Merge branch 'b_dev_saswat' into 'master'

B dev saswat

See merge request mlproject2022/sign-language-classifier!3

live.py

+import numpy as np
+import cv2
+import mediapipe as mp
+import pickle
+import matplotlib.pyplot as plt
+import time
+
+load_model = pickle.load(open("signModel.hd5","rb"))
+
+mphands = mp.solutions.hands
+hands = mphands.Hands(static_image_mode=False,
+                      max_num_hands=1,
+                      min_detection_confidence=0.5,
+                      min_tracking_confidence=0.5)
+mp_drawing = mp.solutions.drawing_utils
+cap = cv2.VideoCapture(0)
+
+_, frame = cap.read()
+
+stop_time = 0.2
+h, w, c = frame.shape
+prev_time = time.time()
+last_pred = ""
+np.set_printoptions(linewidth=np.inf)
+
+def show_frame(frame):
+    cv2.imshow("Frame", frame)
+
+while True:
+    x_max = 0
+    y_max = 0
+    x_min = w
+    y_min = h
+    curr_time = time.time()
+    _, frame = cap.read()
+    framergb = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+    result = hands.process(framergb)
+    hand_landmarks = result.multi_hand_landmarks
+    
+    k = cv2.waitKey(1)
+    if k%256 == 27:
+        # ESC pressed
+        print("Escape hit, closing...")
+        break
+    if hand_landmarks:
+        for handLMs in hand_landmarks:
+            for lm in handLMs.landmark:
+                x, y = int(lm.x * w), int(lm.y * h)
+                if x > x_max:
+                    x_max = x
+                if x < x_min:
+                    x_min = x
+                if y > y_max:
+                    y_max = y
+                if y < y_min:
+                    y_min = y
+            hand_w = x_max - x_min
+            hand_h = y_max - y_min
+            if hand_w > hand_h:
+                diff = hand_w-hand_h
+                y_min -= int(diff/2)
+                y_max += int(diff/2)
+            else:
+                diff = hand_h-hand_w
+                x_min -= int(diff/2)
+                x_max += int(diff/2)
+            cv2.rectangle(frame, (x_min-20, y_min-20), (x_max+20, y_max+20), (255, 0, 0), 2)#             mp_drawing.draw_landmarks(frame, handLMs, mphands.HAND_CONNECTIONS)
+        curr_time = time.time()
+        if curr_time - stop_time > prev_time:
+            prev_time = curr_time
+            crop_img = framergb[y_min-20: y_max+20, x_min-20: x_max+20]
+            #crop_img = framergb[y_min: y_max, x_min: x_max]
+    #         if(len(crop_img)):
+    #             (row, col) = crop_img.shape[0:2]
+    #             # Take the average of pixel values of the BGR Channels
+    #             # to convert the colored image to grayscale image
+    #             for i in range(row):
+    #                 for j in range(col):
+    #                     # Find the average of the BGR pixel values
+    #                     crop_img[i, j] = sum(crop_img[i, j]) * 0.33
+    #             crop_img = crop_img.flatten()
+            try:
+                crop_img = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY) 
+                crop_img = cv2.GaussianBlur(crop_img, (7, 7), 0)
+                crop_img = cv2.resize(crop_img, (28, 28))
+                #cv2.imwrite('crop_image0.jpg', crop_img)
+        #         print(crop_img.shape)
+                crop_img = crop_img.reshape(1,28,28,1)
+                #print(crop_img.shape)
+                crop_img = np.array(crop_img, dtype='float')
+                crop_img /= 255
+                y = load_model.predict(crop_img, verbose=0)
+                pred = np.argmax(y)
+                if(pred >= 9):
+                    pred += 1
+                last_pred = chr(pred+ord("A"))
+                #print(crop_img.round(1).squeeze())
+                #time.sleep(1)
+                #plt.imshow(crop_img[0])
+                #plt.show()
+            except:
+                pass
+    print("Detected: ", last_pred)
+    cv2.putText(frame, last_pred, (x_min-5,y_min-25), cv2.FONT_HERSHEY_PLAIN, 4, (255, 0, 0), 3)
+    cv2.imshow("Frame", frame)
+    
+cap.release()
+cv2.destroyAllWindows()
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utils.py

+import pandas as pd
+import numpy as np
+from sklearn import metrics 
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def save_accuracy_plot(model_history, model_name):
+    plt.figure(figsize = (18,8))
+    plt.plot(model_history.history['accuracy'])
+    plt.plot(model_history.history['val_accuracy'])
+    plt.title("Training & Validation Accuracy")
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.legend(['Training Accuracy','Testing Accuracy'])
+    plt.savefig("./results/"+model_name+"_accuracy.png")
+    plt.show()
+
+def save_loss_plot(model_history, model_name):
+    plt.figure(figsize = (18,8))
+    plt.plot(model_history.history['loss'])
+    plt.plot(model_history.history['val_loss'])
+    plt.title("Training & Validation Loss")
+    plt.xlabel('Epochs')
+    plt.ylabel('Loss')
+    plt.legend(['Train loss','Testing loss'])
+    plt.savefig("./results/"+model_name+"_loss.png")
+    plt.show()
+
+def save_model_history(model_history, model_name):
+    save_accuracy_plot(model_history, model_name)
+    save_loss_plot(model_history, model_name)
+
+
+def save_results(y_test, y_pred, model_name="model"):
+    with open('./results/svm_scores.txt', 'w') as f:
+        print(metrics.classification_report(y_test, y_pred), file = f)
+        print(metrics.classification_report(y_test, y_pred))
+    # Confusion matrix
+    #plt.figure(figsize=(10,10))
+    cmn=metrics.confusion_matrix(y_test,y_pred)
+    #cmn = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+    #fig, ax = plt.subplots(figsize=(15,15))
+    #cmn = np.array(cmn, dtype='int')
+    plt.figure(figsize=(15,15))
+    sns.heatmap(cmn, annot=True,cmap='Blues', fmt='g')
+    plt.title("Confusion Matrix")
+    plt.ylabel('Actual')
+    plt.xlabel('Predicted')
+    plt.savefig("./results/"+model_name+"_confusion.png")
+    plt.show(block=False)
+
+    
+def load_dataset():
+    # load dataset
+    train_df = pd.read_csv("data/sign_mnist_train.csv")
+    test_df = pd.read_csv("data/sign_mnist_test.csv")
+        
+    train_arr = train_df.iloc[:, 1:].to_numpy()
+    train_label_arr = train_df['label'].to_numpy()
+    test_arr = test_df.iloc[:, 1:].to_numpy()
+    test_label_arr = test_df['label'].to_numpy()
+
+    X_train=train_arr/255
+    X_test=test_arr/255
+
+    y_train = train_label_arr
+    y_test  = test_label_arr
+    return X_train, y_train, X_test, y_test
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