def remove_tiny_areas_and_recalc_score():
RESULT_ROOT_DIR = "tmp/detect_building_damage/master-v2/fixed_histogram"
RESULT_GROUND_TRUTH = "img/resource/ground_truth"
C_RED = [0, 0, 255]
C_ORANGE = [0, 127, 255]
target_dirs = [
dir_path for (dir_path, dir_names, file_names) in walk(RESULT_ROOT_DIR)
if dir_path.endswith("meanshift_and_color_thresholding")
]
for target_dir in target_dirs:
eprint(f"Target: {target_dir}")
gt_type, experiment_num = re.match(r".*/GT_(.*)/aerial_roi([0-9]).*",
target_dir).groups()
eprint(
dedent(f"""
Experiment Num: {experiment_num}
GT_TYPE: GT_{gt_type}
"""))
result = imread_with_error(
path.join(target_dir, "building_damage.tiff"),
cv2.IMREAD_UNCHANGED)
# Fixing Image
result_fixed = BuildingDamageExtractor._remove_tiny_area(
(result * 255.0).astype(np.uint8))
imwrite_with_error(path.join(target_dir, "building_damage_fixed.tiff"),
result_fixed.astype(np.float32))
# Re-calc Score
ground_truth = imread_with_error(
path.join(RESULT_GROUND_TRUTH, f"aerial_roi{experiment_num}.png"))
if gt_type == "BOTH":
ground_truth = np.all(
(ground_truth == C_RED) | (ground_truth == C_ORANGE), axis=2)
elif gt_type == "RED":
ground_truth = np.all(ground_truth == C_RED, axis=2)
elif gt_type == "ORANGE":
ground_truth = np.all(ground_truth == C_ORANGE, axis=2)
cm, scores = evaluation_by_confusion_matrix(result_fixed, ground_truth)
j = {"Confusion Matrix": cm, "Score": scores}
json.dump(j,
open(path.join(target_dir, "scores_fixed_result.json"), "w"),
ensure_ascii=False,
sort_keys=True,
indent="\t")
def gen_source_overlay():
ROOT_DIR = "/Users/popunbom/Google Drive/情報学部/研究/修士/最終発表/Thesis/img/resource"
SRC_DIR = join(ROOT_DIR, "aerial_image")
GT_DIR = join(ROOT_DIR, "ground_truth")
ROAD_MASK_DIR = join(ROOT_DIR, "road_mask")
for exp_num in [1, 2, 3, 5, 9]:
eprint("Experiment Num:", exp_num)
bg, fg, road_mask = [
imread_with_error(
join(root_dir, f"aerial_roi{exp_num}.png")
)
for root_dir in [
SRC_DIR,
GT_DIR,
ROAD_MASK_DIR
]
]
road_mask[:, :, [0, 2]] = [0, 0]
gt_overlay = hsv_blending(bg, fg)
imwrite_with_error(
join(
GT_DIR,
f"aerial_roi{exp_num}_overlay.png"
),
gt_overlay
)
road_mask_overlay = hsv_blending(bg, road_mask)
imwrite_with_error(
join(
ROAD_MASK_DIR,
f"aerial_roi{exp_num}_overlay.png"
),
road_mask_overlay
)
def gen_result_road_damage():
ROOT_DIR_RESULT = "/Users/popunbom/.tmp/EQDmgAnalyzr/detect_road_damage_v2"
dirs = [
join(ROOT_DIR_RESULT, d)
for d in listdir(ROOT_DIR_RESULT)
if isdir(join(ROOT_DIR_RESULT, d))
]
for d in dirs:
print(d)
exp_num, = re.match(
r".*aerial_roi([0-9]).*",
d
).groups()
src = imread_with_error(
join(
ROOT_DIR_SRC,
f"aerial_roi{exp_num}.png"
)
)
src_gs = cv2.cvtColor(
cv2.cvtColor(
src,
cv2.COLOR_BGR2GRAY
),
cv2.COLOR_GRAY2BGR
)
road_mask = imread_with_error(
join(
ROOT_DIR_ROAD_MASK,
f"aerial_roi{exp_num}.png"
),
cv2.IMREAD_GRAYSCALE
).astype(bool)
