first commit

.gitignore

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+__pycache__/
+env/
+

main.py

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+import cv2
+import numpy as np
+import mediapipe as mp
+import matplotlib.pyplot as plt
+from google.protobuf.json_format import MessageToDict
+
+# First step is to initialize the Hands class an store it in a variable
+mp_hands = mp.solutions.hands
+
+# Now second step is to set the hands function which will hold the landmarks points
+hands = mp_hands.Hands(static_image_mode=True, max_num_hands=2, min_detection_confidence=0.3)
+
+# Last step is to set up the drawing function of hands landmarks on the image
+mp_drawing = mp.solutions.drawing_utils
+
+
+# Reading the sample image on which we will perform the detection
+# sample_img = cv2.imread('coba.jpg')
+sample_img = cv2.imread('coba2.png')
+
+# Here we are specifing the size of the figure i.e. 10 -height; 10- width.
+plt.figure(figsize = [10, 10])
+
+# Here we will display the sample image as the output.
+# plt.title("Sample Image");plt.axis('off');plt.imshow(sample_img[:,:,::-1]);plt.show()
+
+
+results = hands.process(cv2.cvtColor(sample_img, cv2.COLOR_BGR2RGB))
+
+if results.multi_hand_landmarks:
+    
+    for hand_no, hand_landmarks in enumerate(results.multi_hand_landmarks):
+        
+        print(f'HAND NUMBER: {hand_no+1}')
+        print('-----------------------')
+        
+        for i in range(2):
+
+            print(f'{mp_hands.HandLandmark(i).name}:')
+            print(f'{hand_landmarks.landmark[mp_hands.HandLandmark(i).value]}') 
+
+    # Return whether it is Right or Left Hand
+    i = results.multi_handedness
+    label = MessageToDict(i)['classification'][0]['label']
+                             
+    if label == 'Left':
+        print("ini telapak tangan kiri")
+    if label == 'Right':
+        print("ini telapak tangan kanan")
+

main1.py

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+import cv2
+import numpy as np
+# from matplotlib.pyplot import imshow
+# from google.colab.patches import cv2_imshow
+# !wget  https://i.stack.imgur.com/sDQLM.png
+#read image 
+image = cv2.imread( "croped.png")
+
+width = 350
+height = 450
+dim = (width, height)
+
+image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
+
+#convert to gray
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+#performing binary thresholding
+kernel_size = 3
+ret,thresh = cv2.threshold(gray,200,255,cv2.THRESH_BINARY)  
+
+#finding contours 
+cnts = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+cnts = cnts[0] if len(cnts) == 2 else cnts[1]
+
+#drawing Contours
+radius =2
+color = (30,255,50)
+cv2.drawContours(image, cnts, -1,color , radius)
+# cv2.imshow(image) commented as colab don't support cv2.imshow()
+cv2.imshow("",image)
+# cv2.waitKey()
+cv2.waitKey(0)

server.py

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+import os
+import shutil
+import time
+from fastapi import FastAPI, File, UploadFile,HTTPException  ,Request
+# from fastapi.responses import FileResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+import cv2
+import numpy as np
+import mediapipe as mp
+import matplotlib.pyplot as plt
+
+
+# First step is to initialize the Hands class an store it in a variable
+mp_hands = mp.solutions.hands
+
+# Now second step is to set the hands function which will hold the landmarks points
+hands = mp_hands.Hands(static_image_mode=True, max_num_hands=2, min_detection_confidence=0.3)
+
+# Last step is to set up the drawing function of hands landmarks on the image
+mp_drawing = mp.solutions.drawing_utils
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+def read_root():
+    return {"Hello": "World"}
+
+@app.post("/")
+async def image(image: UploadFile = File(...)):
+    content_type = image.content_type
+    if(content_type != "image/jpeg" and content_type != "image/png") :
+      raise HTTPException(status_code=404,  detail="Fail bukan foto")
+    image_name = image.filename
+    with open("temp/"+image_name, "wb") as buffer:
+        shutil.copyfileobj(image.file, buffer)
+    time.sleep(2)
+    sample_img = cv2.imread("temp/"+image_name)
+    sample_img = cv2.flip(sample_img, 1)
+    results = hands.process(cv2.cvtColor(sample_img, cv2.COLOR_BGR2RGB))
+    if not results.multi_hand_landmarks:
+      raise HTTPException(status_code=404, detail="Foto harus ada telapak tangan")
+        
+    if len(results.multi_handedness) > 1:
+      raise HTTPException(status_code=404, detail="Hanya satu telapak tangan yang bisa diramal")
+
+    tangan =  results.multi_handedness[0].classification[0].label
+    if(tangan == 'Right'):
+      raise HTTPException(status_code=404, detail="Hanya Tangan Kiri Yang Bisa Diramal")
+
+    thumb = None
+    pinky = None
+    for hand_no, hand_landmarks in enumerate(results.multi_hand_landmarks):
+      thumb = hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_TIP].x
+      pinky = hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_TIP].x
+    cek_tapak_tangan = thumb > pinky
+    if(cek_tapak_tangan == False):
+      raise HTTPException(status_code=404, detail="Sila foto telapak tangan kiri anda")
+
+    if os.path.exists("temp/"+image_name):
+      os.remove("temp/"+image_name)
+
+    return {"message": "lakukan ramalan"}  
+
+
+@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")