test_mycnn.py 文件源码

def generate_data_test(img_path):
    data = []
    img = cv2.resize(cv2.imread(img_path, 0), (640, 480))

    y_start = random.randint(64, 160)
    y_end = y_start + 256
    x_start = random.randint(64, 320)
    x_end = x_start + 256

    y_1 = y_start
    x_1 = x_start
    y_2 = y_end
    x_2 = x_start
    y_3 = y_end
    x_3 = x_end
    y_4 = y_start
    x_4 = x_end

    img_patch = img[y_start:y_end, x_start:x_end]  # patch 1

    y_1_offset = random.randint(-64, 64)
    x_1_offset = random.randint(-64, 64)
    y_2_offset = random.randint(-64, 64)
    x_2_offset = random.randint(-64, 64)
    y_3_offset = random.randint(-64, 64)
    x_3_offset = random.randint(-64, 64)
    y_4_offset = random.randint(-64, 64)
    x_4_offset = random.randint(-64, 64)

    y_1_p = y_1 + y_1_offset
    x_1_p = x_1 + x_1_offset
    y_2_p = y_2 + y_2_offset
    x_2_p = x_2 + x_2_offset
    y_3_p = y_3 + y_3_offset
    x_3_p = x_3 + x_3_offset
    y_4_p = y_4 + y_4_offset
    x_4_p = x_4 + x_4_offset

    pts_img_patch = np.array([[y_1,x_1],[y_2,x_2],[y_3,x_3],[y_4,x_4]]).astype(np.float32)
    pts_img_patch_perturb = np.array([[y_1_p,x_1_p],[y_2_p,x_2_p],[y_3_p,x_3_p],[y_4_p,x_4_p]]).astype(np.float32)
    h, status = cv2.findHomography(pts_img_patch, pts_img_patch_perturb, cv2.RANSAC)

    img_perburb = cv2.warpPerspective(img, h, (640, 480))
    img_perburb_patch = img_perburb[y_start:y_end, x_start:x_end]  # patch 2

    data.append(img_patch)
    data.append(img_perburb_patch)

    h_4pt = np.array([y_1_offset,x_1_offset,y_2_offset,x_2_offset,y_3_offset,x_3_offset,y_4_offset,x_4_offset])
    h1 = np.array([y_1,x_1,y_2,x_2,y_3,x_3,y_4,x_4])

    return data, h_4pt, h1, img, img_perburb