src[road_mask == False] = [0, 0, 0]
result_paths = [d]
if exists(join(d, "removed_vegetation")):
result_paths.append(
join(d, "removed_vegetation")
)
for result_path in result_paths:
result = imread_with_error(
join(
result_path,
"result_extracted.tiff"
),
cv2.IMREAD_UNCHANGED
)
result = (cm.get_cmap("jet")(
result / result.max()
) * 255).astype(np.uint8)[:, :, [2, 1, 0]]
dst = hsv_blending(src, result)
dst[road_mask == False] = src_gs[road_mask == False]
imwrite_with_error(
join(
result_path,
f"study_overlay.png"
),
dst
)
def eval_by_utilize_methods():
ROOT_DIR_RESULT = "/Users/popunbom/Google Drive/情報学部/研究/修士/最終発表/Thesis/img/result"
SPLIT_PATTERNS = {
"MEAN_SHIFT" : (0,),
"ANGLE_VARIANCE" : (1,),
"PIXEL_CLASSIFY" : (2,),
"MEAN_SHIFT_AND_ANGLE_VARIANCE" : (0, 1),
"MEAN_SHIFT_AND_PIXEL_CLASSIFY" : (0, 2),
"ANGLE_VARIANCE_AND_PIXEL_CLASSIFY" : (1, 2),
"MEAN_SHIFT_AND_ANGLE_VARIANCE_AND_PIXEL_CLASSIFY": (0, 1, 2)
}
result_paths = sorted([
join(dir_path, "result.png")
for (dir_path, dir_names, file_names) in walk(ROOT_DIR_RESULT)
if "result.png" in file_names
])
for result_path in result_paths:
exp_num, = re.match(
r".*aerial_roi([0-9]).*",
result_path
).groups()
result_src = imread_with_error(
result_path
)
gt_src = imread_with_error(
join(
ROOT_DIR_GT,
f"aerial_roi{exp_num}.png"
)
)
result_dir = join(
dirname(result_path),
"evaluation"
)
if not exists(result_dir):
makedirs(result_dir)
NDARRAY_ASSERT(result_src, ndim=3)
print("File:", result_path)
print(
np.unique(
result_src.reshape(result_src.shape[0] * result_src.shape[1], result_src.shape[2]),
axis=0
)
)
# Use Vegetation Mask
OPTIONS = ["NORMAL"]
path_vegetation_mask = join(
ROOT_DIR_VEG_MASK,
f"aerial_roi{exp_num}.png"
)
vegetation_mask = None
if exists(path_vegetation_mask):
vegetation_mask = imread_with_error(
path_vegetation_mask,
cv2.IMREAD_GRAYSCALE
).astype(bool)
OPTIONS.append("REMOVED_VEGETATION")
scores = dict()
for option in OPTIONS:
for name, channels in SPLIT_PATTERNS.items():
eprint("Evaluation:", name)
result = np.zeros(result_src.shape[:2], dtype=np.int16)
for channel in channels:
result += (result_src[:, :, channel] != 0)
result = result.astype(bool)
if option == "NORMAL":
save_dir = result_dir
elif option == "REMOVED_VEGETATION":
result = result & ~vegetation_mask
save_dir = join(
result_dir,
"removed_vegetation"
)
if not exists(save_dir):
makedirs(save_dir)
imwrite_with_error(
join(save_dir, name.replace(" & ", "_and_") + ".png"),
(result * 255).astype(np.uint8)
)
scores[name] = dict()
for gt_type in ["GT_BOTH", "GT_ORANGE", "GT_RED"]:
ground_truth = None
if gt_type == "GT_BOTH":
ground_truth = np.all(
(gt_src == C_RED) | (gt_src == C_ORANGE),
axis=2
)
elif gt_type == "GT_RED":
ground_truth = np.all(
gt_src == C_RED,
axis=2
)
elif gt_type == "GT_ORANGE":
ground_truth = np.all(
gt_src == C_ORANGE,
axis=2
)
