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Zero
File size: 3,649 Bytes
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import json
from scipy import stats
import numpy as np
import huggingface_hub
def check_mask_stats(img, mask, modality_type, target):
# img: np.array, shape=(H, W, 3) RGB image with pixel values in [0, 255]
# mask: np.array, shape=(H, W, 1) mask probability scaled to [0,255] with pixel values in [0, 255]
# modality_type: str, see target_dist.json for the list of modality types
# target: str, see target_dist.json for the list of targets
huggingface_hub.hf_hub_download('microsoft/BiomedParse', filename='target_dist.json', local_dir='./inference_utils')
huggingface_hub.hf_hub_download('microsoft/BiomedParse', filename="config.yaml", local_dir="./configs")
target_dist = json.load(open("inference_utils/target_dist.json"))
if modality_type not in target_dist:
raise ValueError(f"Currently support modality types: {list(target_dist.keys())}")
if target not in target_dist[modality_type]:
raise ValueError(f"Currently support targets for {modality_type}: {list(target_dist[modality_type].keys())}")
ms = mask_stats(mask, img)
ps = [stats.ks_1samp([ms[i]], stats.beta(param[0], param[1]).cdf).pvalue for i, param in enumerate(target_dist[modality_type][target])]
p_value = np.prod(ps)
adj_p_value = p_value**0.24 # adjustment for four test products
return adj_p_value
def mask_stats(mask, img):
# mask is a prediction mask with pixel values in [0, 255] for probability in [0, 1]
# img is a RGB image with pixel values in [0, 255]
if mask.max() <= 127:
return [0, 0, 0, 0]
return [mask[mask>=128].mean()/256, img[:,:,0][mask>=128].mean()/256,
img[:,:,1][mask>=128].mean()/256, img[:,:,2][mask>=128].mean()/256]
def combine_masks(predicts):
# predicts: a dictionary of pixel probability, {TARGET: pred_prob}
pixel_preds = {}
target_area = {}
target_probs = {}
for target in predicts:
pred = predicts[target]
pred_region = np.where(pred > 0.1)
target_area[target] = 0
target_probs[target] = 0
for (i,j) in zip(*pred_region):
if (i,j) not in pixel_preds:
pixel_preds[(i,j)] = {}
pixel_preds[(i,j)][target] = pred[i,j]
target_area[target] += 1
target_probs[target] += pred[i,j]
for target in predicts:
if target_area[target] == 0:
continue
target_probs[target] /= target_area[target]
# generate combined masks
combined_areas = {t: 0 for t in predicts}
for index in pixel_preds:
pred_target = sorted(pixel_preds[index].keys(), key=lambda t: pixel_preds[index][t], reverse=True)[0]
combined_areas[pred_target] += 1
# discard targets with small areas
discard_targets = []
for target in predicts:
if combined_areas[target] < 0.6 * target_area[target]:
discard_targets.append(target)
# keep the most confident target
most_confident_target = sorted(predicts.keys(), key=lambda t: target_probs[t], reverse=True)[0]
discard_targets = [t for t in discard_targets if t != most_confident_target]
masks = {t: np.zeros_like(predicts[t]).astype(np.uint8) for t in predicts if t not in discard_targets}
for index in pixel_preds:
candidates = [t for t in pixel_preds[index] if t not in discard_targets and pixel_preds[index][t] > 0.5]
if len(candidates) == 0:
continue
pred_target = max(candidates, key=lambda t: pixel_preds[index][t])
masks[pred_target][index[0], index[1]] = 1
return masks |