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prediction_utilities.py
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prediction_utilities.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 26 13:52:52 2019
@author: laakom
"""
import numpy as np
import cv2
from sklearn.feature_extraction import image
from tqdm import tqdm
#from keras.applications.imagenet_utils import preprocess_input
#from utils_viz import adjust_gamma_training
def ill_predict(method,model,testsamplesID,groundtruth_dic):
if method == 'Bianco':
ground_truths, precitions = Bianco_CNN_predict(model, testsamplesID,groundtruth_dic)
if method == 'FC4':
ground_truths, precitions = FC4_predict(model, testsamplesID,groundtruth_dic)
if method == 'BoCF':
ground_truths, precitions = FoCF_predict(model, testsamplesID,groundtruth_dic)
return ground_truths,precitions
def Bianco_CNN_predict(model, testsamplesID,groundtruth_dic):
predictions = []
ground_truths = []
for ID in tqdm(testsamplesID):
img = (cv2.resize(cv2.imread(ID,-1), (0,0), fx=0.25, fy=0.25) *1.0 / 65536.0).astype('float32')
img_patches = image.extract_patches_2d(img,(32,32), 8)
img_patches = (img_patches ).astype('float32')
patches_prediction = np.mean(model.predict(img_patches),axis=0)
patches_prediction = np.clip(patches_prediction, 10**(-6), np.max(patches_prediction) )
patches_prediction /= np.linalg.norm(patches_prediction,2)
predictions.append(patches_prediction)
ground_truths.append(groundtruth_dic[ID])
return np.array(ground_truths,dtype = 'float32' ), np.array(predictions)
def FoCF_predict(model, testsamplesID,groundtruth_dic):
predictions = []
ground_truths = []
for ID in tqdm(testsamplesID):
img = cv2.resize(cv2.imread(ID,-1),(227,227))
img = (img * 1.0/ 65535.0 ).astype('float32')
#img =adjust_gamma_training(img)
img = np.expand_dims(img, axis=0)
#img =preprocess_input(img)
#img_patches = preprocess_input(np.float32(img[..., ::-1]*255.0))
patches_prediction = model.predict(img)
patches_prediction = np.clip(patches_prediction[0], 10**(-6), np.max(patches_prediction[0]) )
patches_prediction /= np.linalg.norm(patches_prediction,2)
predictions.append(patches_prediction)
ground_truths.append(groundtruth_dic[ID])
return np.array(ground_truths,dtype = 'float32' ), np.array(predictions)
def FC4_predict(model, testsamplesID,groundtruth_dic):
predictions = []
ground_truths = []
for ID in tqdm(testsamplesID):
img = cv2.resize(cv2.imread(ID,-1),(227,227))
img = (img * 1.0/ 65535.0 ).astype('float32')
#img =adjust_gamma_training(img)
img = np.expand_dims(img, axis=0)
#img =preprocess_input(img)
#img_patches = preprocess_input(np.float32(img[..., ::-1]*255.0))
patches_prediction = model.predict(img)
patches_prediction = np.clip(patches_prediction[0], 10**(-6), np.max(patches_prediction[0]) )
patches_prediction /= np.linalg.norm(patches_prediction,2)
predictions.append(patches_prediction)
ground_truths.append(groundtruth_dic[ID])
return np.array(ground_truths,dtype = 'float32' ), np.array(predictions)
def angular_error_reproduction(ground_truth, prediction):
"""
calculate angular error(s) between the ground truth RGB triplet(s) and the predicted one(s)
:param ground_truth: N*3 or 1*3 Numpy array, each row for one ground truth triplet
:param prediction: N*3 Numpy array, each row for one predicted triplet
:return: angular error(s) in degree as Numpy array
"""
res = np.divide(ground_truth,prediction)
res_norm = res / np.linalg.norm(res, ord=2, axis=-1, keepdims=True)
u = np.ones(np.shape(res)) / np.sqrt(3)
u_norm = u / np.linalg.norm(u, ord=2, axis=-1, keepdims=True)
return 180 * np.arccos(np.sum(res_norm * u_norm, axis=-1)) / np.pi
def angular_error_recovery(ground_truth, prediction):
"""
calculate angular error(s) between the ground truth RGB triplet(s) and the predicted one(s)
:param ground_truth: N*3 or 1*3 Numpy array, each row for one ground truth triplet
:param prediction: N*3 Numpy array, each row for one predicted triplet
:return: angular error(s) in degree as Numpy array
"""
ground_truth_norm = ground_truth / np.linalg.norm(ground_truth, ord=2, axis=-1, keepdims=True)
prediction_norm = prediction / np.linalg.norm(prediction, ord=2, axis=-1, keepdims=True)
return 180 * np.arccos(np.sum(ground_truth_norm * prediction_norm, axis=-1)) / np.pi
def summary_angular_errors(errors):
errors = sorted(errors)
def g(f):
return np.percentile(errors, f * 100)
median = g(0.5)
mean = np.mean(errors)
max = np.max(errors)
trimean = 0.25 * (g(0.25) + 2 * g(0.5) + g(0.75))
results = {
'25': np.mean(errors[:int(0.25 * len(errors))]),
'75': np.mean(errors[int(0.75 * len(errors)):]),
'95': g(0.95),
'tri': trimean,
'med': median,
'mean': mean,
'max': max
}
return results
def just_print_angular_errors(results):
print("25: %5.3f," % results['25'], end=' ')
print("med: %5.3f" % results['med'], end=' ')
print("tri: %5.3f" % results['tri'], end=' ')
print("avg: %5.3f" % results['mean'], end=' ')
print("75: %5.3f" % results['75'], end=' ')
print("95: %5.3f" % results['95'], end=' ')
print("max: %5.3f" % results['max'])
def print_angular_errors(errors):
print("%d images tested. Results:" % len(errors))
results = summary_angular_errors(errors)
just_print_angular_errors(results)
def save_errors(errors,path):
import csv
print("%d images tested. Results:" % len(errors))
results = summary_angular_errors(errors)
w = csv.writer(open(path+ '.csv', "w"))
for key, val in results.items():
w.writerow([key, val])
just_print_angular_errors(results)