def get_lungs_size(img, min_area=int(1024 * 1024 * 0.05)):
mask = np.zeros((1024, 1024), dtype=int)
# Try hog
right = lf.find_right_lung_hog(img)
left = lf.find_left_lung_hog(img)
# If hog don't find lungs then try lbp method
if right is None:
right = lf.find_right_lung_lbp(img)
left = lf.find_left_lung_lbp(img)
# If lbp don't find lungs then try haar method
if right is None:
right = lf.find_right_lung_haar(img)
left = lf.find_left_lung_haar(img)
# If don't find then return mask filled mask
if (right is None) or (left is None):
return 1024, 1024
x_r, y_r, width_r, height_r = right
x_l, y_l, width_l, height_l = left
# If found lungs is very small
if (width_r * height_r < min_area) or (width_l * height_l < min_area):
return 1024, 1024
x_l_union = min(x_r, x_l)
x_r_union = max(x_r + width_r, x_l + width_l)
y_u_union = max(y_r + height_r, y_l + height_l)
y_d_union = min(y_r, y_l)
w = x_r_union - x_l_union
h = y_u_union - y_d_union
return w, h
def lungs_finder_segmentator(img,
is_union=True,
min_area=int(1024 * 1024 * 0.05)):
mask = np.zeros((1024, 1024), dtype=int)
# Try hog
right = lf.find_right_lung_hog(img)
left = lf.find_left_lung_hog(img)
# If hog don't find lungs then try lbp method
if right is None:
right = lf.find_right_lung_lbp(img)
left = lf.find_left_lung_lbp(img)
# If lbp don't find lungs then try haar method
if right is None:
right = lf.find_right_lung_haar(img)
left = lf.find_left_lung_haar(img)
# If don't find then return mask filled mask
if (right is None) or (left is None):
return mask + 255
x_r, y_r, width_r, height_r = right
x_l, y_l, width_l, height_l = left
# If found lungs is very small
if (width_r * height_r < min_area) or (width_l * height_l < min_area):
return mask + 255
if is_union:
x_l_union = min(x_r, x_l)
x_r_union = max(x_r + width_r, x_l + width_l)
y_u_union = max(y_r + height_r, y_l + height_l)
y_d_union = min(y_r, y_l)
mask[y_d_union:y_u_union, x_l_union:x_r_union] = 255
else:
mask[y_r:y_r + height_r, x_r:x_r + width_r] = 255
mask[y_l:y_l + height_l, x_l:x_l + width_l] = 255
return mask
def scan(argv):
path_to_folder, use_labels = parse_argv(argv)
walks = list(os.walk(path_to_folder))
i = 0
while i < len(walks):
path, directories, files = walks[i]
files = [file for file in files if not file[0] == "."]
j = 0
while j < len(files):
file = files[j]
_, extension = os.path.splitext(file)
if extension == ".dcm" and dicom_support:
parsed = dicomparser.DicomParser(path + os.sep + file)
image = np.array(parsed.GetImage(), dtype=np.uint8)
if parsed.GetImageData(
)["photometricinterpretation"] == "MONOCHROME1":
image = 255 - image
image = cv2.equalizeHist(image)
image = cv2.medianBlur(image, 3)
elif extension in [
".bmp", ".pbm", ".pgm", ".ppm", ".sr", ".ras", ".jpeg",
".jpg", ".jpe", ".png", ".tiff", ".tif"
]:
image = cv2.imread(path + os.sep + file, 0)
image = cv2.equalizeHist(image)
image = cv2.medianBlur(image, 3)
else:
j += 1
continue
scaled_image = proportional_resize(image, 512)
right_lung_hog_rectangle = lf.find_right_lung_hog(scaled_image)
left_lung_hog_rectangle = lf.find_left_lung_hog(scaled_image)
right_lung_lbp_rectangle = lf.find_right_lung_lbp(scaled_image)
left_lung_lbp_rectangle = lf.find_left_lung_lbp(scaled_image)
right_lung_haar_rectangle = lf.find_right_lung_haar(scaled_image)
left_lung_haar_rectangle = lf.find_left_lung_haar(scaled_image)
color_image = cv2.cvtColor(scaled_image, cv2.COLOR_GRAY2BGR)
