Industrial Meter Reader¶
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This notebook shows how to create a industrial meter reader with OpenVINO Runtime. We use the PaddlePaddle pre-trained model PPYOLOv2 and DeepLabV3P to built-up a multiple inference task pipeline:
Run detection model to find the meters, and crop them from the origin photo.
Run segmentation model on these cropped meters to get the pointer and scale instance.
Find the location of the pointer in scale map.
workflow¶
Import¶
import os
import sys
from pathlib import Path
import numpy as np
import math
import cv2
import tarfile
import matplotlib.pyplot as plt
from openvino.runtime import Core
sys.path.append("../utils")
from notebook_utils import download_file, segmentation_map_to_image
Prepare the Model and Test Image¶
Download PPYolov2 and DeepLabV3P pre-trained models from PaddlePaddle community.
MODEL_DIR = "model"
DATA_DIR = "data"
DET_MODEL_LINK = "https://bj.bcebos.com/paddlex/examples2/meter_reader/meter_det_model.tar.gz"
SEG_MODEL_LINK = "https://bj.bcebos.com/paddlex/examples2/meter_reader/meter_seg_model.tar.gz"
DET_FILE_NAME = DET_MODEL_LINK.split("/")[-1]
SEG_FILE_NAME = SEG_MODEL_LINK.split("/")[-1]
IMG_LINK = "https://user-images.githubusercontent.com/91237924/170696219-f68699c6-1e82-46bf-aaed-8e2fc3fa5f7b.jpg"
IMG_FILE_NAME = IMG_LINK.split("/")[-1]
IMG_PATH = Path(f"{DATA_DIR}/{IMG_FILE_NAME}")
os.makedirs(MODEL_DIR, exist_ok=True)
download_file(DET_MODEL_LINK, directory=MODEL_DIR, show_progress=True)
file = tarfile.open(f"model/{DET_FILE_NAME}")
res = file.extractall("model")
if not res:
print(f"Detection Model Extracted to \"./{MODEL_DIR}\".")
else:
print("Error Extracting the Detection model. Please check the network.")
download_file(SEG_MODEL_LINK, directory=MODEL_DIR, show_progress=True)
file = tarfile.open(f"model/{SEG_FILE_NAME}")
res = file.extractall("model")
if not res:
print(f"Segmentation Model Extracted to \"./{MODEL_DIR}\".")
else:
print("Error Extracting the Segmentation model. Please check the network.")
download_file(IMG_LINK, directory=DATA_DIR, show_progress=True)
if IMG_PATH.is_file():
print(f"Test Image Saved to \"./{DATA_DIR}\".")
else:
print("Error Downloading the Test Image. Please check the network.")
model/meter_det_model.tar.gz: 0%| | 0.00/192M [00:00<?, ?B/s]
Detection Model Extracted to "./model".
model/meter_seg_model.tar.gz: 0%| | 0.00/94.9M [00:00<?, ?B/s]
Segmentation Model Extracted to "./model".
data/170696219-f68699c6-1e82-46bf-aaed-8e2fc3fa5f7b.jpg: 0%| | 0.00/183k [00:00<?, ?B/s]
Test Image Saved to "./data".
Configuration¶
Add parameter configuration for reading calculation.
METER_SHAPE = [512, 512]
CIRCLE_CENTER = [256, 256]
CIRCLE_RADIUS = 250
PI = math.pi
RECTANGLE_HEIGHT = 120
RECTANGLE_WIDTH = 1570
TYPE_THRESHOLD = 40
COLORMAP = np.array([[28, 28, 28], [238, 44, 44], [250, 250, 250]])
# There are 2 types of meters in test image datasets
METER_CONFIG = [{
'scale_interval_value': 25.0 / 50.0,
'range': 25.0,
'unit': "(MPa)"
}, {
'scale_interval_value': 1.6 / 32.0,
'range': 1.6,
'unit': "(MPa)"
}]
SEG_LABEL = {'background': 0, 'pointer': 1, 'scale': 2}
Load the Models¶
Initialize the OpenVINO compiled model.
