def get_plot(hvf_image_gray, y_ratio, y_size, x_ratio, x_size, plot_type,
icon_type):
plot_image = Image_Utils.slice_image(hvf_image_gray, y_ratio, y_size,
x_ratio, x_size)
hvf_image_gray_process = Image_Utils.preprocess_image(
hvf_image_gray.copy())
plot_image_process = Image_Utils.slice_image(hvf_image_gray_process,
y_ratio, y_size, x_ratio,
x_size)
# Get bounding box from processed image:
top_left, w, h = Hvf_Plot_Array.get_bounding_box(plot_image_process)
bottom_right = (top_left[0] + w, top_left[1] + h)
# Need to specifically handle raw value plot - can have a discontinuity in the
# x axis (with triangle icon), which causes a mis-fit. So need to fill in x-axis
# and try again
cv2.line(plot_image_process, (top_left[0], top_left[1] + int(h / 2)),
(top_left[0] + w, top_left[1] + int(h / 2)), (0),
max(int(h * 0.015), 1))
top_left, w, h = Hvf_Plot_Array.get_bounding_box(plot_image_process)
bottom_right = (top_left[0] + w, top_left[1] + h)
# For debugging: Draw rectangle around the plot - MUST BE COMMENTED OUT, BECAUSE
# IT WILL INTERFERE WITH LATER PLOT EXTRACTIONS
#cv2.rectangle(plot_image, top_left, bottom_right, 0, 2)
# Debug function for showing the plot:
#show_plot_func = (lambda : cv2.imshow("Bound rect for plot " + plot_type, plot_image))
#Logger.get_logger().log_function(Logger.DEBUG_FLAG_DEBUG, show_plot_func);
#cv2.waitKey();
# Slice out the axes plot on the original:
tight_plot = plot_image[top_left[1]:(top_left[1] + h),
top_left[0]:(top_left[0] + w)]
# And extract the values from the array:
plot_array = Hvf_Plot_Array.extract_values_from_plot(
tight_plot, plot_type, icon_type)
# Return the array:
return plot_array, tight_plot
def perform_ocr(img):
# First, preprocessor the image:
img = Image_Utils.preprocess_image(img)
# Next, convert image to python PIL (because pytesseract using PIL):
img_pil = Image.fromarray(img)
if not Ocr_Utils.OCR_API_HANDLE:
Ocr_Utils.OCR_API_HANDLE = PyTessBaseAPI(psm=PSM.SINGLE_COLUMN)
#Ocr_Utils.OCR_API_HANDLE = PyTessBaseAPI(psm=PSM.SINGLE_BLOCK)
Ocr_Utils.OCR_API_HANDLE.SetImage(img_pil)
text = Ocr_Utils.OCR_API_HANDLE.GetUTF8Text()
# Return extracted text:
return text
def is_pattern_not_shown(hvf_image_gray, y_ratio, y_size, x_ratio, x_size):
# Calculate height/width for calculation later:
height = np.size(hvf_image_gray, 0)
width = np.size(hvf_image_gray, 1)
# Slice image:
hvf_image_gray = Image_Utils.preprocess_image(hvf_image_gray)
sliced_img = Image_Utils.slice_image(hvf_image_gray, y_ratio, y_size,
x_ratio, x_size)
# Try to detect a bounding box:
top_left, w, h = Hvf_Plot_Array.get_bounding_box(sliced_img)
# Calculate the relative (percentage) size of the bounding box compared to slice:
box_ratio_w = w / (x_size * width)
box_ratio_h = h / (y_size * height)
# Define a threshold below which if the size ratio is, we declare that the pattern
# is not detected:
threshold_size = 0.3
return (box_ratio_w < threshold_size or box_ratio_h < threshold_size)