diff --git a/algorithm/SalamanderImage.py b/algorithm/SalamanderImage.py
index f2f9c00c61e502417f9ef47a906ab8e13a001465..517a434b713880f620c4cec238e31d26d368e8de 100644
--- a/algorithm/SalamanderImage.py
+++ b/algorithm/SalamanderImage.py
@@ -1,6 +1,26 @@
-class SalamanderImage():
- descriptors = []
- filename = ''
+from cv2 import cv2
+import numpy as np
+import algorithm.dsift as dsift
- def __init__(self, filename):
- self.filename
+
+class SalamanderImage:
+ descriptors = []
+ filename = ''
+
+ def __init__(self, filename):
+ self.filename = filename
+ self.descriptors = self.calculate_descriptors()
+
+ def calculate_descriptors(self):
+ """
+ Calculates the descriptors of the member image
+
+ Returns: The calculated descriptors
+
+ """
+
+ image = cv2.imdecode(np.fromfile(self.filename, dtype=np.uint8), cv2.IMREAD_GRAYSCALE)
+
+ if image is None:
+ raise FileNotFoundError("Cannot find image file " + self.filename)
+ return dsift.compute_descriptors(image)
diff --git a/algorithm/brute_force_matching.py b/algorithm/brute_force_matching.py
index c69d20af84a98caf9d5af57f013ec199858bc70c..2c383b9654a8fab57648c5a15fa09db6e45aa410 100644
--- a/algorithm/brute_force_matching.py
+++ b/algorithm/brute_force_matching.py
@@ -1,13 +1,24 @@
import os
from cv2 import cv2
-from algorithm.imageprocessing import create_salamander_image
import glob
-
-min_good_match = 15
-match_dist = 0.75
+from algorithm.SalamanderImage import SalamanderImage
def match_single_image(input_salamander, match_salamander):
+ """
+ Compares two SalamanderImages and determines if they are similar enough to be a match
+
+ Args:
+ input_salamander: SalamanderImage of input salamander
+ match_salamander: SalamanderImage of salamander from the database
+
+ Returns:
+ Boolean value indicating if the comparison was a match and the number of hits gotten in the comparison
+ """
+
+ min_good_match = 15
+ match_dist = 0.75
+
match = cv2.BFMatcher().knnMatch(input_salamander.descriptors, match_salamander.descriptors, k=2)
goodmatch = []
@@ -20,16 +31,28 @@ def match_single_image(input_salamander, match_salamander):
def match_file_structure(input_image: str, match_directory: str):
+ """
+ Loops through a given directory of salamanders represented by folders containing their images, and finds the best
+ match (if any) based on an input image
+
+ Args:
+ input_image: SalamanderImage of input salamander
+ match_directory: Path of directory to looped through
+
+ Returns:
+ The ID of the best matched salamander or None if no match is found
+ """
+
best_match = -1
match_count = 0
# check if input path is valid:
if os.path.isfile(input_image):
- input_salamander = create_salamander_image(input_image)
+ input_salamander = SalamanderImage(input_image)
for folder in os.listdir(match_directory):
name_list = glob.glob(os.path.join(match_directory, folder, "*_str.*"))
for filename in name_list:
- res, num_matches = match_single_image(input_salamander, create_salamander_image(filename))
+ res, num_matches = match_single_image(input_salamander, SalamanderImage(filename))
if res and num_matches > match_count:
match_count = num_matches
best_match = int(folder)
diff --git a/algorithm/dsift.py b/algorithm/dsift.py
index 19ad68f07fdc2b421a6e6338943a9faa8d2a3658..dd782630657209eed335aa590b53f9c7b0d3c73a 100644
--- a/algorithm/dsift.py
+++ b/algorithm/dsift.py
@@ -7,6 +7,15 @@ sift = cv2.SIFT_create()
def compute_descriptors(image):
- dense = sift.compute(image,kp)
+ """
+ Computes the descriptors of an incoming image
