Final version

Author: Arshad221b

opencv.py

@@ -1,10 +1,34 @@
 import cv2
-from matplotlib import pyplot as plt
-import numpy as np 
+import numpy as np
 from keras.models import load_model
+from skimage.transform import resize, pyramid_reduce
 
 model = load_model('model.h5')
 
+def get_square(image, square_size):
+    
+    height, width = image.shape    
+    if(height > width):
+      differ = height
+    else:
+      differ = width
+    differ += 4
+
+
+    mask = np.zeros((differ, differ), dtype = "uint8")
+
+    x_pos = int((differ - width) / 2)
+    y_pos = int((differ - height) / 2)
+
+   
+    mask[y_pos: y_pos + height, x_pos: x_pos + width] = image[0: height, 0: width]
+
+ 
+    if differ / square_size > 1:
+      mask = pyramid_reduce(mask, differ / square_size)
+    else:
+      mask = cv2.resize(mask, (square_size, square_size), interpolation = cv2.INTER_AREA)
+    return mask
 
 
 def keras_predict(model, image):
@@ -14,66 +38,51 @@ def keras_predict(model, image):
     pred_class = list(pred_probab).index(max(pred_probab))
     return max(pred_probab), pred_class
 
-
-
 def keras_process_image(img):
+    
     image_x = 28
     image_y = 28
-    img = cv2.resize(img, (image_x, image_y))
-    img = np.array(img, dtype=np.float32)
-    img = np.reshape(img, (-1, image_x, image_y, 1))
-    return img
-
-
-
-
-
-
-
-def sketch_transform(image):
-    image_grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    image_grayscale_blurred = cv2.GaussianBlur(image_grayscale, (15,15), 0)
+    #img = cv2.resize(img, (28,28), interpolation = cv2.INTER_AREA)
+    img = get_square(img, 28)
+    img = np.reshape(img, (image_x, image_y))
 
 
-    return image_grayscale_blurred
-
-
-
-
-cam_capture = cv2.VideoCapture(0)
-
-upper_left = (300, 300)
-bottom_right = (1084, 1084)
-
+    return img
+ 
 
+def crop_image(image, x, y, width, height):
+    return image[y:y + height, x:x + width]
 
-while True:
+while True:  
     cam_capture = cv2.VideoCapture(0)
+    _, image_frame = cam_capture.read()  
+    # Select ROI
+    im2 = crop_image(image_frame, 300,300,300,300)
+    image_grayscale = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
+    image_grayscale_blurred = cv2.GaussianBlur(image_grayscale, (15,15), 0)
 
-    upper_left = (300, 300)
-    bottom_right = (1084, 1084)
 
-    _, image_frame = cam_capture.read()
-    
-    #Rectangle marker
-    r = cv2.rectangle(image_frame, upper_left, bottom_right, (100, 50, 200), 5)
-    rect_img = image_frame[upper_left[1] : bottom_right[1], upper_left[0] : bottom_right[0]]
-    
-    sketcher_rect = rect_img
-    sketcher_rect = sketch_transform(sketcher_rect)
-    
-    
-    
-    cv2.resize(sketcher_rect, (28,28), interpolation = cv2.INTER_AREA)
-    pred_probab, pred_class = keras_predict(model, sketcher_rect)
-    print(pred_class, pred_probab)
-    cv2.imshow('image_frame',sketcher_rect)
+    #resized_img = image_resize(image_grayscale_blurred, width = 28, height = 28, inter = cv2.INTER_AREA) 
+    #resized_img = keras_process_image(image_grayscale_blurred)
+    resized_img = cv2.resize(image_grayscale_blurred,(28,28))
+    #ar = np.array(resized_img)
+    ar = resized_img.reshape(1,784)
 
+    pred_probab, pred_class = keras_predict(model, ar )
+    print(pred_class, pred_probab)
+     
 
+ 
+    # Display cropped image
+
+    cv2.imshow("Image2",im2)
+    cv2.imshow("Image4",resized_img)
+    cv2.imshow("Image3",image_grayscale_blurred)
 
     if cv2.waitKey(25) & 0xFF == ord('q'):
             cv2.destroyAllWindows()
             break
-        
+    
+
 cam_capture.release()
-cv2.destroyAllWindows()
+cv2.destroyAllWindows()