testing and added data for testing similarity

server.py

@@ -9,6 +9,7 @@ import cv2
 import numpy as np
 import mediapipe as mp
 import matplotlib.pyplot as plt
+import json
 
 
 # First step is to initialize the Hands class an store it in a variable
@@ -38,7 +39,8 @@ def read_root():
 @app.post("/")
 async def image(image: UploadFile = File(...)):
     content_type = image.content_type
-    if(content_type != "image/jpeg" and content_type != "image/png") :
+    print(content_type)
+    if(content_type != "image/jpeg" and content_type != "image/png" and content_type != "image/jpg") :
       raise HTTPException(status_code=404,  detail="Fail bukan foto")
     image_name = image.filename
     with open("temp/"+image_name, "wb") as buffer:
@@ -48,13 +50,19 @@ async def image(image: UploadFile = File(...)):
     sample_img = cv2.flip(sample_img, 1)
     results = hands.process(cv2.cvtColor(sample_img, cv2.COLOR_BGR2RGB))
     if not results.multi_hand_landmarks:
+      if os.path.exists("temp/"+image_name):
+        os.remove("temp/"+image_name)
       raise HTTPException(status_code=404, detail="Foto harus ada telapak tangan")
         
     if len(results.multi_handedness) > 1:
+      if os.path.exists("temp/"+image_name):
+        os.remove("temp/"+image_name)
       raise HTTPException(status_code=404, detail="Hanya satu telapak tangan yang bisa diramal")
 
     tangan =  results.multi_handedness[0].classification[0].label
     if(tangan == 'Right'):
+      if os.path.exists("temp/"+image_name):
+        os.remove("temp/"+image_name)
       raise HTTPException(status_code=404, detail="Hanya Tangan Kiri Yang Bisa Diramal")
 
     thumb = None
@@ -64,20 +72,160 @@ async def image(image: UploadFile = File(...)):
       pinky = hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_TIP].x
     cek_tapak_tangan = thumb > pinky
     if(cek_tapak_tangan == False):
+      if os.path.exists("temp/"+image_name):
+        os.remove("temp/"+image_name)
       raise HTTPException(status_code=404, detail="Sila foto telapak tangan kiri anda")
 
-    if os.path.exists("temp/"+image_name):
-      os.remove("temp/"+image_name)
+    # if os.path.exists("temp/"+image_name):
+    #   os.remove("temp/"+image_name)
 
     return {"message": "lakukan ramalan"}  
 
 
-@app.post("/ramalan")
-async def image( request: Request):
+# @app.post("/ramalan")
+# async def image( request: Request):
 