if right_lung_hog_rectangle is not None:
x, y, width, height = right_lung_hog_rectangle
cv2.rectangle(color_image, (x, y), (x + width, y + height),
(59, 254, 211), 2)
if use_labels:
cv2.putText(color_image, "HOG Right", (x + 5, y + 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (59, 254, 211),
1)
if left_lung_hog_rectangle is not None:
x, y, width, height = left_lung_hog_rectangle
cv2.rectangle(color_image, (x, y), (x + width, y + height),
(59, 254, 211), 2)
if use_labels:
cv2.putText(color_image, "HOG Left", (x + 5, y + 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (59, 254, 211),
1)
if right_lung_lbp_rectangle is not None:
x, y, width, height = right_lung_lbp_rectangle
cv2.rectangle(color_image, (x, y), (x + width, y + height),
(130, 199, 0), 2)
if use_labels:
cv2.putText(color_image, "LBP Right", (x + 5, y + 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (130, 199, 0),
1)
if left_lung_lbp_rectangle is not None:
x, y, width, height = left_lung_lbp_rectangle
cv2.rectangle(color_image, (x, y), (x + width, y + height),
(130, 199, 0), 2)
if use_labels:
cv2.putText(color_image, "LBP Left", (x + 5, y + 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (130, 199, 0),
1)
if right_lung_haar_rectangle is not None:
x, y, width, height = right_lung_haar_rectangle
cv2.rectangle(color_image, (x, y), (x + width, y + height),
(245, 199, 75), 2)
if use_labels:
cv2.putText(color_image, "HAAR Right", (x + 5, y + 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (245, 199, 75),
1)
if left_lung_haar_rectangle is not None:
x, y, width, height = left_lung_haar_rectangle
cv2.rectangle(color_image, (x, y), (x + width, y + height),
(245, 199, 75), 2)
if use_labels:
cv2.putText(color_image, "HAAR Left", (x + 5, y + 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (245, 199, 75),
1)
cv2.imshow("lungs-finder", color_image)
found_lungs = lf.get_lungs(scaled_image)
if found_lungs is not None:
cv2.imshow("Found lungs", found_lungs)
code = cv2.waitKey(0)
while code not in [2, 3, 27, 32]:
code = cv2.waitKey(0)
if code == 27:
exit(0)
elif code in [3, 32]:
j += 1
else:
if j > 0:
j -= 1
else:
if i > 0:
i -= 2
i += 1
def main(data):
decoded_data = base64.b64decode(data)
np_data = np.fromstring(decoded_data, np.uint8)
img = cv2.imdecode(np_data, cv2.IMREAD_UNCHANGED)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
image2 = cv2.equalizeHist(img_gray)
image = cv2.medianBlur(image2, 3)
scaled_image = image # cv2.resize(image, (1024,1024))
right_lung_hog_rectangle = lf.find_right_lung_hog(scaled_image)
left_lung_hog_rectangle = lf.find_left_lung_hog(scaled_image)
right_lung_lbp_rectangle = lf.find_right_lung_lbp(scaled_image)
left_lung_lbp_rectangle = lf.find_left_lung_lbp(scaled_image)
right_lung_haar_rectangle = lf.find_right_lung_haar(scaled_image)
left_lung_haar_rectangle = lf.find_left_lung_haar(scaled_image)
color_image = cv2.cvtColor(scaled_image, cv2.COLOR_GRAY2BGR)
if (right_lung_hog_rectangle is not None) and (left_lung_hog_rectangle
is not None):
x1, y1, width1, height1 = right_lung_hog_rectangle
x2, y2, width2, height2 = left_lung_hog_rectangle
cimage1 = image2[np.minimum(y1, y2):np.maximum(y1 + height1, y2 +
height2),
x1:x2 + width2]
pole1 = width1 * height1 + width2 * height2
if (right_lung_lbp_rectangle is not None) and (left_lung_lbp_rectangle
is not None):
x1, y1, width1, height1 = right_lung_lbp_rectangle
x2, y2, width2, height2 = left_lung_lbp_rectangle
