Console.log("Test Acc:", score[1])
if args["predict"] != None and args["predict"] != "False":
if args["predict"] != "True":
Console.info("Predict for", args["predict"])
else:
Console.info("Predict...")
# new instance where we do not know the answer
Xnew = X_train
Xnew = np.array(Xnew)
# make a prediction
ynew = model.predict(Xnew)
error_count = 0
for i in range(len(ynew)):
predict = 1 if ynew[i][0] > 0.5 else 0
if y_train[i] != predict:
error_count = error_count + 1
Console.error(
"ID:",
i,
"Original",
y_train[i],
"Predict",
predict,
"-",
str(math.trunc(ynew[i][0] * 100) / 100),
)
Console.wran("Img with error", error_count)
Console.info("Fix image colors")
X_img = processeImg(files, y_lower_upper)
Console.info("Run model_valid_hand")
files = []
X_to_predict = []
for img_file, img, hist in X_img:
files.append((img_file, img))
X_to_predict.append(hist)
X_to_predict = np.array(X_to_predict)
# make a prediction
y_valid_hand = model_valid_hand.predict(X_to_predict)
Console.info("Saveing image..")
total_file = len(files)
for i in range(total_file):
img_file, img = files[i]
progress = float(i / total_file), (i + 1)
# updateProgress(progress[0], progress[1], total_file, img_file)
if y_valid_hand[i][0] > 0.5:
path = os.path.join(__location__, "deep_fight", "hand", img_file)
else:
path = os.path.join(__location__, "deep_fight", "not_hand", img_file)
cv2.imwrite(path, img)
if y_valid_hand[i][0] < 0.6 and y_valid_hand[i][0] > 0.4:
Console.wran("Image:", img_file, "hand valid:", y_valid_hand[i][0])
# updateProgress(1, total_file, total_file, img_file)
Console.info("Proeces", total_file, "images")
# para creae las iagenes en capetas separadas
