numpy qr factorization problems

Question

I'm trying to extract rotation matrix from affine transform matrix (via QR decomposition), but it gives me wrong rotations. After applying rotation vectors should have zero angle between them. I have no idea what is going wrong.

Here is the code that is currently giving the unexpected output:

import numpy as np
import math

np.set_printoptions(suppress=True)

def compute_affine(ins, out): # matrices 3x4
    # https://stackoverflow.com/questions/8873462/how-to-perform-coordinates-affine-transformation-using-python
    # finding transformation
    l = len(ins)
    entry = lambda r, d: np.linalg.det(np.delete(np.vstack([r, ins.T, np.ones(l)]), d, axis=0))
    M = np.array([[(-1) ** i * entry(R, i) for R in out.T] for i in range(l + 1)])
    print(M)
    A, t = np.hsplit(M[1:].T / (-M[0])[:, None], [l - 1])
    t = np.transpose(t)[0]
    # output transformation
    print("Affine transformation matrix:\n", A)
    print("Affine transformation translation vector:\n", t)
    # unittests
    print("TESTING:")
    for p, P in zip(np.array(ins), np.array(out)):
        image_p = np.dot(A, p) + t
        result = "[OK]" if np.allclose(image_p, P) else "[ERROR]"
        print(p, " mapped to: ", image_p, " ; expected: ", P, result)
    return A, t

def dot_product_angle(v1,v2):

    if np.linalg.norm(v1) == 0 or np.linalg.norm(v2) == 0:
        print("Zero magnitude vector!")
    else:
        vector_dot_product = np.dot(v1,v2)
        arccos = np.arccos(vector_dot_product / (np.linalg.norm(v1) * np.linalg.norm(v2)))
        angle = np.degrees(arccos)
        return angle
    return 0

if __name__=="__main__":
    import numpy as np

    src = np.array([
        [1, 0, 0],
        [0, 0, 1],
        [1, 0, 1],
        [1, 1, 1],
    ])

    uv = np.array([
        [0.1, 0],
        [0, -0.1],
        [0.1, -0.1],
        [1, 1]
    ])

    angle = 45
    rot_matrix = np.array([[math.cos(angle), -math.sin(angle), 0 ],
                                [math.sin(angle), math.cos(angle), 0],
                                [0, 0, 1]])

    dst = np.dot(src, rot_matrix)
    # compute affine matrices
    src_A, src_t = compute_affine(src, uv)
    dst_A, dst_t = compute_affine(dst, uv)

    src_Q, src_R = np.linalg.qr(np.vstack([src_A, np.cross(src_A[0], src_A[1])]))
    dst_Q, dst_R = np.linalg.qr(np.vstack([dst_A, np.cross(dst_A[0], dst_A[1])]))


    vec1 = np.dot(src_Q[:-1], src[0])
    vec2 = np.dot(dst_Q[:-1], dst[0])

    if not dot_product_angle(vec1, vec2) == 0:
        raise Exception("Angle is invalid should be zero")