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like_unlike_dist.py
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like_unlike_dist.py
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import json
import h5py
import numpy as np
from matplotlib import pyplot as plt
from tqdm import tqdm
from modules.DataModule import center_crop
from utils.utils import calc_np_fhd, calc_np_pc, calc_fhd
dataset_number = 2
pred_type = '8k'
n_responses = 100
def plot_data():
with h5py.File("data/Kyungduk_grainS_no_bg/data.h5", 'r') as data:
rs = np.array(data.get("responses"))
np.random.shuffle(rs)
rs = rs[:n_responses]
unlike_fhds = []
unlike_pcs = []
for idx, r1 in tqdm(enumerate(rs)):
# prepare array of repeated r1s to compare with all following responses
r2s = rs[idx + 1:]
r1s = np.repeat(r1[None, :, :], r2s.shape[0], axis=0)
fhds = calc_np_fhd(r1s, r2s, do_gabor=True)
pcs = calc_np_pc(r1s, r2s)
unlike_fhds.extend(fhds)
unlike_pcs.extend(pcs)
print(np.mean(unlike_fhds), len(unlike_fhds))
print(np.mean(unlike_pcs), len(unlike_pcs))
print("Starting to plot...")
plt.hist(unlike_fhds, bins=100, label="Unlike FHD", density=True)
plt.title("FHD Distribution (grainS)")
plt.legend()
plt.savefig("grainS_unlike_fhd_dist.png")
plt.close()
print("Starting to plot...")
plt.hist(unlike_pcs, bins=100, label="Unlike PC", density=True)
plt.title("PC Distribution (grainS)")
plt.legend()
plt.savefig("grainS_unlike_pc_dist.png")
plt.close()
with h5py.File("data/Kyungduk_grainL_no_bg/data.h5", 'r') as data:
rs = np.array(data.get("responses"))
np.random.shuffle(rs)
rs = rs[:n_responses]
unlike_fhds = []
unlike_pcs = []
for idx, r1 in tqdm(enumerate(rs)):
# prepare array of repeated r1s to compare with all following responses
r2s = rs[idx + 1:]
r1s = np.repeat(r1[None, :, :], r2s.shape[0], axis=0)
fhds = calc_np_fhd(r1s, r2s, do_gabor=True)
pcs = calc_np_pc(r1s, r2s)
unlike_fhds.extend(fhds)
unlike_pcs.extend(pcs)
print(np.mean(unlike_fhds), len(unlike_fhds))
print(np.mean(unlike_pcs), len(unlike_pcs))
print("Starting to plot...")
plt.hist(unlike_fhds, bins=100, label="Unlike FHD", density=True)
plt.title("FHD Distribution (grainL)")
plt.legend()
plt.savefig("grainL_unlike_fhd_dist.png")
plt.close()
print("Starting to plot...")
