opencv小白08PracticalExercise学习笔记02

_,im = cv2.threshold(im, 128, 255, cv2.THRESH_BINARY)
moment = cv2.moments(im)
huMoments = cv2.HuMoments(moment)

m2 = cv2.matchShapes(im1,im2,cv2.CONTOURS_MATCH_I2,0)

qrDecoder = cv2.QRCodeDetector()
data,bbox,rectifiedImage = qrDecoder.detectAndDecode(inputImage)

inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight), (0, 0, 0), swapRB=False, crop=False)

net.setInput(inpBlob)

output = net.forward()
print("time taken by network : {:.3f}".format(time.time() - t))

points = []

for i in range(nPoints):
    probMap = output[0, i, :, :]
    probMap = cv2.resize(probMap, (frameWidth, frameHeight))

    minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)

    if prob > threshold :
        cv2.circle(frameCopy, (int(point[0]), int(point[1])), 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)
        points.append((int(point[0]), int(point[1])))
    else :
        points.append(None)

# For each frame, extract the bounding box and mask for each detected object
def postprocess(boxes, masks):
    numClasses = masks.shape[1]
    numDetections = boxes.shape[2]

    frameH = frame.shape[0]
    frameW = frame.shape[1]

    for i in range(numDetections):
        box = boxes[0, 0, i]
        mask = masks[i]
        score = box[2]
        if score > confThreshold:
            classId = int(box[1])

            # Extract the bounding box
            left top right bottom = xxx

            classMask = mask[classId]
            drawBox(frame, classId, score, left, top, right, bottom, classMask)

bbox = cv2.selectROI(frame, False)
ok = tracker.init(frame, bbox)

while True:
    ok, frame = video.read()
    if not ok:
        break

    ok, bbox = tracker.update(frame)

    if ok:
        # Tracking success
        p1 = (int(bbox[0]), int(bbox[1]))
        p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
        cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1)

# Create MultiTracker object
multiTracker = cv2.MultiTracker_create()
for bbox in bboxes:
multiTracker.add(createTrackerByName(trackerType), frame, bbox)

while cap.isOpened():
success, frame = cap.read()
if not success:
  break

# get updated location of objects in subsequent frames
success, boxes = multiTracker.update(frame)

# draw tracked objects
for i, newbox in enumerate(boxes):
  p1 = (int(newbox[0]), int(newbox[1]))
  p2 = (int(newbox[0] + newbox[2]), int(newbox[1] + newbox[3]))
  cv2.rectangle(frame, p1, p2, colors[i], 2, 1)

# Calculate Homography
h, status = cv2.findHomography(pts_src, pts_dst)

# Warp source image to destination based on homography
im_out = cv2.warpPerspective(im_src, h, (im_dst.shape[1],im_dst.shape[0]))