Python cv2.perspectiveTransform方法代碼示例

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def render(img, obj, projection, model, color=False):
    """
    Render a loaded obj model into the current video frame
    """
    vertices = obj.vertices
    scale_matrix = np.eye(3) * 3
    h, w = model.shape

    for face in obj.faces:
        face_vertices = face[0]
        points = np.array([vertices[vertex - 1] for vertex in face_vertices])
        points = np.dot(points, scale_matrix)
        # render model in the middle of the reference surface. To do so,
        # model points must be displaced
        points = np.array([[p[0] + w / 2, p[1] + h / 2, p[2]] for p in points])
        dst = cv2.perspectiveTransform(points.reshape(-1, 1, 3), projection)
        imgpts = np.int32(dst)
        if color is False:
            cv2.fillConvexPoly(img, imgpts, (137, 27, 211))
        else:
            color = hex_to_rgb(face[-1])
            color = color[::-1]  # reverse
            cv2.fillConvexPoly(img, imgpts, color)

    return img 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def filter_matrix_corners_homography(pts, max, matrix) -> (float, List):
        '''
        Compute the images of the image corners and of its center (i.e. the points you get when you apply the homography to those corners and center),
        and verify that they make sense, i.e. are they inside the image canvas (if you expect them to be)? Are they well separated from each other?
        Return a distance and a list of the transformed points
        '''

        # Transform the 4 corners thanks to the transformation matrix calculated
        transformed_pts = cv2.perspectiveTransform(pts, matrix)

        # Compute the difference between original and modified position of points
        dist = round(cv2.norm(pts - transformed_pts, cv2.NORM_L2) / max, 10)  # sqrt((X1-X2)²+(Y1-Y2)²+...)

        # Totally an heuristic (geometry based):
        if dist < 0.20:
            return dist, transformed_pts
        else:
            return 1, transformed_pts 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def filter_matrix_corners_affine(pts, max, matrix) -> (float, List):
        '''
        Compute the images of the image corners and of its center (i.e. the points you get when you apply the homography to those corners and center),
        and verify that they make sense, i.e. are they inside the image canvas (if you expect them to be)? Are they well separated from each other?
        Return a distance and a list of the transformed points
        '''

        # Make affine transformation
        add_row = np.array([[0, 0, 1]])
        affine_matrix = np.concatenate((matrix, add_row), axis=0)
        transformed_pts_affine = cv2.perspectiveTransform(pts, affine_matrix)

        # Affine distance
        tmp_dist_affine = round(cv2.norm(pts - transformed_pts_affine, cv2.NORM_L2) / max, 10)  # sqrt((X1-X2)²+(Y1-Y2)²+...)

        # Totally an heuristic (geometry based):
        if tmp_dist_affine < 0.20:
            return tmp_dist_affine, transformed_pts_affine
        else:
            return 1, transformed_pts_affine 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def _augment_keypoints(self, keypoints_on_images, random_state, parents, hooks):
        result = keypoints_on_images
        matrices, max_heights, max_widths = self._create_matrices(
            [kps.shape for kps in keypoints_on_images],
            random_state
        )

        for i, (M, max_height, max_width) in enumerate(zip(matrices, max_heights, max_widths)):
            keypoints_on_image = keypoints_on_images[i]
            kps_arr = keypoints_on_image.get_coords_array()
            #nb_channels = keypoints_on_image.shape[2] if len(keypoints_on_image.shape) >= 3 else None

            warped = cv2.perspectiveTransform(np.array([kps_arr], dtype=np.float32), M)
            warped = warped[0]
            warped_kps = ia.KeypointsOnImage.from_coords_array(
                warped,
                shape=(max_height, max_width) + keypoints_on_image.shape[2:]
            )
            if self.keep_size:
                warped_kps = warped_kps.on(keypoints_on_image.shape)
            result[i] = warped_kps

        return result 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def getTransformedLocation(x_coord,y_coord, calData):
    try:
            # transform only the hit point with the saved transformation matrix
            # ToDo: idea for second camera -> transform complete image and overlap both images to find dart location?
            dart_loc_temp = np.array([[x_coord, y_coord]], dtype="float32")
            dart_loc_temp = np.array([dart_loc_temp])
            dart_loc = cv2.perspectiveTransform(dart_loc_temp, calData.transformation_matrix)
            new_dart_loc = tuple(dart_loc.reshape(1, -1)[0])

            return new_dart_loc

    #system not calibrated
    except AttributeError as err1:
        print err1
        return (-1, -1)

    except NameError as err2:
        #not calibrated error
        print err2
        return (-2, -2)