# Initialize OpenVINO Runtime
ie_core = Core()
def model_init(det_model_path, seg_model_path):
"""
Initialize the Detection and Segmentation models
:param:
model (str): model path *.pdmodel
:retuns:
detector: detection compiled model
segmenter: segmentation compiled model
"""
# Load the PaddlePaddle detection model directly
det_model = ie_core.read_model(model=det_model_path)
det_model.reshape({'image': [1, 3, 608, 608], 'im_shape': [1, 2], 'scale_factor': [1, 2]})
detector = ie_core.compile_model(model=det_model, device_name="CPU")
# Load the PaddlePaddle segmentation model directly, the input batch size is dynamic
seg_model = ie_core.read_model(model=seg_model_path)
seg_model.reshape({'image': [-1, 3, 512, 512]})
segmenter = ie_core.compile_model(model=seg_model, device_name="CPU")
return detector, segmenter
Predictor¶
Define a common predictor function for both detection and segmentation models
def predictor(input, compiled_model):
"""
A predictor to run inference
:param:
input: input data
compiled_model: a IE detector or segmenter
:retuns:
result (np.array): model output data
"""
output_layer = compiled_model.output(0)
result = compiled_model(input)[output_layer]
return result
Data Process¶
Including the preprocessing and postprocessing tasks of each model.
def det_preprocess(input_image, target_size):
"""
Preprocessing the input data for detection task
:param:
input_image (np.array): input data
size (int): the image size required by model input layer
:retuns:
img.astype (np.float32): preprocessed image
"""
img = cv2.resize(input_image, (target_size, target_size))
img = np.transpose(img, [2, 0, 1]) / 255
img = np.expand_dims(img, 0)
img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
img -= img_mean
img /= img_std
return img.astype(np.float32)
def filter_bboxes(det_results, score_threshold):
"""
filter out the detection results with low confidence
:param：
det_results (list[dict]): detection results
score_threshold (float)： confidence threshold
:retuns：
filtered_results (list[dict]): filter detection results
"""
filtered_results = []
for i in range(len(det_results)):
if det_results[i, 1] > score_threshold:
filtered_results.append(det_results[i])
return filtered_results
def roi_crop(image, results, scale_x, scale_y):
"""
crop the area of detected meter of original image
:param：
img (np.array)：original image。
det_results (list[dict]): detection results
scale_x (float): the scale value in x axis
scale_y (float): the scale value in y axis
:retuns：
roi_imgs (list[np.array]): the list of meter images
loc (list[int]): the list of meter locations
"""
roi_imgs = []
loc = []
for result in results:
bbox = result[2:]
xmin, ymin, xmax, ymax = [int(bbox[0] * scale_x), int(bbox[1] * scale_y), int(bbox[2] * scale_x), int(bbox[3] * scale_y)]
sub_img = image[ymin:(ymax + 1), xmin:(xmax + 1), :]
roi_imgs.append(sub_img)
loc.append([xmin, ymin, xmax, ymax])
return roi_imgs, loc
def roi_process(input_images, target_size, interp=cv2.INTER_LINEAR):
"""
Prepare the roi image of detection results data
Preprocessing the input data for segmentation task
:param：
input_images (list[np.array])：the list of meter images
target_size (list|tuple)： height and width of resized image， e.g [heigh,width]
interp (int)：the interp method for image reszing
:retuns：
img_list (list[np.array])：the list of processed images
resize_img (list[np.array]): for visualization
"""
img_list = list()
resize_list = list()
for img in input_images:
img_shape = img.shape
scale_x = float(target_size[1]) / float(img_shape[1])
scale_y = float(target_size[0]) / float(img_shape[0])
resize_img = cv2.resize(img, None, None, fx=scale_x, fy=scale_y, interpolation=interp)
resize_list.append(resize_img)
resize_img = resize_img.transpose(2, 0, 1) / 255
img_mean = np.array([0.5, 0.5, 0.5]).reshape((3, 1, 1))
img_std = np.array([0.5, 0.5, 0.5]).reshape((3, 1, 1))
resize_img -= img_mean
resize_img /= img_std
