def test_mode():
while True:
last_time = time.time()
raw_img = cv.imread(image_name)
process_image(raw_img, do_roi)
print('loop took {} seconds'.format(time.time() - last_time))
if cv.waitKey(25) & 0xFF == ord('q'):
cv.destroyAllWindows()
break
def real_time_mode():
while True:
last_time = time.time()
screen = grab_screen(region=(0, 40, 800, 600))
screen = cv.cvtColor(screen, cv.COLOR_BGR2RGB)
cv.imshow("Real time screen", screen)
process_image(screen, do_roi)
print('loop took {} seconds'.format(time.time() - last_time))
if cv.waitKey(25) & 0xFF == ord('q'):
cv.destroyAllWindows()
break
def test_transform(d, i, tmpdir):
path, name = os.path.split(i)
destination = os.path.join(tmpdir, d, name)
image = (i, destination, settings['IMAGE_PROCESS'][d])
process_image(image, settings)
transformed = Image.open(destination)
expected_path = os.path.join(path, 'results', d, name)
expected = Image.open(expected_path)
img_diff = ImageChops.difference(transformed, expected).getbbox()
self.assertEqual(img_diff, None)
def test_transform(d, i, tmpdir):
path, name = os.path.split(i)
destination = os.path.join(tmpdir, d, name)
image = (i, destination, settings['IMAGE_PROCESS'][d])
process_image(image, settings)
transformed = Image.open(destination)
expected_path = os.path.join(path, 'results', d, name)
expected = Image.open(expected_path)
img_diff = ImageChops.difference(transformed, expected).getbbox()
self.assertEqual(img_diff, None)
def main():
in_args = get_input_args()
norm_image = process_image(in_args.input)
model, arch, class_idx = load_model(in_args.checkpoint)
classes, probs = predict(norm_image, model, in_args.top_k, in_args.gpu, in_args.category_names, arch, class_idx)
print_predict(classes, probs)
pass
def process(self, state):
processed_image = process_image(state, flip=self.flip_episode)
self.frames.append(processed_image)
frame = LazyFrames(list(self.frames))
# frame_np = np.array(frame)
# print (frame_np.shape)
return frame
def evaluate(individual):
"""
Evaluacija jedinke, usporedba s pripadnom maskom.
:param individual: Jedinka za evaluaciju
:return: Evaluacija jedinke
"""
sum = 0
for i, (name, info) in enumerate(image_process.IMAGES_WITH_INFO.items()):
if i == TRAIN_NO: break
image, gray_image, mask, no_cells = info
processed = image_process.process_image(copy.copy(image), individual)
sum += compare(processed, mask, no_cells)
return (sum / TRAIN_NO),
def setImage():
global panelA, panelB, panelC, root, individual, currentImage
if currentImage is None:
return
image = currentImage
processed = cv2.cvtColor(
ipss.process_image(ipss.revert_img(copy.copy(image)), individual),
cv2.COLOR_GRAY2RGB)
contours = cv2.cvtColor(
ipss.draw_contours(ipss.revert_img(copy.copy(image)),
ipss.find_contours(processed)), cv2.COLOR_BGR2RGB)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)
processed = Image.fromarray(processed)
contours = Image.fromarray(contours)
image = ImageTk.PhotoImage(image)
processed = ImageTk.PhotoImage(processed)
contours = ImageTk.PhotoImage(contours)
if panelA is None or panelB is None or panelC is None:
panelA = Label(root, image=image)
panelA.image = image
panelA.pack(side="top", padx=10, pady=10)
panelB = Label(root, image=processed)
panelB.image = processed
panelB.pack(side="right", padx=10, pady=10)
panelC = Label(root, image=contours)
panelC.image = contours
panelC.pack(side="left", padx=10, pady=10)
else:
panelA.configure(image=image)
panelB.configure(image=processed)
panelC.configure(image=contours)
panelA.image = image
panelB.image = processed
panelC.image = contours
import joblib
import cv2
import image_process
from parameters import *
import copy
import numpy as np
import genetics
image_process.load('images_serialized/images_with_info.dict')
individual = joblib.load('transformations/best.ind')
for i, (name, info) in enumerate(image_process.IMAGES_WITH_INFO.items()):
if i == TRAIN_NO: break
image, gray_image, mask, no_cells = info
processed = image_process.process_image(copy.copy(image), individual)
background = image_process.kmeans(processed, 1)
if background > np.array(128):
processed = (255 - processed)
cv2.imwrite('results2/' + name + '_image.png', image)
cv2.imwrite('results2/' + name + '_mask.png', mask)
cv2.imwrite('results2/' + name + '_result.png', processed)