+ Args:
+ image: Image from openCV
+
+ Returns:
+ Descriptors of image
+ """
+
+ dense = sift.compute(image, kp)
des = dense[1]
return des
diff --git a/algorithm/imageprocessing.py b/algorithm/imageprocessing.py
deleted file mode 100644
index fdc01ae30d09fd3921aaef1366d51795a33ed600..0000000000000000000000000000000000000000
--- a/algorithm/imageprocessing.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from cv2 import cv2
-import numpy as np
-
-from algorithm.SalamanderImage import SalamanderImage
-
-import algorithm.dsift as dsift
-import algorithm.segmentation as segmentation
-
-
-def create_salamander_image(filename: str):
- salamander_image = SalamanderImage(filename)
-
- salamander_image.filename = filename
- salamander_image.descriptors = get_descriptors(filename)
-
- return salamander_image
-
-
-def get_descriptors(filename: str):
- image = get_straightened_image(filename)
- return calculate_descriptors(image)
-
-
-# Reads, straightens, resizes the image and returns it
-def get_straightened_image(filename: str):
- straightened_filename = filename # [0:-4] + '_str.jpg'
- image = get_image(straightened_filename)
-
- return image
-
-
-# Should return a binary image (numpy ndarray) with 1 for "Part of salamander"
-# and 0 for "Not part of the salamander".
-def get_segmented_image(filename: str):
- image = get_image(filename)
- return segmentation.get_salamander_mask(image)
-
-
-def get_image(filename):
- img = cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_GRAYSCALE)
-
- if img is None:
- raise FileNotFoundError("Cannot find image file " + filename)
-
- return img
-
-
-# Calculates descriptors from preprocessed image
-def calculate_descriptors(image):
- return dsift.compute_descriptors(image)
diff --git a/algorithm/predict_salamander_abdomen.py b/algorithm/predict_salamander_abdomen.py
index e54cd8cc92182a39330f1df245ca06f02f439e69..a4b8937e5b1c807ecfd739dd06d278463c8f6283 100644
--- a/algorithm/predict_salamander_abdomen.py
+++ b/algorithm/predict_salamander_abdomen.py
@@ -21,7 +21,6 @@ def run_prediction_dlc(config: str, image: np.ndarray, shuffle: int = 1,
if gputouse is not None: # gpu selection
os.environ['CUDA_VISIBLE_DEVICES'] = str(gputouse)
- # tf.compat.v1.reset_default_graph()
reset_default_graph()
# record cwd to return to this directory in the end:
@@ -30,7 +29,6 @@ def run_prediction_dlc(config: str, image: np.ndarray, shuffle: int = 1,
model_folder = os.path.join(cfg["project_path"],
str(auxiliaryfunctions.GetModelFolder(train_fraction, shuffle, cfg)))
path_test_config = Path(model_folder) / 'test' / 'pose_cfg.yaml'
- # print(path_test_config)
try:
dlc_cfg = load_config(str(path_test_config))
except FileNotFoundError:
@@ -58,24 +56,17 @@ def run_prediction_dlc(config: str, image: np.ndarray, shuffle: int = 1,
increasing_indices = np.argsort([int(m.split('-')[1]) for m in snapshots])
snapshots = snapshots[increasing_indices]
- # print("Using %s" % snapshots[snapshot_index], "for model", model_folder)
-
##################################################
# Load and setup CNN part detector
##################################################
# Check if data already was generated:
dlc_cfg['init_weights'] = os.path.join(model_folder, 'train', snapshots[snapshot_index])
- trainingsiterations = (dlc_cfg['init_weights'].split(os.sep)[-1]).split('-')[-1]
# Update batchsize (based on parameters in config.yaml)
dlc_cfg['batch_size'] = 1
- # Name for scorer:
