test

utils.py

@@ -52,44 +52,42 @@ for i in range(0,len(l)):
 
 
 def calc_filtered_img(img):
-    residual_img= np.zeros(img.shape)
+    # residual_img= np.zeros(img.shape)
     # residual_img = np.array(img);
-    # fil = np.array([[-1,3,-3,1]])
-    # residual_img = cv2.filter2D(residual_img, -1, fil)
-    for i in range(img.shape[0]):
-        for j in range(img.shape[1]):
-            residual_img[i, j] = - 3*img[i, j];
-            if(j>0):
-                residual_img[i, j] += img[i, j-1]
-            if(j+1<img.shape[1]):
-                residual_img[i, j] += 3*img[i, j+1]
-            if(j+2<img.shape[1]):
-                residual_img[i,j]-= img[i, j+2]
-    
+    fil = np.array([[-1,3,-3,1]])
+    residual_img = cv2.filter2D(img, -1, fil)
+    # for i in range(img.shape[0]):
+    #     for j in range(img.shape[1]):
+    #         residual_img[i, j] = - 3*img[i, j];
+    #         if(j>0):
+    #             residual_img[i, j] += img[i, j-1]
+    #         if(j+1<img.shape[1]):
+    #             residual_img[i, j] += 3*img[i, j+1]
+    #         if(j+2<img.shape[1]):
+    #             residual_img[i,j]-= img[i, j+2]
+    # residual_img = np.convolve(img,[1,-3,3,-1],mode='same')
     return residual_img
 
 def calc_q_t_img(img, q, t):
-    qt_img = np.zeros(img.shape)
-    dct = {}
-    for i in range(img.shape[0]):
-        for j in range(img.shape[1]):
-            val = np.minimum(t, np.maximum(-t, np.round(img[i, j]/q)))
-            dct[val] = dct.get(val,0)+1
-            qt_img[i, j] = val
+    # qt_img = np.zeros(img.shape)
+    # for i in range(img.shape[0]):
+    #     for j in range(img.shape[1]):
+            # val = np.minimum(t, np.maximum(-t, np.round(img[i, j]/q)))
+    #         qt_img[i, j] = val
     # print(dct)
+    qt_img = np.minimum(t, np.maximum(-t, np.round(img/q)))
     return qt_img
 
 def process_frame(frame_and_index):
     frame, index = frame_and_index
     # processing logic for a single frame
-    # logging.info(f"Processing frame {index}")
+    logging.info(f"Processing frame {index}")
     filtered_image = calc_filtered_img(frame)
     output_image = calc_q_t_img(filtered_image, q, t)
     output_image=output_image+2
     # plt.imshow(output_image)
     return output_image.astype(np.uint8)
     
-
 # Center the filtered image at zero by adding 128
 q = 3
 t = 2