229 lines
7.9 KiB
Python
229 lines
7.9 KiB
Python
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import cv2
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import depthai
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import numpy as np
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_conf_threshold = 0.5
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def get_cv_rotated_rect(bbox, angle):
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x0, y0, x1, y1 = bbox
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width = abs(x0 - x1)
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height = abs(y0 - y1)
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x = x0 + width * 0.5
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y = y0 + height * 0.5
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return ([x.tolist(), y.tolist()], [width.tolist(), height.tolist()], np.rad2deg(angle))
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def rotated_Rectangle(bbox, angle):
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X0, Y0, X1, Y1 = bbox
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width = abs(X0 - X1)
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height = abs(Y0 - Y1)
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x = int(X0 + width * 0.5)
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y = int(Y0 + height * 0.5)
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pt1_1 = (int(x + width / 2), int(y + height / 2))
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pt2_1 = (int(x + width / 2), int(y - height / 2))
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pt3_1 = (int(x - width / 2), int(y - height / 2))
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pt4_1 = (int(x - width / 2), int(y + height / 2))
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t = np.array([[np.cos(angle), -np.sin(angle), x - x * np.cos(angle) + y * np.sin(angle)],
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[np.sin(angle), np.cos(angle), y - x * np.sin(angle) - y * np.cos(angle)],
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[0, 0, 1]])
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tmp_pt1_1 = np.array([[pt1_1[0]], [pt1_1[1]], [1]])
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tmp_pt1_2 = np.dot(t, tmp_pt1_1)
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pt1_2 = (int(tmp_pt1_2[0][0]), int(tmp_pt1_2[1][0]))
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tmp_pt2_1 = np.array([[pt2_1[0]], [pt2_1[1]], [1]])
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tmp_pt2_2 = np.dot(t, tmp_pt2_1)
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pt2_2 = (int(tmp_pt2_2[0][0]), int(tmp_pt2_2[1][0]))
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tmp_pt3_1 = np.array([[pt3_1[0]], [pt3_1[1]], [1]])
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tmp_pt3_2 = np.dot(t, tmp_pt3_1)
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pt3_2 = (int(tmp_pt3_2[0][0]), int(tmp_pt3_2[1][0]))
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tmp_pt4_1 = np.array([[pt4_1[0]], [pt4_1[1]], [1]])
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tmp_pt4_2 = np.dot(t, tmp_pt4_1)
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pt4_2 = (int(tmp_pt4_2[0][0]), int(tmp_pt4_2[1][0]))
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points = np.array([pt1_2, pt2_2, pt3_2, pt4_2])
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return points
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def non_max_suppression(boxes, probs=None, angles=None, overlapThresh=0.3):
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# if there are no boxes, return an empty list
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if len(boxes) == 0:
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return [], []
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# if the bounding boxes are integers, convert them to floats -- this
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# is important since we'll be doing a bunch of divisions
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if boxes.dtype.kind == "i":
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boxes = boxes.astype("float")
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# initialize the list of picked indexes
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pick = []
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# grab the coordinates of the bounding boxes
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = boxes[:, 2]
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y2 = boxes[:, 3]
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# compute the area of the bounding boxes and grab the indexes to sort
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# (in the case that no probabilities are provided, simply sort on the bottom-left y-coordinate)
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area = (x2 - x1 + 1) * (y2 - y1 + 1)
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idxs = y2
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# if probabilities are provided, sort on them instead
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if probs is not None:
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idxs = probs
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# sort the indexes
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idxs = np.argsort(idxs)
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# keep looping while some indexes still remain in the indexes list
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while len(idxs) > 0:
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# grab the last index in the indexes list and add the index value to the list of picked indexes
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last = len(idxs) - 1
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i = idxs[last]
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pick.append(i)
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# 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
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xx1 = np.maximum(x1[i], x1[idxs[:last]])
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yy1 = np.maximum(y1[i], y1[idxs[:last]])
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xx2 = np.minimum(x2[i], x2[idxs[:last]])
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yy2 = np.minimum(y2[i], y2[idxs[:last]])
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# compute the width and height of the bounding box
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w = np.maximum(0, xx2 - xx1 + 1)
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h = np.maximum(0, yy2 - yy1 + 1)
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# compute the ratio of overlap
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overlap = (w * h) / area[idxs[:last]]
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# delete all indexes from the index list that have overlap greater than the provided overlap threshold
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idxs = np.delete(idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0])))
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# return only the bounding boxes that were picked
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return boxes[pick].astype("int"), angles[pick]
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def decode_predictions(scores, geometry1, geometry2):
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# grab the number of rows and columns from the scores volume, then
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# initialize our set of bounding box rectangles and corresponding
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# confidence scores
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(numRows, numCols) = scores.shape[2:4]
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rects = []
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confidences = []