img_list.append(resize_img)
return img_list, resize_list
def erode(seg_results, erode_kernel):
"""
erode the segmentation result to get the more clear instance of pointer and scale
:param：
seg_results (list[dict])：segmentation results
erode_kernel (int): size of erode_kernel
:return：
eroded_results (list[dict])： the lab map of eroded_results
"""
kernel = np.ones((erode_kernel, erode_kernel), np.uint8)
eroded_results = seg_results
for i in range(len(seg_results)):
eroded_results[i] = cv2.erode(seg_results[i].astype(np.uint8), kernel)
return eroded_results
def circle_to_rectangle(seg_results):
"""
switch the shape of label_map from circle to rectangle
:param：
seg_results (list[dict])：segmentation results
:return：
rectangle_meters (list[np.array])：the rectangle of label map
"""
rectangle_meters = list()
for i, seg_result in enumerate(seg_results):
label_map = seg_result
# The size of rectangle_meter is determined by RECTANGLE_HEIGHT and RECTANGLE_WIDTH
rectangle_meter = np.zeros((RECTANGLE_HEIGHT, RECTANGLE_WIDTH), dtype=np.uint8)
for row in range(RECTANGLE_HEIGHT):
for col in range(RECTANGLE_WIDTH):
theta = PI * 2 * (col + 1) / RECTANGLE_WIDTH
# The radius of meter circle will be mapped to the height of rectangle
rho = CIRCLE_RADIUS - row - 1
y = int(CIRCLE_CENTER[0] + rho * math.cos(theta) + 0.5)
x = int(CIRCLE_CENTER[1] - rho * math.sin(theta) + 0.5)
rectangle_meter[row, col] = label_map[y, x]
rectangle_meters.append(rectangle_meter)
return rectangle_meters
def rectangle_to_line(rectangle_meters):
"""
switch the dimension of rectangle label map from 2D to 1D
:param：
rectangle_meters (list[np.array])：2D rectangle OF label_map。
:return：
line_scales (list[np.array])： the list of scales value
line_pointers (list[np.array])：the list of pointers value
"""
line_scales = list()
line_pointers = list()
for rectangle_meter in rectangle_meters:
height, width = rectangle_meter.shape[0:2]
line_scale = np.zeros((width), dtype=np.uint8)
line_pointer = np.zeros((width), dtype=np.uint8)
for col in range(width):
for row in range(height):
if rectangle_meter[row, col] == SEG_LABEL['pointer']:
line_pointer[col] += 1
elif rectangle_meter[row, col] == SEG_LABEL['scale']:
line_scale[col] += 1
line_scales.append(line_scale)
line_pointers.append(line_pointer)
return line_scales, line_pointers
def mean_binarization(data_list):
"""
binarize the data
:param：
data_list (list[np.array])：input data
:return：
binaried_data_list (list[np.array])：output data。
"""
batch_size = len(data_list)
binaried_data_list = data_list
for i in range(batch_size):
mean_data = np.mean(data_list[i])
width = data_list[i].shape[0]
for col in range(width):
if data_list[i][col] < mean_data:
binaried_data_list[i][col] = 0
else:
binaried_data_list[i][col] = 1
return binaried_data_list
def locate_scale(line_scales):
"""
find out the location of center of each scale
:param：
line_scales (list[np.array])：the list of binaried scales value
:return：
scale_locations (list[list])：location of each scale
"""
batch_size = len(line_scales)
scale_locations = list()
for i in range(batch_size):
line_scale = line_scales[i]
width = line_scale.shape[0]
find_start = False
one_scale_start = 0
one_scale_end = 0
locations = list()
for j in range(width - 1):
if line_scale[j] > 0 and line_scale[j + 1] > 0:
if not find_start:
one_scale_start = j
find_start = True
if find_start:
if line_scale[j] == 0 and line_scale[j + 1] == 0:
one_scale_end = j - 1
one_scale_location = (one_scale_start + one_scale_end) / 2
locations.append(one_scale_location)
one_scale_start = 0
one_scale_end = 0
find_start = False
scale_locations.append(locations)
return scale_locations
def locate_pointer(line_pointers):
"""
find out the location of center of pointer
:param：
line_scales (list[np.array])：the list of binaried pointer value
:return：
scale_locations (list[list])：location of pointer
"""
batch_size = len(line_pointers)
pointer_locations = list()