-    body = await request.form()
-    if body:
-      print(body['image'].file)
-      return {"filename": "image.filename"}
-    else :
-      raise HTTPException(status_code=404, detail="error")
+#     body = await request.form()
+#     if body:
+#       print(body['image'].content_type)
+#       return {"filename": "image.filename"}
+#     else :
+#       raise HTTPException(status_code=404, detail="error")
+
+def makeCircle(img,circle_y,circle_x,radius):
+    image_height, image_width, _ = img.shape
+    y = int(circle_y * image_height)
+    x = int(circle_x * image_width)
+    circle_coordinates = (x,y)
+    color = (255, 0, 0)
+    thickness = 2
+    return cv2.circle(img,circle_coordinates,radius,color,thickness)
+
+@app.post("/ramalan")
+async def ramalan(request: Request):
+  body = await request.form()
+  if(body == False) :
+    raise HTTPException(status_code=404, detail="error")
+  
+  if(body['image'] == None and body['image'] == '' ):
+    raise HTTPException(status_code=404, detail="error")
+
+  # print(body['image'])
+  image_file = body['image'] #ini file yang akan digunakan
+  image_src = "temp/"+body['image'] #ini file yang akan digunakan
+  size = len(image_file)
+  image_name = image_file[:size - 4]
+
+  change_background_mp = mp.solutions.selfie_segmentation #untuk hapus background
+  change_bg_segment = change_background_mp.SelfieSegmentation() #untuk hapus background
+
+  with mp_hands.Hands(
+    static_image_mode=True,
+    max_num_hands=1,
+    min_detection_confidence=0.5) as hands: 
+    
+    image = cv2.flip(cv2.imread(image_src), 1)
+    sample_img = cv2.flip(cv2.imread(image_src), 1)
+
+
+    image = cv2.cvtColor(sample_img, cv2.COLOR_BGR2RGB)
+    image = change_bg_segment.process(image)
+    image = image.segmentation_mask > 0.9
+    image = np.dstack((image,image,image))
+    image = np.where(image, sample_img, 255) 
+    
+    shape = image.shape
+    
+    results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    image_height, image_width, _ = image.shape
+    
+    for hand_landmarks in results.multi_hand_landmarks:
+      palm_center_y = (hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_CMC].y +
+      hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_MCP].y)/2.1
+      palm_center_x = (hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_CMC].x +
+      hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_MCP].x)/2.1
+      myradius = int(image_height/4.9) 
+      y = int(palm_center_y * image_height)
+      x = int(palm_center_x * image_width)
+      circle_coordinates = (x,y)
+      mask = np.zeros(image.shape, dtype=np.uint8)
+      cv2.circle(mask, circle_coordinates, myradius, (255,255,255), -1)
+      ROI = cv2.bitwise_and(image, mask)
+      mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+      x,y,w,h = cv2.boundingRect(mask)
+      result = ROI[y:y+h,x:x+w]
+      mask = mask[y:y+h,x:x+w]
+      result[mask==0] = (255,255,255)
+      cv2.imwrite("temp/"+image_name+"cropped.png", result)
+      cv2.waitKey(0)
+
+  image = cv2.imread("temp/"+image_name+"cropped.png")
+  width = 450
+  height = 450
+  dim = (width, height)
+  image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
+  # cv2.imshow("palm",image) #to view the palm in python
+  # cv2.waitKey(0)
+  gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+  edges = cv2.Canny(gray,30,80,apertureSize = 3)
+  # cv2.imshow("edges in palm",edges)
+  # cv2.waitKey(0)
+  edges = cv2.bitwise_not(edges)
+  # cv2.imshow("edges in palm1",edges)
+  cv2.imwrite("temp/"+image_name+"lines.png", edges)
+  cv2.waitKey(0)
+
+  TARGET_FILE = "temp/"+image_name+"lines.png"
+  IMG_DIR = os.path.abspath(os.path.dirname(__file__)) + '/images/'
+  IMG_SIZE = (200, 200)
+  
+  target_img = cv2.imread(TARGET_FILE)
+  target_img = cv2.resize(target_img, IMG_SIZE)
+
+  print('TARGET_FILE: %s' % (TARGET_FILE))
+
+  bf = cv2.BFMatcher(cv2.NORM_HAMMING)
+  detector = cv2.AKAZE_create()
+  (target_kp, target_des) = detector.detectAndCompute(target_img, None)
+
+  hasil_ramalan = None
+
+  datas = None
+
+  with open('dataset.json') as f:
+    datas = json.load(f)
+
+  pilihan = 2000
+  image_ramalan = None
+
+  files = os.listdir(IMG_DIR)
+  for file in files:
+
+    comparing_img_path = IMG_DIR + file
+    try:
+        comparing_img = cv2.imread(comparing_img_path, cv2.IMREAD_GRAYSCALE)
+        comparing_img = cv2.resize(comparing_img, IMG_SIZE)
+        (comparing_kp, comparing_des) = detector.detectAndCompute(comparing_img, None)
+        matches = bf.match(target_des, comparing_des)
+        dist = [m.distance for m in matches]
+        ret = sum(dist) / len(dist)
+    except cv2.error:
+        ret = 100000
+
+    print(file, ret)
+    if(ret < pilihan):
+        pilihan =ret
+        image_ramalan = file
+  theindex = None
+  for index,data in enumerate(datas):
+    if(data["id"] == image_ramalan):
+      theindex= index
+          
+  hasil_ramalan = datas[theindex]['datanya']
+  # if os.path.exists("temp/"):
+  #     os.remove("temp/"+image_name+"lines.png") 
+  #     os.remove("temp/"+image_name+"cropped.png") 
+  #     os.remove("temp/"+body['image']) 
+
+  return {"message": hasil_ramalan}
+