from sklearn.metrics import mean_squared_error, mean_absolute_error
from scipy.io import loadmat
import os
import re

root_path = "."


def get_seq_class(seq, set):
    backlight = [
        "DJI_0021",
        "DJI_0022",
        "DJI_0032",
        "DJI_0202",
        "DJI_0339",
        "DJI_0340",
        "DJI_0463",
        "DJI_0003",
    ]

    fly = [
        "DJI_0177",
        "DJI_0174",
        "DJI_0022",
        "DJI_0180",
        "DJI_0181",
        "DJI_0200",
        "DJI_0544",
        "DJI_0012",
        "DJI_0178",
        "DJI_0343",
        "DJI_0185",
        "DJI_0195",
        "DJI_0996",
        "DJI_0977",
        "DJI_0945",
        "DJI_0946",
        "DJI_0091",
        "DJI_0442",
        "DJI_0466",
        "DJI_0459",
        "DJI_0464",
    ]

    angle_90 = [
        "DJI_0179",
        "DJI_0186",
        "DJI_0189",
        "DJI_0191",
        "DJI_0196",
        "DJI_0190",
        "DJI_0070",
        "DJI_0091",
    ]

    mid_size = [
        "DJI_0012",
        "DJI_0013",
        "DJI_0014",
        "DJI_0021",
        "DJI_0022",
        "DJI_0026",
        "DJI_0028",
        "DJI_0028",
        "DJI_0030",
        "DJI_0028",
        "DJI_0030",
        "DJI_0034",
        "DJI_0200",
        "DJI_0544",
        "DJI_0463",
        "DJI_0001",
        "DJI_0149",
    ]

    light = "sunny"
    bird = "stand"
    angle = "60"
    size = "small"
    # resolution = '4k'
    if seq in backlight:
        light = "backlight"
    if seq in fly:
        bird = "fly"
    if seq in angle_90:
        angle = "90"
    if seq in mid_size:
        size = "mid"

    count = "sparse"
    loca = loadmat(
        os.path.join(
            root_path,
            set,
            "ground_truth",
            "GT_img" + str(seq[-3:]) + "000.mat",
        )
    )["locations"]
    if loca.shape[0] > 150:
        count = "crowded"
    return [light, angle, bird, size, count]


with open("./result.txt", "r") as f:
    lines = f.readlines()
print(len(lines))

data = []
for line in lines:
    match = re.match(
        r"\d+: *err: *(\d+), *gt_count: *(\d+), *pred_count: *(\d+), *name: *(.+)",
        # r'\d+: err: (\d+\.\d+), gt_count: (\d+\.\d+), pred_count: (\d+\.\d+), name: (.+)',
        # r'vi: \d+, name: (.+), gt: (\d+\.\d+), pred: (\d+\.\d+), res: (\d+\.\d+)',
        line,
    )
    if match:
        error = float(match.group(1))
        gt_count = float(match.group(2))
        pred_count = float(match.group(3))
        name = match.group(4)
        data.append(
            {
                "error": error,
                "gt_count": gt_count,
                "pred_count": pred_count,
                "name": name,
            }
        )
        print(error, gt_count, pred_count, name)

print(len(data))


preds = []
gts = []
preds_hist = [[] for i in range(10)]
gts_hist = [[] for i in range(10)]
attri = [
    "sunny",
    "backlight",
    "crowded",
    "sparse",
    "60",
    "90",
    "stand",
    "fly",
    "small",
    "mid",
]

for d in data:
    name = d["name"]
    error = d["error"]
    gt_count = d["gt_count"]
    pred_count = d["pred_count"]
    seq = "DJI_" + str(int(name[3:6])).zfill(4)
    cur_attris = get_seq_class(seq, "test")
    preds.append(pred_count)
    gts.append(gt_count)
    for cur_attri in cur_attris:
        preds_hist[attri.index(cur_attri)].append(pred_count)
        gts_hist[attri.index(cur_attri)].append(gt_count)
test_log = "Test: MAE: {:.2f}, MSE: {:.2f}\n".format(
    mean_absolute_error(gts, preds), mean_squared_error(gts, preds)
)
for i in range(10):
    if len(preds_hist[i]) == 0:
        continue
    test_log_attri = "{}: MAE: {:.2f}, MSE: {:.2f}\n".format(
        attri[i],
        mean_absolute_error(gts_hist[i], preds_hist[i]),
        mean_squared_error(gts_hist[i], preds_hist[i]),
    )
    test_log += test_log_attri
print(test_log)