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import cv2
import numpy as np
def class_Order(boxes, categories):
Z = []
# Z = [x for _,x in sorted(zip(categories, boxes))]
cate = np.argsort(categories)
for index in cate:
Z.append(boxes[index])
return Z
def non_max_suppression_fast(boxes, labels, overlapThresh):
# if there are no boxes, return an empty list
if len(boxes) == 0:
return []
# if the bounding boxes integers, convert them to floats --
# this is important since we'll be doing a bunch of divisions
if boxes.dtype.kind == "i":
boxes = boxes.astype("float")
# initialize the list of picked indexes
pick = []
# grab the coordinates of the bounding boxes
x1 = boxes[:, 1]
y1 = boxes[:, 0]
x2 = boxes[:, 3]
y2 = boxes[:, 2]
# compute the area of the bounding boxes and sort the bounding
# boxes by the bottom-right y-coordinate of the bounding box
area = (x2 - x1 + 1) * (y2 - y1 + 1)
idxs = np.argsort(y2)
# keep looping while some indexes still remain in the indexes
# list
while len(idxs) > 0:
# grab the last index in the indexes list and add the
# index value to the list of picked indexes
last = len(idxs) - 1
i = idxs[last]
pick.append(i)
# find the largest (x, y) coordinates for the start of
# the bounding box and the smallest (x, y) coordinates
# for the end of the bounding box
xx1 = np.maximum(x1[i], x1[idxs[:last]])
yy1 = np.maximum(y1[i], y1[idxs[:last]])
xx2 = np.minimum(x2[i], x2[idxs[:last]])
yy2 = np.minimum(y2[i], y2[idxs[:last]])
# compute the width and height of the bounding box
w = np.maximum(0, xx2 - xx1 + 1)
h = np.maximum(0, yy2 - yy1 + 1)
# compute the ratio of overlap
overlap = (w * h) / area[idxs[:last]]
# delete all indexes from the index list that have
idxs = np.delete(
idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0]))
)
# return only the bounding boxes that were picked using the
# integer data type
final_labels = [labels[idx] for idx in pick]
final_boxes = boxes[pick].astype("int")
return final_boxes, final_labels
def get_center_point(box):
left, top, right, bottom = box
return left + ((right - left) // 2), top + (
(bottom - top) // 2
) # (x_c, y_c) # Need to fix bottom_left and bottom_right
def order_points(pts):
rect = np.zeros((4, 2), dtype="float32")
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
return rect
def four_point_transform(image, pts):
image = np.asarray(image)
rect = order_points(pts)
(tl, tr, br, bl) = rect
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
dst = np.array(
[[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]],
dtype="float32",
)
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
return warped
# def getMissingCorner(categories, boxes): # boxes: top_left, top_right, bottom_left, bottom_right
# if 0 not in categories: # Missing top_left
# delta_vertical = boxes[3][2] - boxes[1][2]
# delta_horizon = boxes[3][3] - boxes[2][3]
# x_miss =
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