Upload app.py

app.py

@@ -4,46 +4,14 @@ import gradio as gr
 
 
 def my_app(img):
-    # Opening image
-    # img = cv2.imread("image.jpg")
-
-# OpenCV opens images as BRG
-# but we want it as RGB We'll
-# also need a grayscale version
-    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-
-# Use minSize because for not
-# bothering with extra-small
-# dots that would look like STOP signs
-    stop_data = cv2.CascadeClassifier('stop_data.xml')
-
-    found = stop_data.detectMultiScale(img_gray,
-                                       minSize=(20, 20))
-
-# Don't do anything if there's
-# no sign
-    amount_found = len(found)
-
-    if amount_found != 0:
-            return ("S T O P!")
-        # There may be more than one
-        # sign in the image
-        # for (x, y, width, height) in found:
-
-        #     # We draw a green rectangle around
-        #     # every recognized sign
-        #     cv2.rectangle(img_rgb, (x, y),
-        #                   (x + height, y + width),
-        #                   (0, 255, 0), 5)
-
-# Creates the environment of
-# the picture and shows it
-    plt.subplot(1, 1, 1)
-    plt.imshow(img_rgb)
-    plt.show()
-    return ("S T O P!")
+    grayscale_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    classifier = cv2.CascadeClassifier('classifier.xml')
+    analysedData = classifier.detectMultiScale(grayscale_image,
+                                               minSize=(20, 20))
+    if len(analysedData) != 0:
+        return ("S T O P!")
+    else:
+        return ("You're good to go :)")
 
 
 gr.interface.Interface(fn=my_app, live=True, inputs=gr.Image(