for i in range(batch_size):
line_pointer = line_pointers[i]
find_start = False
pointer_start = 0
pointer_end = 0
location = 0
width = line_pointer.shape[0]
for j in range(width - 1):
if line_pointer[j] > 0 and line_pointer[j + 1] > 0:
if not find_start:
pointer_start = j
find_start = True
if find_start:
if line_pointer[j] == 0 and line_pointer[j + 1] == 0 :
pointer_end = j - 1
location = (pointer_start + pointer_end) / 2
find_start = False
break
pointer_locations.append(location)
return pointer_locations
def get_relative_location(scale_locations, pointer_locations):
"""
match the location of pointer and scales
:param：
scale_locations (list[list])：location of each scale
pointer_locations (list[list])：location of pointer
:return：
pointed_scales (list[dict])： a list of dict with:
'num_scales': total number of scales
'pointed_scale': predicted number of scales
"""
pointed_scales = list()
for scale_location, pointer_location in zip(scale_locations,
pointer_locations):
num_scales = len(scale_location)
pointed_scale = -1
if num_scales > 0:
for i in range(num_scales - 1):
if scale_location[i] <= pointer_location < scale_location[i + 1]:
pointed_scale = i + (pointer_location - scale_location[i]) / (scale_location[i + 1] - scale_location[i] + 1e-05) + 1
result = {'num_scales': num_scales, 'pointed_scale': pointed_scale}
pointed_scales.append(result)
return pointed_scales
def calculate_reading(pointed_scales):
"""
calculate the value of meter according to the type of meter
:param：
pointed_scales (list[list])：predicted number of scales
:return：
readings (list[float])： the list of values read from meter
"""
readings = list()
batch_size = len(pointed_scales)
for i in range(batch_size):
pointed_scale = pointed_scales[i]
# find out the type of meter according the total number of scales
if pointed_scale['num_scales'] > TYPE_THRESHOLD:
reading = pointed_scale['pointed_scale'] * METER_CONFIG[0]['scale_interval_value']
else:
reading = pointed_scale['pointed_scale'] * METER_CONFIG[1]['scale_interval_value']
readings.append(reading)
return readings
Main Function¶
Intialize the model and parameters¶
img_file = f"{DATA_DIR}/{IMG_FILE_NAME}"
det_model_path = f"{MODEL_DIR}/meter_det_model/model.pdmodel"
seg_model_path = f"{MODEL_DIR}/meter_seg_model/model.pdmodel"
erode_kernel = 4
score_threshold = 0.5
seg_batch_size = 2
input_shape = 608
detector, segmenter = model_init(det_model_path, seg_model_path)
image = cv2.imread(img_file)
plt.imshow(image)
<matplotlib.image.AxesImage at 0x7f764d7a40a0>
Run meter detection model¶
Detect the location of the meter and prepare the ROI images for segmentation.
# Prepare the input data for meter detection model
im_shape = np.array([[input_shape, input_shape]]).astype('float32')
scale_factor = np.array([[1, 2]]).astype('float32')
input_image = det_preprocess(image, input_shape)
inputs_dict = {'image': input_image, "im_shape": im_shape, "scale_factor": scale_factor}
# Run meter detection model
det_results = predictor(inputs_dict, detector)
# Filter out the bounding box with low confidence
filtered_results = filter_bboxes(det_results, score_threshold)
# Prepare the input data for meter segmentation model
scale_x = image.shape[1] / input_shape * 2
scale_y = image.shape[0] / input_shape
# Create the individual photo for each detected meter
roi_imgs, loc = roi_crop(image, filtered_results, scale_x, scale_y)
roi_imgs, resize_imgs = roi_process(roi_imgs, METER_SHAPE)
# Create the photos of detection results
if len(resize_imgs) == 2:
roi_stack = np.hstack([resize_imgs[0], resize_imgs[1]])
else:
roi_stack = resize_imgs[0]
if cv2.imwrite(f"{DATA_DIR}/detection_results.jpg", roi_stack):
print("The detection result image has been saved as \"detection_results.jpg\" in data")
plt.imshow(roi_stack)
The detection result image has been saved as "detection_results.jpg" in data
Run meter segmentation model¶
Get the results of segmentation task on detected ROI.