cm, metrics = evaluation_by_confusion_matrix(
result,
ground_truth
)
scores[name][gt_type] = {
"Confusion Matrix": cm,
"Score" : metrics
}
json.dump(
scores,
open(join(save_dir, "scores.json"), "w"),
ensure_ascii=False,
sort_keys=True,
indent="\t"
)
def apply_vegetation_mask():
ROOT_DIR_RESULT = "/Users/popunbom/Google Drive/情報学部/研究/修士/最終発表/Thesis/img"
ROOT_DIR_VEG = "img/resource/vegetation_mask"
ROOT_DIR_SRC = join(ROOT_DIR_RESULT, "resource/aerial_image")
for exp_num in [1, 2, 3, 5, 9]:
file_name = f"aerial_roi{exp_num}.png"
result_dir = join(ROOT_DIR_RESULT, f"result/aerial_roi{exp_num}")
save_dir = join(result_dir, "extract_vegetation")
if not exists(save_dir):
makedirs(save_dir)
src = imread_with_error(
join(ROOT_DIR_SRC, file_name)
)
veg_mask = imread_with_error(
join(ROOT_DIR_VEG, file_name),
)
result = imread_with_error(
join(result_dir, "result.png")
)
imwrite_with_error(
join(
save_dir,
"thresholded.png"
),
veg_mask
)
# WHITE -> GREEN
veg_mask[:, :, [0, 2]] = [0, 0]
veg_overlay = hsv_blending(
bg_img=src,
fg_img=veg_mask,
bg_v_scale=0.6,
fg_v_scale=1.5,
)
imwrite_with_error(
join(
save_dir,
"vegetation_overlay.png"
),
veg_overlay
)
# Result - Vegetation
veg_mask_bin = veg_mask[:, :, 1].astype(bool)
result[veg_mask_bin == True] = [0, 0, 0]
imwrite_with_error(
join(
result_dir,
"result_removed_vegetation.png"
),
result
)
# Overlay result
result_overlay = hsv_blending(
bg_img=src,
fg_img=result,
bg_v_scale=0.6
)
imwrite_with_error(
join(
result_dir,
"result_overlay_removed_vegetation.png"
),
result_overlay
)
def gen_result_overlay():
ROOT_DIR = "/Users/popunbom/Google Drive/情報学部/研究/修士/最終発表/Thesis/img"
SRC_DIR = join(ROOT_DIR, "resource/aerial_image")
for exp_num in [1, 2, 3, 5, 9]:
eprint("Experiment Num:", exp_num)
bg = imread_with_error(
join(
SRC_DIR,
f"aerial_roi{exp_num}.png"
)
)
building_damage = imread_with_error(
join(
ROOT_DIR,
f"result/aerial_roi{exp_num}/result.png"
)
)
building_damage_overlay = hsv_blending(bg, building_damage, bg_v_scale=0.6)
imwrite_with_error(
join(
ROOT_DIR,
f"result/aerial_roi{exp_num}/result_overlay.png"
),
building_damage_overlay
)
road_damage = imread_with_error(
join(
ROOT_DIR,
f"result/aerial_roi{exp_num}/road_damage/thresholded.png"
)
)
# White -> Red
road_damage[:, :, [0, 1]] = [0, 0]
road_damage_overlay = hsv_blending(bg, road_damage)
imwrite_with_error(
join(
ROOT_DIR,
f"result/aerial_roi{exp_num}/road_damage/result_overlay.png"
),
road_damage_overlay
)
road_damage_wo_veg = imread_with_error(
join(
ROOT_DIR,
f"result/aerial_roi{exp_num}/road_damage/removed_vegetation/thresholded.png"
)
)
# White -> Red
road_damage_wo_veg[:, :, [0, 1]] = [0, 0]
road_damage_wo_veg_overlay = hsv_blending(bg, road_damage_wo_veg)
imwrite_with_error(
join(
ROOT_DIR,
f"result/aerial_roi{exp_num}/road_damage/removed_vegetation/result_overlay.png"
),
road_damage_wo_veg_overlay
)