- # DLCscorer, DLCscorerlegacy = auxiliaryfunctions.GetScorerName(cfg,shuffle,trainFraction,trainingsiterations=trainingsiterations)
- # return tensorflow session, input and outbut (whatever those are):
sess, inputs, outputs = predict.setup_pose_prediction(dlc_cfg)
- # sess, inputs, outputs = predict.setup_pose_prediction(dlc_cfg)
# update number of outputs and adjust pandas indices
dlc_cfg['num_outputs'] = cfg.get('num_outputs', 1)
@@ -86,26 +77,12 @@ def run_prediction_dlc(config: str, image: np.ndarray, shuffle: int = 1,
##################################################
# Loading the images
##################################################
- # Predicting data:
- # print("session: ", sess)
- # PredictedData,nframes,nx,ny=get_poses_deeplabcut_model(dlc_cfg, sess, inputs, outputs,image)
ny, nx, nc = np.shape(image)
nframes = 1
- # print("Frame dimensions: ", nx,ny)
PredictedData = np.zeros((nframes, dlc_cfg['num_outputs'] * 3 * len(dlc_cfg['all_joints_names'])))
- # change from int:
frame = img_as_ubyte(image)
pose = predict.getpose(frame, dlc_cfg, sess, inputs, outputs)
PredictedData[0, :] = pose.flatten()
-
- stop = time.time()
- # print("PredictedData done:\n")
-
- # closing the session:
- # device = cuda.get_current_device()
- # device.reset()
-
- # tf.Graph.reset_default_graph()
return PredictedData[0], nframes, nx, ny
diff --git a/algorithm/segmentation.py b/algorithm/segmentation.py
deleted file mode 100644
index 3e530cfa6ac1a40542cbd98841932487fcefd097..0000000000000000000000000000000000000000
--- a/algorithm/segmentation.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from cv2 import cv2
-import numpy as np
-
-
-def get_salamander_mask(image):
- ret, threshold = cv2.threshold(image, np.mean(image) - 40, 255, 0)
-
- i, contours, hierarchy = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
- largest_area, largest_contour_index = find_biggest_contour(contours, image.size)
-
- cv2.drawContours(image, contours[largest_contour_index], -1, (0, 0, 255), 3)
-
- mask = np.zeros(image.shape)
-
- cv2.fillPoly(mask, pts=[contours[largest_contour_index]], color=(255, 255, 255))
-
- return mask
-
-
-def find_biggest_contour(contours, imsize):
- largest_area = 0
- largest_contour_index = -1
-
- i = 0
- total_contours = len(contours)
- while i < total_contours:
- area = cv2.contourArea(contours[i])
- if largest_area < area < imsize * 0.9:
- largest_area = area
- largest_contour_index = i
-
- i += 1
-
- return largest_area, largest_contour_index
diff --git a/algorithm/straighten_with_dlc.py b/algorithm/straighten_with_dlc.py
index cdf9861e81e22f186e4bc3f8290a70a8b18e4147..1b8692ee22340f503366a618a5870ee79024e0b6 100644
--- a/algorithm/straighten_with_dlc.py
+++ b/algorithm/straighten_with_dlc.py
@@ -169,11 +169,14 @@ def straighten(image):
def generate_map_from_bellycurve(curve, width=STRAIGTHENED_IMAGE_WIDTH):
"""
Generates a map that can be passed to cv2.remap to extract the belly pattern
- """
- # salamander_length = np.linalg.norm(curve[len(curve) - 1] - curve[0])
+ Args:
+ curve: Interpolated curve along the spine
+ width: Width of the abdominal pattern
+
+ Returns: Map generated from the curve
+ """
- # salamander_width = salamander_length * STRAIGHTENED_IMAGE_ASPECT_RATIO
salamander_width = width + SHOULDER_ESTIMATION_BUFFER
gradient = np.gradient(curve, axis=0)
@@ -200,10 +203,16 @@ def get_smooth_curve(points, num_points):
Takes in an array of 2-D points and returns a new set of points
with numpoints elements where the missing points are interpolated