cimage2 = image2[np.minimum(y1, y2):np.maximum(y1 + height1, y2 +
height2),
x1:x2 + width2]
pole2 = width1 * height1 + width2 * height2
if (right_lung_haar_rectangle is not None) and (left_lung_haar_rectangle
is not None):
x1, y1, width1, height1 = right_lung_haar_rectangle
x2, y2, width2, height2 = left_lung_haar_rectangle
cimage3 = image2[np.minimum(y1, y2):np.maximum(y1 + height1, y2 +
height2),
x1:x2 + width2]
pole3 = width1 * height1 + width2 * height2
if (pole1 >= pole2) and (pole1 >= pole3) and (pole1 > 0):
cimage = cimage1
if (pole2 > pole1) and (pole2 >= pole3):
cimage = cimage2
if (pole3 > pole1) and (pole3 > pole2):
cimage = cimage3
if (cimage.shape[0] > 0) and (cimage.shape[1] > 0):
image = cimage
filename = join(dirname(__file__), "resnet_cropped_CvHvP_detection2.h5")
model = tf.keras.models.load_model(filename)
filename = join(dirname(__file__), "unet_lung_seg_serial.hdf5")
model_croping = tf.keras.models.load_model(filename)
##
image_c = cimage
image_c = cv2.resize(image_c, (512, 512), interpolation=cv2.INTER_AREA)
image_c = np.array(image_c) / 255
image_c = image_c.astype('float32')
image_to_predict = image_c.reshape(1, 512, 512, 1)
predict_img = model_croping.predict(image_to_predict)
mask = predict_img[0, :, :, 0]
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0.35
mask = cv2.medianBlur(mask, 3)
#mask = cv2.resize(mask, (224,224), interpolation = cv2.INTER_AREA)
#img = skimage.morphology.remove_small_objects(img,min_size=10000, connectivity=8)
mask = skimage.morphology.opening(mask, disk(10))
mask = skimage.morphology.dilation(mask, disk(20))
mask = cv2.resize(mask, (224, 224), interpolation=cv2.INTER_AREA)
image_size = 224
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
#image = np.random.random((image_size, image_size, 3))
x_test = np.array(image) / 255
x_test = x_test.reshape(-1, image_size, image_size, 3)
x_test = x_test.astype('float32')
dim = (image_size, image_size)
# resize image
image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
img_tensor = x_test
conv_layer = model.get_layer('conv5_block3_2_conv')
heatmap_model = tf.keras.models.Model(
[model.inputs],
[model.get_layer('conv5_block3_2_conv').output, model.output])
with tf.GradientTape() as tape:
conv_output, predictions = heatmap_model(img_tensor)
loss = predictions[:, np.argmax(predictions[0])]
grads = tape.gradient(loss, conv_output)
pooled_grads = K.mean(grads, axis=(0, 1, 2))
heatmap = tf.reduce_mean(tf.multiply(pooled_grads, conv_output), axis=-1)
heatmap = np.maximum(heatmap, 0)
max_heat = np.max(heatmap)
if max_heat == 0:
max_heat = 1e-10
heatmap /= max_heat
heatmap = heatmap[0, :, :]
heatmap = cv2.resize(heatmap, dim, interpolation=cv2.INTER_AREA)
heatmap = 1 - heatmap
result_image = image
result_image[:, :, 1] = image[:, :, 1] * heatmap
result_image[:, :, 2] = image[:, :, 1] * heatmap
result_image[:, :, 0] = result_image[:, :, 0] * mask
result_image[:, :, 1] = result_image[:, :, 1] * mask
result_image[:, :, 2] = result_image[:, :, 2] * mask
pil_im = Image.fromarray(result_image)
if pil_im.mode != 'RGB':
pil_im = pil_im.convert('RGB')
buff = io.BytesIO()
pil_im.save(buff, format="PNG")
img_str = base64.b64encode(buff.getvalue())
return "" + str(img_str, 'utf-8')
def pred(data):
decoded_data = base64.b64decode(data)
np_data = np.fromstring(decoded_data, np.uint8)
img = cv2.imdecode(np_data, cv2.IMREAD_UNCHANGED)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
