Update app.py

app.py

@@ -17,7 +17,30 @@ recog_interpreter.allocate_tensors()
 input_details = interpreter.get_input_details()
 output_details = interpreter.get_output_details()
 
-    
+
+def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0):
+    """Return a sharpened version of the image, using an unsharp mask."""
+    blurred = cv2.GaussianBlur(image, kernel_size, sigma)
+    sharpened = float(amount + 1) * image - float(amount) * blurred
+    sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
+    sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
+    sharpened = sharpened.round().astype(np.uint8)
+    if threshold > 0:
+        low_contrast_mask = np.absolute(image - blurred) < threshold
+        np.copyto(sharpened, image, where=low_contrast_mask)
+    return sharpened
+
+def increase_brightness(img, value):
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    h, s, v = cv2.split(hsv)
+
+    lim = 255 - value
+    v[v > lim] = 255
+    v[v <= lim] += value
+
+    final_hsv = cv2.merge((h, s, v))
+    img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
+    return img
 
 def execute_text_recognition_tflite( boxes, frame, interpreter, input_details, output_details):
     x1, x2, y1, y2 = boxes[1], boxes[3], boxes[0], boxes[2]
@@ -56,6 +79,9 @@ def execute_text_recognition_tflite( boxes, frame, interpreter, input_details, o
     return text,cv2.resize(save_frame,(94,24))
 
 def greet(image):
+    sharpened = unsharp_mask(image)
+    image = increase_brightness(sharpened, value=10) # 60 ->5qoOk.png #10 -> if8nC.png
+
     resized = cv2.resize(image, (320,320), interpolation=cv2.INTER_LINEAR)
     input_data = resized.astype(np.float32)          # Set as 3D RGB float array
     input_data /= 255.                               # Normalize