path.join(OUTPUT_DIR, "out_" + os.path.basename(pic))
print("output:%s" % output_file)
cv.imwrite(output_file, bgr_img)
print("Execute end")
def main():
"""
acl resource initialization
"""
acl_resource = AclLiteResource()
acl_resource.init()
model = AclLiteModel(model_path)
with codecs.open(dict_path, 'r', 'utf-8') as reader:
for line in reader:
token = line.strip()
token_dict[token] = len(token_dict)
with open(sample_path, "r") as f:
text = f.read()
with open(label_path, "r", encoding="utf-8") as f:
label_dict = json.loads(f.read())
X1, X2 = preprocess(text)
X1 = np.ascontiguousarray(X1, dtype='float32')
X2 = np.ascontiguousarray(X2, dtype='float32')
X1 = np.expand_dims(X1, 0)
X2 = np.expand_dims(X2, 0)
s_time = time.time()
result_list = model.execute([X1, X2])
e_time = time.time()
print(result_list)
y = postprocess(result_list)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
save_to_file(output_dir + 'prediction_label.txt', label_dict[str(y)])
print("Original text: %s" % text)
print("Prediction label: %s" % label_dict[str(y)])
print("Cost time:", e_time - s_time)
print("Execute end")
def main():
"""
Program execution with picture directory parameters
"""
if (len(sys.argv) != 3):
print("The App arg is invalid. The style you can choose: \
xingkong/tangguo/bijiasuo/worksoldiers.eg: python3 main.py ../data xingkong"
)
exit(1)
style_type = sys.argv[2]
if style_type == "tangguo":
model_path = '../model/tangguo_fp32_nchw_no_aipp.om'
elif style_type == "bijiasuo":
model_path = '../model/bijiasuo_fp32_nchw_no_aipp.om'
elif style_type == "worksoldiers":
model_path = '../model/work_soldiers_fp32_nchw_no_aipp.om'
elif style_type == "xingkong":
model_path = '../model/xingkong1_fp32_nchw_no_aipp.om'
acl_resource = AclLiteResource()
acl_resource.init()
model = AclLiteModel(model_path)
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('../out'):
os.mkdir('../out')
for image_file in images_list:
orig_shape, rgb_data = pre_process(image_file)
print("pre process end")
result_list = model.execute([rgb_data])
print("Execute end")
post_process(result_list, orig_shape, image_file)
print("postprocess end")
def main():
"""main"""
#acl init
if (len(sys.argv) != 3):
print("The App arg is invalid")
exit(1)
acl_resource = AclLiteResource()
acl_resource.init()
model = AclLiteModel(MODEL_PATH)
#x=296
#y=330
#x=410
#y=664
coordinate = [-1, -1]
#From the parameters of the picture storage directory, reasoning by a picture
coordinate = [int(sys.argv[1]), int(sys.argv[2])]
if not os.path.exists(DATA_PATH):
os.mkdir(DATA_PATH)
if not os.path.exists(MASK_PATH):
os.mkdir(MASK_PATH)
if not os.path.exists(OUTPUT_PATH):
os.mkdir(OUTPUT_PATH)
images_list = [os.path.join(DATA_PATH, img)
for img in os.listdir(DATA_PATH)
if os.path.splitext(img)[1] in const.IMG_EXT]
#infer picture
for pic in images_list:
#get pic data
orig_shape, l_data, im_info = preprocess(pic)
#inference
result_list = model.execute([l_data, im_info])
#postprocess
postprocess(result_list, pic, coordinate, OUTPUT_PATH)
print("Execute end")
def main():
"""
main
"""
#create output directory
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
#acl init
acl_resource = AclLiteResource()
acl_resource.init()
#load model
model = AclLiteModel(MODEL_PATH)
src_dir = os.listdir(INPUT_DIR)
#infer picture
for pic in src_dir:
if not pic.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg',
'.pbm', '.pgm', '.ppm', '.tif', '.tiff')):
print('it is not a picture, %s, ignore this file and continue,' %
pic)
continue
#read picture
pic_path = os.path.join(INPUT_DIR, pic)
bgr_img = cv2.imread(pic_path)
#get pic data
orig_shape, test_img = preprocess(bgr_img)
#inference
result_list = model.execute([
test_img,
])
#postprocess
postprocess(result_list, pic)