#Returns dartThrow (score, multiplier, angle, magnitude) based on x,y location 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def persTransform(pts, H):
    """Transforms a list of points, `pts`,
    using the perspective transform `H`."""
    src = np.zeros((len(pts), 1, 2))
    src[:, 0] = pts
    dst = cv2.perspectiveTransform(src, H)
    return np.array(dst[:, 0, :], dtype='float32') 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def track(self, frame):
        '''Returns a list of detected TrackedTarget objects'''
        self.frame_points, frame_descrs = self.detect_features(frame)
        if len(self.frame_points) < MIN_MATCH_COUNT:
            return []
        matches = self.matcher.knnMatch(frame_descrs, k = 2)
        matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * 0.75]
        if len(matches) < MIN_MATCH_COUNT:
            return []
        matches_by_id = [[] for _ in xrange(len(self.targets))]
        for m in matches:
            matches_by_id[m.imgIdx].append(m)
        tracked = []
        for imgIdx, matches in enumerate(matches_by_id):
            if len(matches) < MIN_MATCH_COUNT:
                continue
            target = self.targets[imgIdx]
            p0 = [target.keypoints[m.trainIdx].pt for m in matches]
            p1 = [self.frame_points[m.queryIdx].pt for m in matches]
            p0, p1 = np.float32((p0, p1))
            H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 3.0)
            status = status.ravel() != 0
            if status.sum() < MIN_MATCH_COUNT:
                continue
            p0, p1 = p0[status], p1[status]

            x0, y0, x1, y1 = target.rect
            quad = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
            quad = cv2.perspectiveTransform(quad.reshape(1, -1, 2), H).reshape(-1, 2)

            track = TrackedTarget(target=target, p0=p0, p1=p1, H=H, quad=quad)
            tracked.append(track)
        tracked.sort(key = lambda t: len(t.p0), reverse=True)
        return tracked 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def visualize_homo(img1, img2, kp1, kp2, matches, homo, mask):
    h, w, d = img1.shape
    pts = [[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]
    pts = np.array(pts, dtype=np.float32).reshape((-1, 1, 2))
    dst = cv.perspectiveTransform(pts, homo)

    img2 = cv.polylines(img2, [np.int32(dst)], True, [255, 0, 0], 3, 8)

    matches_mask = mask.ravel().tolist()
    draw_params = dict(matchesMask=matches_mask,
                       singlePointColor=None,
                       matchColor=(0, 255, 0),
                       flags=2)
    res = cv.drawMatches(img1, kp1, img2, kp2, matches, None, **draw_params)
    return res 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def track_target(self, frame): 
        self.cur_keypoints, self.cur_descriptors = self.detect_features(frame) 

        if len(self.cur_keypoints) < self.min_matches: return []
        try: matches = self.feature_matcher.knnMatch(self.cur_descriptors, k=2)
        except Exception as e:
            print('Invalid target, please select another with features to extract')
            return []
        matches = [match[0] for match in matches if len(match) == 2 and match[0].distance < match[1].distance * 0.75] 
        if len(matches) < self.min_matches: return [] 
 
        matches_using_index = [[] for _ in range(len(self.tracking_targets))] 
        for match in matches: 
            matches_using_index[match.imgIdx].append(match) 
 
        tracked = [] 
        for image_index, matches in enumerate(matches_using_index): 
            if len(matches) < self.min_matches: continue 
 
            target = self.tracking_targets[image_index] 
            points_prev = [target.keypoints[m.trainIdx].pt for m in matches]
            points_cur = [self.cur_keypoints[m.queryIdx].pt for m in matches]
            points_prev, points_cur = np.float32((points_prev, points_cur))
            H, status = cv2.findHomography(points_prev, points_cur, cv2.RANSAC, 3.0) 
            status = status.ravel() != 0

            if status.sum() < self.min_matches: continue 
 
            points_prev, points_cur = points_prev[status], points_cur[status] 
 
            x_start, y_start, x_end, y_end = target.rect 
            quad = np.float32([[x_start, y_start], [x_end, y_start], [x_end, y_end], [x_start, y_end]])
            quad = cv2.perspectiveTransform(quad.reshape(1, -1, 2), H).reshape(-1, 2)
            track = self.tracked_target(target=target, points_prev=points_prev, points_cur=points_cur, H=H, quad=quad) 
            tracked.append(track) 
 
        tracked.sort(key = lambda x: len(x.points_prev), reverse=True) 
        return tracked 
 