seg_results = list()
num_imgs = len(roi_imgs)
# Run meter segmentation model on all meters detected
for i in range(0, num_imgs, seg_batch_size):
batch = roi_imgs[i : min(num_imgs, i + seg_batch_size)]
seg_result = predictor({"image": np.array(batch)}, segmenter)
seg_results.extend(seg_result)
results = []
for i in range(len(seg_results)):
results.append(np.argmax(seg_results[i], axis=0))
seg_results = erode(results, erode_kernel)
# Create the photos of segmentation results
if len(resize_imgs) == 2:
mask_stack = np.hstack([segmentation_map_to_image(seg_results[0], COLORMAP),
segmentation_map_to_image(seg_results[1], COLORMAP)])
else:
mask_stack = segmentation_map_to_image(seg_results[0], COLORMAP)
if cv2.imwrite(f"{DATA_DIR}/segmentation_results.jpg", mask_stack):
print("The segmentation result image has been saved as \"segmentation_results.jpg\" in data")
plt.imshow(mask_stack)
The segmentation result image has been saved as "segmentation_results.jpg" in data
Postprocess the models result and calculate the final readings¶
Use OpenCV function to find the location of the pointer in scale map.
# Find the pointer location in scale map and calculate the meters reading
rectangle_meters = circle_to_rectangle(seg_results)
line_scales, line_pointers = rectangle_to_line(rectangle_meters)
binaried_scales = mean_binarization(line_scales)
binaried_pointers = mean_binarization(line_pointers)
scale_locations = locate_scale(binaried_scales)
pointer_locations = locate_pointer(binaried_pointers)
pointed_scales = get_relative_location(scale_locations, pointer_locations)
meter_readings = calculate_reading(pointed_scales)
# Plot the rectangle meters
if len(rectangle_meters) == 2:
rectangle_meters_stack = np.hstack([segmentation_map_to_image(rectangle_meters[0], COLORMAP),
segmentation_map_to_image(rectangle_meters[1], COLORMAP)])
else:
rectangle_meters_stack = segmentation_map_to_image(rectangle_meters[0], COLORMAP)
if cv2.imwrite(f"{DATA_DIR}/rectangle_meters.jpg", rectangle_meters_stack):
print("The rectangle_meters result image has been saved as \"rectangle_meters.jpg\" in data")
plt.imshow(rectangle_meters_stack)
The rectangle_meters result image has been saved as "rectangle_meters.jpg" in data
Get the reading result on the meter picture¶
# Create final result photo with meter value
for i in range(len(meter_readings)):
print("Meter {}: {:.3f}".format(i + 1, meter_readings[i]))
result_image = image.copy()
for i in range(len(loc)):
cv2.rectangle(result_image,(loc[i][0], loc[i][1]), (loc[i][2], loc[i][3]), (0, 150, 0), 3)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.rectangle(result_image, (loc[i][0], loc[i][1]), (loc[i][0] + 100, loc[i][1] + 40), (0, 150, 0), -1)
cv2.putText(result_image, "#{:.3f}".format(meter_readings[i]), (loc[i][0],loc[i][1] + 25), font, 0.8, (255, 255, 255), 2, cv2.LINE_AA)
if cv2.imwrite(f"{DATA_DIR}/reading_results.jpg", result_image):
print("The reading results image has been saved as \"reading_results.jpg\" in data")
plt.imshow(result_image)
Meter 1: 1.100
Meter 2: 6.185
The reading results image has been saved as "reading_results.jpg" in data
## Try it with your meter photos!