Merge pull request #7 from kookmin-sw/feature/face-shape

Feature/face shape 얼굴형 검출 전용 디렉토리

main.py

@@ -1,2 +1,6 @@
-import controller
+from shape_detect import shape_detect
 
+train_path = "./train"
+test_path = "./test"
+
+shape_detect.run_label(test_path)

shape_detect/controller.py

@@ -0,0 +1,44 @@
+from shape_detect.utils import landmark, line, norm, ratio, vector
+import cv2
+import pandas as pd
+import os
+
+def get_vector(img_path):
+
+    display_img, pos = landmark.get_landmark(img_path)#사진 랜드마크 처리
+
+    distances = line.get_line(display_img, pos)#랜드마크간 특징 거리 추출
+
+    norm_distances = norm.get_norm(distances) #거리정보 정규화
+
+    vectors = vector.get_vector(pos)#각도 산출을 위한 벡터 추출
+
+    angles = vector.get_angles(vectors) #각도 추출
+
+    rations = ratio.get_ratio(distances) #길이 간 비율 정보
+
+
+    # print("norm: ", norm_distances, "\n\n")
+    # print("angle: ", angles, "\n\n")
+    # print("ratio: ", rations, "\n\n")
+
+    # cv2.imshow("Face Landmark", display_img)
+    # cv2.waitKey(0)
+
+    return norm_distances, angles, rations
+
+def list_files(directory):
+    file_names = []
+    print(directory)
+    for root, _, files in os.walk(directory):
+        print(files)
+        for file in files:
+            file_names.append(os.path.join(root, file))
+    return file_names
+
+def make_label(data):
+    row = ["D1","D2","D3","D4","D5","D6","D7","R1","R2","R3","R4","R5","R6","R7","R8","R9","R10","A1","A2","A3"]
+    df = pd.DataFrame(data, columns = row)
+    df.to_csv("./test.csv")
+    print(df)
+

shape_detect/shape_detect.py

@@ -0,0 +1,10 @@
+import shape_detect.controller as controller
+
+def run_label(path):
+    data_set_directory = controller.list_files(path)
+    vectors = []
+    for path in data_set_directory:
+        norm_distances, angles, rations = controller.get_vector(path)
+        vectors.append(norm_distances + rations + angles)
+
+    controller.make_label(vectors)

utils/line.py → shape_detect/utils/line.py

@@ -1,5 +1,5 @@
 import cv2
-from utils import distance
+import shape_detect.utils.distance as distance
 
 def get_line(display_image, pos):
     D_LIST = [(2,14),(76,79),(71,8),(8, 12),(4,12),(6,10),(7,9)] #선분을 만들 주요 점 관계
@@ -11,7 +11,6 @@ def get_line(display_image, pos):
         cv2.putText(display_image, "D{}".format(i + 1), (pos[start][0], pos[start][1] - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 1)
 
         dist = distance.get_distance( (pos[start][0], pos[start][1]), (pos[end][0], pos[end][1]))
-        print("D{}".format(i + 1),"'s distances is {}".format(dist), "\n" )
 
         distances.append(dist)
 

utils/ratio.py → shape_detect/utils/ratio.py

@@ -12,7 +12,5 @@ def get_ratio(distances):
     rations.append(distances[4] / distances[1])
     rations.append(distances[3] / distances[4])
     rations.append(distances[6] / distances[5])
-    print("distances: ", distances, "\n")
-    print("Ratio: ", rations, "\n")
 
     return rations

utils/vector.py → shape_detect/utils/vector.py

@@ -1,4 +1,4 @@
-from utils import angle
+import shape_detect.utils.angle as angle
 
 def get_vector(pos):
     #각도를 구하기위한 벡터
@@ -7,7 +7,6 @@ def get_vector(pos):
     V.append( (pos[12][0] - pos[8][0], pos[12][1] - pos[8][1]) ) #D4
     V.append( (pos[12][0] - pos[4][0], pos[12][1] - pos[4][1]) ) #D5
     V.append( (pos[10][0] - pos[8][0], pos[10][1] - pos[8][1]) ) #8-10
-    print("Vector: ", V, "\n")
     return V
 
 def get_angles(V):
@@ -16,6 +15,5 @@ def get_angles(V):
     A.append(angle.get_angle(V[0], V[1]))
     A.append(angle.get_angle(V[0], V[3]))
     A.append(angle.get_angle(V[1], V[2]))
-    print("A:", A, "\n")
 
     return A

test.csv

@@ -0,0 +1,4 @@
+,D1,D2,D3,D4,D5,D6,D7,R1,R2,R3,R4,R5,R6,R7,R8,R9,R10,A1,A2,A3
+0,0.18925852735638332,0.15909147190070777,0.25902758173236456,0.09989648802183161,0.15701627961111192,0.08964434472955639,0.04606530664804442,0.8406039829377437,0.7306500956022944,0.614187380497132,1.2053433429013027,0.5709238873292749,1.1143646408839778,0.47366079606416667,0.9869559803250108,0.636217392675772,0.5138674033149172,55.00117175299869,68.94829220934821,34.385443504740294
+1,0.19009156487794346,0.15493472929296562,0.2548705679181133,0.09787308040559582,0.16071524941984924,0.09234440866598308,0.049170399419549427,0.815053152897385,0.7458356860530773,0.6078957274609448,1.182784866800985,0.5745839862696814,1.0598701298701299,0.4857890918267563,1.0373093892716154,0.6089844041489442,0.5324675324675324,53.16379875419142,67.65019622075658,35.386029506161094
+2,0.19424137033619607,0.1585515887098682,0.2496037097499231,0.09794875434736318,0.1621714339792273,0.09003477890552913,0.04744836397189296,0.8162606577344702,0.7781990521327014,0.6352132701421801,1.1977533007513312,0.5551827266759063,1.0878990934174222,0.46352009744214373,1.0228307095426397,0.603982785031731,0.5270003941663381,53.124855247662765,64.32924645581284,35.05652754431111