    # Detect features in the selected ROIs and return the keypoints and descriptors 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def dealcurve(curve):
	cmean = curve.mean(0)
	angle = (random.random()*10)-5
	scale = ((random.random()-0.5)*0.1)+1.0
	m = cv2.getRotationMatrix2D((0,0),angle,scale)
	m = np.vstack([m,[0,0,1]])
	dmean = (np.random.rand(1,2)-0.5)*10
	curve = curve - cmean
	curve = cv2.perspectiveTransform(np.array([curve]),m)
	curve += cmean
	curve += dmean
	return curve[0] 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def _homography(src_pts,dst_pts,template_width,template_height,match_point=None):
    row,col,dim = dst_pts.shape
    if match_point:
        for i in range(row):
            match_point.append([int(dst_pts[i][0][0]),int(dst_pts[i][0][1])])
    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    pts = np.float32([[0, 0], [0, template_height - 1], 
                    [template_width - 1, template_height - 1], 
                    [template_width - 1, 0]]).reshape(-1, 1, 2)
    #找到一個變換矩陣，從查詢圖映射到檢測圖片
    dst = cv2.perspectiveTransform(pts, M) 
    return dst 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def find(search_file, image_file, threshold=None):
    '''
    param threshold are disabled in sift match.
    '''
    sch = _cv2open(search_file, 0)
    img = _cv2open(image_file, 0)

    kp_sch, des_sch = sift.detectAndCompute(sch, None)
    kp_img, des_img = sift.detectAndCompute(img, None)

    if len(kp_sch) < MIN_MATCH_COUNT or len(kp_img) < MIN_MATCH_COUNT:
        return None

    FLANN_INDEX_KDTREE = 0
    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
    search_params = dict(checks = 50)

    flann = cv2.FlannBasedMatcher(index_params, search_params)

    matches = flann.knnMatch(des_sch, des_img, k=2)

    good = []
    for m,n in matches:
        if m.distance < 0.7*n.distance:
            good.append(m)

    if len(good) > MIN_MATCH_COUNT:
        sch_pts = np.float32([kp_sch[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
        img_pts = np.float32([kp_img[m.trainIdx].pt for m in good]).reshape(-1, 1, 2) 

        M, mask = cv2.findHomography(sch_pts, img_pts, cv2.RANSAC, 5.0)
        # matchesMask = mask.ravel().tolist()

        h, w = sch.shape
        pts = np.float32([ [0, 0], [0, h-1], [w-1, h-1], [w-1, 0] ]).reshape(-1, 1, 2)
        dst = cv2.perspectiveTransform(pts, M)
        lt, br = dst[0][0], dst[2][0]
        return map(int, (lt[0]+w/2, lt[1]+h/2))
    else:
        return None 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def _homography(src_pts,dst_pts,template_width,template_height,match_point=None):
    row,col,dim = dst_pts.shape
    if match_point:
        for i in range(row):
            match_point.append([int(dst_pts[i][0][0]),int(dst_pts[i][0][1])])
    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    pts = np.float32([[0, 0], [0, template_height - 1], 
                    [template_width - 1, template_height - 1], 
                    [template_width - 1, 0]]).reshape(-1, 1, 2)
    #找到一個變換矩陣，從查詢圖映射到檢測圖片
    dst = cv2.perspectiveTransform(pts, M) 
    return dst

#SIFT + Homography 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def ld2bbSample(sample, h):
    sample = np.float32([sample]).reshape(-1, 1, 2)
    con = cv2.perspectiveTransform(sample, h)
    return np.array(list(con[0][0])) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import perspectiveTransform [as 別名]
def transform_pnts(self, pnts, M33):
        """
        :param pnts: 2D pnts, left-top, right-top, right-bottom, left-bottom
        :param M33: output from transform_image()
        :return: 2D pnts apply perspective transform
        """
        pnts = np.asarray(pnts, dtype=np.float32)
        pnts = np.array([pnts])
        dst_pnts = cv2.perspectiveTransform(pnts, M33)[0]

        return dst_pnts