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angles = []
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# loop over the number of rows
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for y in range(0, numRows):
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# extract the scores (probabilities), followed by the
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# geometrical data used to derive potential bounding box
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# coordinates that surround text
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scoresData = scores[0, 0, y]
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xData0 = geometry1[0, 0, y]
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xData1 = geometry1[0, 1, y]
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xData2 = geometry1[0, 2, y]
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xData3 = geometry1[0, 3, y]
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anglesData = geometry2[0, 0, y]
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# loop over the number of columns
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for x in range(0, numCols):
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# if our score does not have sufficient probability,
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# ignore it
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if scoresData[x] < _conf_threshold:
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continue
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# compute the offset factor as our resulting feature
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# maps will be 4x smaller than the input image
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(offsetX, offsetY) = (x * 4.0, y * 4.0)
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# extract the rotation angle for the prediction and
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# then compute the sin and cosine
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angle = anglesData[x]
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cos = np.cos(angle)
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sin = np.sin(angle)
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# use the geometry volume to derive the width and height
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# of the bounding box
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h = xData0[x] + xData2[x]
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w = xData1[x] + xData3[x]
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# compute both the starting and ending (x, y)-coordinates
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# for the text prediction bounding box
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endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
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endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
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startX = int(endX - w)
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startY = int(endY - h)
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# add the bounding box coordinates and probability score
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# to our respective lists
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rects.append((startX, startY, endX, endY))
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confidences.append(scoresData[x])
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angles.append(angle)
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# return a tuple of the bounding boxes and associated confidences
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return (rects, confidences, angles)
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def decode_east(nnet_packet, **kwargs):
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scores = nnet_packet.get_tensor(0)
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geometry1 = nnet_packet.get_tensor(1)
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geometry2 = nnet_packet.get_tensor(2)
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bboxes, confs, angles = decode_predictions(scores, geometry1, geometry2
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)
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boxes, angles = non_max_suppression(np.array(bboxes), probs=confs, angles=np.array(angles))
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boxesangles = (boxes, angles)
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return boxesangles
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def show_east(boxesangles, frame, **kwargs):
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bboxes = boxesangles[0]
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angles = boxesangles[1]
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for ((X0, Y0, X1, Y1), angle) in zip(bboxes, angles):
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width = abs(X0 - X1)
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height = abs(Y0 - Y1)
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cX = int(X0 + width * 0.5)
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cY = int(Y0 + height * 0.5)
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rotRect = ((cX, cY), ((X1 - X0), (Y1 - Y0)), angle * (-1))
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points = rotated_Rectangle(frame, rotRect, color=(255, 0, 0), thickness=1)
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cv2.polylines(frame, [points], isClosed=True, color=(255, 0, 0), thickness=1, lineType=cv2.LINE_8)
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return frame
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def order_points(pts):
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rect = np.zeros((4, 2), dtype="float32")
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s = pts.sum(axis=1)
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rect[0] = pts[np.argmin(s)]
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rect[2] = pts[np.argmax(s)]
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diff = np.diff(pts, axis=1)
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rect[1] = pts[np.argmin(diff)]
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rect[3] = pts[np.argmax(diff)]
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return rect
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def four_point_transform(image, pts):
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rect = order_points(pts)
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(tl, tr, br, bl) = rect
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widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
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widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
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maxWidth = max(int(widthA), int(widthB))
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heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
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heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
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maxHeight = max(int(heightA), int(heightB))
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dst = np.array([
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[0, 0],
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[maxWidth - 1, 0],
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[maxWidth - 1, maxHeight - 1],
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[0, maxHeight - 1]], dtype="float32")
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M = cv2.getPerspectiveTransform(rect, dst)
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warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
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return warped
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