uploaded site and api data

2023-05-14 11:56:01 +03:00
parent db657aac76
commit bb7265d6b4
28 changed files with 3673 additions and 0 deletions
@@ -0,0 +1,50 @@
+import os
+import mediapipe as mp
+import cv2
+import pickle
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+import numpy as np
+
+mp_hands = mp.solutions.hands
+hands = mp_hands.Hands(static_image_mode=True, min_detection_confidence=0.5)
+data_dir = "./data"
+data = []
+labels = []
+
+for dir_ in os.listdir(data_dir):
+    print(dir_)
+    for img_path in os.listdir(os.path.join(data_dir, dir_)):
+        data_aux = []
+        img = cv2.imread(os.path.join(data_dir, dir_, img_path))
+
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        results = hands.process(img_rgb)
+        if results.multi_hand_landmarks:
+            for hand_landmarks in results.multi_hand_landmarks:
+                for i in range(len(hand_landmarks.landmark)):
+                    x = hand_landmarks.landmark[i].x
+                    y = hand_landmarks.landmark[i].y
+                    data_aux.append(x)
+                    data_aux.append(y)
+                    # print(f"Poza {img_path} cu ", end=' ')
+                    # print(x, y)
+            data.append(data_aux)
+            labels.append(dir_)
+
+
+data = np.asarray(data)
+labels = np.asarray(labels)
+x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, shuffle=True, stratify=labels)
+model = RandomForestClassifier()
+model.fit(x_train, y_train)
+y_predict = model.predict(x_test)
+score = accuracy_score(y_predict, y_test)
+
+print(f"{score * 100}% classified correctly")
+f = open("model.p", "wb")
+pickle.dump({"model": model}, f)
+f.close()
+
+
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@@ -0,0 +1,53 @@
+from fastapi import FastAPI, Request
+import uvicorn
+from starlette.middleware.cors import CORSMiddleware
+import pickle
+import numpy as np
+
+app = FastAPI()
+
+origins = ["http://localhost"]
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model_dict = pickle.load(open("./model.p", "rb"))
+model = model_dict["model"]
+labels_dict = {0: "hello", 1: "i love you", 2: "yes", 3: "good", 4: "bad", 5: "okay", 6: "you", 7: "i/i'm", 8: "why", 9: "no"}
+
+@app.get("/")
+async def read_root():
+    return {"message": "Hello World"}
+
+@app.post("/")
+async def process_json(request: Request):
+    # Get the JSON data from the request
+    data = await request.json()
+    if len(data) == 0:
+        return {"result": "no data"}
+    hands_data = data
+    data_aux = []
+
+    # hand landmarks are taken from data.json
+    if len(hands_data) == 42:
+        hand = hands_data[:21]
+    else:
+        hand = hands_data
+
+    for landmark in hand:
+        x = landmark["x"]
+        y = landmark["y"]
+        data_aux.append(x)
+        data_aux.append(y)
+
+    prediction = model.predict([np.asarray(data_aux)])
+    predicted_sign = labels_dict[int(prediction[0])]
+    print(predicted_sign)
+    return {"result": predicted_sign}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)
@@ -0,0 +1,8 @@
+home = C:\Users\mihne\AppData\Local\Programs\Python\Python310
+implementation = CPython
+version_info = 3.10.7.final.0
+virtualenv = 20.16.7
+include-system-site-packages = false
+base-prefix = C:\Users\mihne\AppData\Local\Programs\Python\Python310
+base-exec-prefix = C:\Users\mihne\AppData\Local\Programs\Python\Python310
+base-executable = C:\Users\mihne\AppData\Local\Programs\Python\Python310\python.exe
@@ -0,0 +1,60 @@
+# DEPENDENCIES ------>
+import json
+import pickle
+import numpy as np
+
+
+model_dict = pickle.load(open("./model.p", "rb"))
+model = model_dict["model"]
+labels_dict = {1: "hello", 2: "Nothing"}
+
+hands_data = json.load(open("./data.json", "r"))
+data_aux = []
+
+# hand landmarks are taken from data.json
+hand = hands_data[0]
+for landmark in hand:
+    x = landmark["x"]
+    y = landmark["y"]
+    z = landmark["z"]
+    data_aux.append(x)
+    data_aux.append(y)
+    data_aux.append(z)
+
+prediction = model.predict([np.asarray(data_aux)])
+print(prediction)
+predicted_sign = labels_dict[int(prediction[0])]
+print(predicted_sign)
+data_aux = []
+
+
+# if results.multi_hand_landmarks:
+#     for hand_landmarks in results.multi_hand_landmarks:
+#         mp_drawing.draw_landmarks(
+#             frame,
+#             hand_landmarks,
+#             mp_hands.HAND_CONNECTIONS,
+#             mp_drawing_styles.get_default_hand_landmarks_style(),
+#             mp_drawing_styles.get_default_hand_connections_style())
+#
+#         for i in range(len(hand_landmarks.landmark)):
+#             x = hand_landmarks.landmark[i].x
+#             y = hand_landmarks.landmark[i].y
+#             z = hand_landmarks.landmark[i].z
+#             data_aux.append(x)
+#             data_aux.append(y)
+#             data_aux.append(z)
+#             x_.append(x)
+#             y_.append(y)
+#         prediction = model.predict([np.asarray(data_aux)])
+#         predicted_sign = labels_dict[int(prediction[0])]
+#         data_aux = []
+#         x_ = []
+#         y_ = []
+#
+# cv2.imshow("Handy", frame)
+# cv2.waitKey(1)
+
+#
+# capture.release()
+# cv2.destroyAllWindows()
@@ -0,0 +1,35 @@
+import os
+import cv2
+
+DATA_DIR = "./data"
+if not os.path.exists(DATA_DIR):
+    os.makedirs(DATA_DIR)
+
+cap = cv2.VideoCapture(0)
+while True:
+    # ask user for word number to collect data for
+    i = int(input("Enter word number (-1 to eit): "))
+    if i == -1:
+        break
+    if os.path.exists(os.path.join(DATA_DIR, str(i))):
+        # find last image number
+        count = len(os.listdir(os.path.join(DATA_DIR, str(i))))
+    else:
+        os.makedirs(os.path.join(DATA_DIR, str(i)))
+        count = 0
+    print("Collecting data")
+    done = False
+    # takes a picture every time the space bar is pressed, ends when q is pressed
+    print("Press space to take a picture, press q to stop")
+    while not done:
+        ret, frame = cap.read()
+        cv2.imshow("Frame", frame)
+        if cv2.waitKey(25) == ord(" "):
+            print("Saving image " + str(count))
+            cv2.imwrite(os.path.join(DATA_DIR, str(i), str(count) + ".jpg"), frame)
+            count += 1
+        if cv2.waitKey(25) == ord("q"):
+            done = True
+            break
+cap.release()
+cv2.destroyAllWindows()