using cubic interpolation
+
+ Args:
+ points:
+ num_points:
+
+ Returns: Interpolated points
+
"""
# Calculate distance between all the points
distance = np.sqrt(np.sum(np.diff(points, axis=0) ** 2, axis=1))
- # print("distance, ", distance.shape)
# Accumulate distance to use as parameter
accumulated_distance = np.cumsum(distance)
@@ -223,7 +232,14 @@ def get_smooth_curve(points, num_points):
def halfway_between(point1, point2):
"""
- halfway_between() calculates a point between two points:
+ halfway_between() calculates a point between two points
+
+ Args:
+ point1:
+ point2:
+
+ Returns: A point between the two provided points
+
"""
vec = [(point2[0] - point1[0]) / 2, (point2[1] - point1[1]) / 2]
return [point1[0] + vec[0], point1[1] + vec[1]]
diff --git a/api/endpoints/editsalamander.py b/api/endpoints/editsalamander.py
index 310e7e8baf14e431966e3802a7b37f76afa2841c..7c292f59218aaf26d7ed8e5ac3e6763446ca644c 100644
--- a/api/endpoints/editsalamander.py
+++ b/api/endpoints/editsalamander.py
@@ -10,7 +10,8 @@ import glob
from shutil import move
from path_constants import _ACCESS_DATABASE
from image_encoder.image_encoder import *
-import cv2
+from cv2 import cv2
+import numpy as np
"""
Endpoint for editing salamanders.
@@ -43,7 +44,10 @@ class EditSalamander(Resource):
basename = os.path.basename(path)
if basename.__contains__(str(image_id)):
- image = cv2.imread(path)
+ image = cv2.imdecode(np.fromfile(path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
+ if image is None:
+ raise FileNotFoundError("Cannot find image file " + path)
+
if not basename.__contains__("str"): # Scale only original to set size
height, width, _ = image.shape
diff --git a/api/endpoints/findsalamanderinfo.py b/api/endpoints/findsalamanderinfo.py
index e18ae1410a145718340024fa9c9ccf4a08adbb84..d8e2af7e9de2c15d183782356c8020304a7787f1 100644
--- a/api/endpoints/findsalamanderinfo.py
+++ b/api/endpoints/findsalamanderinfo.py
@@ -2,7 +2,7 @@ from flask import request, jsonify
from flask_restful import Resource
from flask_jwt_extended import get_jwt_identity, jwt_required
from algorithm.straighten_with_dlc import straighten
-import cv2
+from cv2 import cv2
import os
from api import limiter
from image_encoder.image_encoder import *
diff --git a/api/endpoints/salamander.py b/api/endpoints/salamander.py
index 5240d29ca7e7918518a21a7b08d08cfd4d4a49c5..abb739733cea808506aacb0ddf92efb8db49c4f5 100644
--- a/api/endpoints/salamander.py
+++ b/api/endpoints/salamander.py
@@ -5,7 +5,8 @@ from api import db, limiter
from api.models.dbmodels import Location, User, Salamander
import os
import glob
-import cv2
+import numpy as np
+from cv2 import cv2
from image_encoder.image_encoder import *
from path_constants import _ACCESS_DATABASE
@@ -33,8 +34,10 @@ class SalamanderEndpoint(Resource):
list_of_paths = glob.glob(os.path.join(path_to_salamander_images, '*.*'))
for path in list_of_paths:
if not path.__contains__("_str"):
+ image = cv2.imdecode(np.fromfile(path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
- image = cv2.imread(path)
+ if image is None:
+ raise FileNotFoundError("Cannot find image file " + path)
# scaling to set size
height, width, _ = image.shape
diff --git a/run.py b/run.py
index 4a8885568d3f3175ce002776a21e6719f345f628..6e51c1ac74ee3c4a4a97943856f71de3c7874cc9 100644
--- a/run.py
+++ b/run.py
@@ -1,4 +1,4 @@
from api import app
if __name__ == "__main__":
- app.run(debug=True, host='0.0.0.0')
\ No newline at end of file
+ app.run(debug=True, host='0.0.0.0')