image2 = cv2.equalizeHist(img_gray)
image = cv2.medianBlur(image2, 3)
scaled_image = image # cv2.resize(image, (1024,1024))
right_lung_hog_rectangle = lf.find_right_lung_hog(scaled_image)
left_lung_hog_rectangle = lf.find_left_lung_hog(scaled_image)
right_lung_lbp_rectangle = lf.find_right_lung_lbp(scaled_image)
left_lung_lbp_rectangle = lf.find_left_lung_lbp(scaled_image)
right_lung_haar_rectangle = lf.find_right_lung_haar(scaled_image)
left_lung_haar_rectangle = lf.find_left_lung_haar(scaled_image)
color_image = cv2.cvtColor(scaled_image, cv2.COLOR_GRAY2BGR)
if (right_lung_hog_rectangle is not None) and (left_lung_hog_rectangle
is not None):
x1, y1, width1, height1 = right_lung_hog_rectangle
x2, y2, width2, height2 = left_lung_hog_rectangle
cimage1 = image2[np.minimum(y1, y2):np.maximum(y1 + height1, y2 +
height2),
x1:x2 + width2]
pole1 = width1 * height1 + width2 * height2
if (right_lung_lbp_rectangle is not None) and (left_lung_lbp_rectangle
is not None):
x1, y1, width1, height1 = right_lung_lbp_rectangle
x2, y2, width2, height2 = left_lung_lbp_rectangle
cimage2 = image2[np.minimum(y1, y2):np.maximum(y1 + height1, y2 +
height2),
x1:x2 + width2]
pole2 = width1 * height1 + width2 * height2
if (right_lung_haar_rectangle is not None) and (left_lung_haar_rectangle
is not None):
x1, y1, width1, height1 = right_lung_haar_rectangle
x2, y2, width2, height2 = left_lung_haar_rectangle
cimage3 = image2[np.minimum(y1, y2):np.maximum(y1 + height1, y2 +
height2),
x1:x2 + width2]
pole3 = width1 * height1 + width2 * height2
if (pole1 >= pole2) and (pole1 >= pole3) and (pole1 > 0):
cimage = cimage1
if (pole2 > pole1) and (pole2 >= pole3):
cimage = cimage2
if (pole3 > pole1) and (pole3 > pole2):
cimage = cimage3
if (cimage.shape[0] > 0) and (cimage.shape[1] > 0):
image = cimage
filename = join(dirname(__file__), "unet2_lung_seg_hist.hdf5")
model_croping = keras.models.load_model(filename,
custom_objects={
'dice_coef_loss':
dice_coef_loss,
'dice_coef': dice_coef
})
filename = join(dirname(__file__), "resnet_cropped_CvHvP_detection.hdf5'")
model = keras.models.load_model(filename)
##
image_c = cimage
image_c = cv2.resize(image_c, (512, 512), interpolation=cv2.INTER_AREA)
image_c = np.array(image_c) / 255
image_c = image_c.astype('float32')
image_to_predict = image_c.reshape(1, 512, 512, 1)
predict_img = model_croping.predict(image_to_predict)
mask = predict_img[0, :, :, 0]
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0.35
mask = cv2.medianBlur(mask, 3)
#mask = cv2.resize(mask, (224,224), interpolation = cv2.INTER_AREA)
#img = skimage.morphology.remove_small_objects(img,min_size=10000, connectivity=8)
mask = skimage.morphology.opening(mask, disk(10))
mask = skimage.morphology.dilation(mask, disk(20))
mask = cv2.resize(mask, (224, 224), interpolation=cv2.INTER_AREA)
image_size = 224
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
#image = np.random.random((image_size, image_size, 3))
x_test = np.array(image) / 255
x_test = x_test.reshape(-1, image_size, image_size, 3)
x_test = x_test.astype('float32')
prediction = model.predict(x_test)
pred = np.argmax(prediction, axis=1)
probability = prediction.max()
result = 'unknown'
if pred[0] == 0:
result = 'Covid_19'
if pred[0] == 1:
result = 'Healthy Lungs!'
if pred[0] == 2:
result = 'Pneumonia'
probability = round(probability * 100, 2)
a = (f'The pattient has {result} with probability of {probability} %')
return a