plt.hist(unlike_pcs, bins=100, label="Unlike PC", density=True)
plt.title("PC Distribution (grainL)")
plt.legend()
plt.savefig("grainL_unlike_pc_dist.png")
plt.close()
exit()
# with open(f"distribution_data/pred_fhd_{pred_type}{dataset_number}.json", "r") as f:
with open(f"distribution_data/pred_fhd_{pred_type}1.json", "r") as f:
pred_fhds = json.load(f)
# with open(f"distribution_data/pred_pc_{pred_type}{dataset_number}.json", "r") as f:
with open(f"distribution_data/pred_pc_{pred_type}1.json", "r") as f:
pred_pcs = json.load(f)
with open(f"distribution_data/like_pc{dataset_number}.json", "r") as f:
like_pcs = json.load(f)
with open(f"distribution_data/unlike_pc{dataset_number}.json", "r") as f:
unlike_pcs = json.load(f)
with open(f"distribution_data/like_fhd{dataset_number}.json", "r") as f:
unlike_fhds = json.load(f)
with open(f"distribution_data/unlike_fhd{dataset_number}.json", "r") as f:
unlike_fhds = json.load(f)
plt.hist(unlike_fhds, bins=100, label="Like FHD", density=True)
plt.hist(np.array(unlike_fhds)[np.array(unlike_fhds) > 0.1], bins=100,
label="Unlike FHD", density=True)
plt.hist(pred_fhds, bins=100, label="Prediction FHD", density=True)
plt.title("FHD Distributions")
plt.legend()
plt.savefig(f"fhd_dist{dataset_number}.png")
plt.clf()
plt.hist(like_pcs, bins=100, label="Like PC", density=True)
plt.hist(unlike_pcs, bins=100, label="Unlike PC", density=True)
plt.hist(pred_pcs, bins=100, label="Prediction PC", density=True)
plt.title("PC Distributions")
plt.legend()
plt.savefig(f"pc_dist{dataset_number}.png")
exit()
plot_data()
with h5py.File(f"data/{pred_type}{dataset_number}/data.h5", 'r') as data:
c = data.get("challenges")
r = np.array(data.get("responses"))
if pred_type == 'cycle':
c_refs = c[:1000]
r_refs = r[:1000]
else:
c_refs = c[()]
r_refs = r[()]
if pred_type == 'cycle':
preds = np.load(f'results/cycle{dataset_number}/preds/preds.npy')
elif pred_type == '8k':
preds = np.load(f'results/8k{dataset_number}/tmp/preds/preds.npy')
c_preds = preds['challenges']
r_preds = preds['responses']
pred_fhds = []
pred_pcs = []
for c_pred, r_pred in tqdm(zip(c_preds, r_preds), total=800):
idx = (c_refs == c_pred).all(axis=1).nonzero()
r = r_refs[idx]
r = center_crop(r)
fhd = calc_np_fhd(r_pred[None, :, :], r, do_gabor=True)
pc = calc_np_pc(r_pred[None, :, :], r, do_gabor=True)
pred_fhds.extend(fhd)
pred_pcs.extend(pc)
with open(f"distribution_data/pred_fhd_{pred_type}{dataset_number}.json",
"w") as f:
json.dump(pred_fhds, f)
with open(f"distribution_data/pred_pc_{pred_type}{dataset_number}.json",
"w") as f:
json.dump(pred_pcs, f)
def fhd_comp():
like_pcs = []
like_fhds = []
for idx, r_batch1 in enumerate(r_refs):
for idx2, r_batch2 in enumerate(r_refs[idx + 1:]):
print(f'{idx} - {idx2}')
like_fhd = calc_np_fhd(r_batch1, r_batch2, do_gabor=True)
like_fhds.extend(like_fhd)
like_pc = calc_np_pc(r_batch1, r_batch2, do_gabor=True)
like_pcs.extend(like_pc)
return like_fhds, like_pcs
def unlike_fhd_comp():
unlike_fhds = []
unlike_pcs = []
for idx, r_batch1 in enumerate(r_refs):
for idx2, r_batch2 in enumerate(r_refs[idx + 1:]):
print(f'{idx} - {idx2}')
np.random.shuffle(r_batch1)
np.random.shuffle(r_batch2)
unlike_pc = calc_np_pc(r_batch1, r_batch2, do_gabor=True)
unlike_pcs.extend(unlike_pc)
unlike_fhd = calc_np_fhd(r_batch1, r_batch2, do_gabor=True)
unlike_fhds.extend(unlike_fhd)
return unlike_fhds, unlike_pcs
like_fhds, like_pcs = fhd_comp()
unlike_fhds, unlike_pcs = unlike_fhd_comp()
with open(f"distribution_data/like_pc{dataset_number}.json", "w") as f:
json.dump(like_pcs, f)
with open(f"distribution_data/unlike_pc{dataset_number}.json", "w") as f:
json.dump(unlike_pcs, f)
with open(f"distribution_data/like_fhd{dataset_number}.json", "w") as f:
json.dump(like_fhds, f)
with open(f"distribution_data/unlike_fhd{dataset_number}.json", "w") as f:
json.dump(unlike_fhds, f)