Initial

utils.py

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+import numpy as np
+import os
+import cv2 as cv
+from PIL import Image
+
+
+def mse(imageA, imageB):
+    # the 'Mean Squared Error' between the two images is the
+    # sum of the squared difference between the two images;
+    # NOTE: the two images must have the same dimension
+    err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)
+    err /= float(imageA.shape[0] * imageA.shape[1])
+
+    # return the MSE, the lower the error, the more "similar"
+    # the two images are
+    return err
+
+
+def mse_with_reshape(imageA, imageB):
+    # the 'Mean Squared Error' between the two images is the
+    # sum of the squared difference between the two images;
+    # NOTE: the two images must have the same dimension
+
+    if len(imageA) == 0 or len(imageB) == 0:
+        return 999
+    try:
+        imga = np.reshape(imageA, (len(imageA) * 17, 740, 3), 'C')
+        imgb = np.reshape(imageB, (len(imageB) * 17, 740, 3), 'C')
+
+        err = np.sum((imga.astype("float") - imgb.astype("float")) ** 2)
+        err /= float(imga.shape[0] * imga.shape[1])
+
+        # return the MSE, the lower the error, the more "similar"
+        # the two images are
+        return err
+    except:
+        return 999
+
+
+def load_bl_line():
+    list_store = []
+    for path in os.listdir('bl-lines'):
+        if path.endswith(".npy"):
+            list_store.append(np.load("bl-lines\\" + path))
+    return list_store
+
+
+def check_for_bl_line(line_item, black_list):
+    for bl_item in black_list:
+        if mse(line_item, bl_item) < 500:
+            return True
+    return False
+
+
+def scale_screenshot(screenshot):
+    scale_percent = 200  # percent of original size
+    width = int(screenshot.shape[1] * scale_percent / 100)
+    height = int(screenshot.shape[0] * scale_percent / 100)
+    dim = (width, height)
+    resized_img = cv.resize(screenshot, dim, interpolation=4)
+
+    gray = cv.cvtColor(resized_img, cv.COLOR_BGR2GRAY)
+    thresh = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV + cv.THRESH_OTSU)[1]
+    # cv.imshow("Tresh", thresh)
+    # cv.waitKey(1000)
+    return thresh
+
+
+def save_line_item_npy_jpg(short_pic_list):
+    ifr = 1
+    for x in short_pic_list:
+        np.save("test{}".format(ifr), x)
+        Image.fromarray(x).save("test{}.jpg".format(ifr))
+        ifr = ifr + 1
+
+
+def shorten_picture_input(new_pic_lst, old_pic_lst):
+    len_old = len(old_pic_lst)
+    len_new = len(new_pic_lst)
+    offset = 0
+    offset_diff = len_new - len_old
+    retval = []
+
+    while mse_with_reshape(old_pic_lst[offset: len_old], new_pic_lst[0: len_new - offset - offset_diff]) > 500.0:
+        offset = offset + 1
+        # print(offset)
+        if offset == len(old_pic_lst):
+            break
+
+    if offset > 0:
+        retval = new_pic_lst[len_new - offset: len_new]
+    return retval
+
+
+def shorten_input(new_lst, old_lst):
+    len_old = len(old_lst)
+    len_new = len(new_lst)
+    offset = 0
+    offset_diff = len_new - len_old
+    retval = []
+
+    while old_lst[offset: len_old] != new_lst[0: len_new - offset - offset_diff]:
+        offset = offset + 1
+        # print(offset)
+
+    if offset > 0:
+        retval = new_lst[len_new - offset: len_new]
+
+    return retval
+
+
+def format_output(data):
+    new_list = data.split("\n")
+
+    try:
+        while True:
+            # new_list.remove() ("",)
+            new_list.remove('\x0c')
+    except ValueError:
+        pass
+    return new_list
+
+
+def update_screenshot_with_short_pic_list(short_pic_lst, lst_bl_items):
+    screenshot = []
+    # check if shorten line pictures list has any entries
+    if len(short_pic_lst) > 0:
+        index_lst = []
+        # check if any of the line item pictures is on the blacklist and save index
+        for x in range(len(short_pic_lst)):
+            if check_for_bl_line(short_pic_lst[x], lst_bl_items):
+                index_lst.append(x)
+
+        # remove blacklisted items from shorten list
+        r_short_pic_lst = np.delete(short_pic_lst, index_lst, axis=0)
+        # check if further shortened line pictures list has any entries
+        if len(r_short_pic_lst) == 0:
+            return screenshot
+
+        # combine shorten line picture list to one picture
+        screenshot = np.reshape(r_short_pic_lst, (len(r_short_pic_lst) * 17, 740, 3), 'C')
+
+        # saves all line item pictures to disk for blacklist evaluation
+        # save_line_item_npy_jpg(r_short_pic_lst)
+
+    return screenshot