Update app.py

app.py

@@ -1,7 +1,8 @@
 import cv2
+from matplotlib.pyplot import hsv
+from PIL import ImageOps
 import numpy as np
 import gradio as gr
-from PIL import ImageOps
 import warnings
 
 # Filter out user warnings to avoid any unnecessary clutter in the output
@@ -9,8 +10,11 @@ warnings.filterwarnings("ignore", category=UserWarning)
 
 font = cv2.FONT_HERSHEY_SIMPLEX
 font_scale = 1
+color_search = np.zeros((200, 200, 3), np.uint8)
+color_selected = np.zeros((200, 200, 3), np.uint8)
+hue = 0
 
-def search_contours(mask, frame):
+def search_contours(mask, frame, source):
     contours_count = 0
     contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     pip_count = 0
@@ -30,6 +34,18 @@ def search_contours(mask, frame):
             cv2.putText(frame, str(pip_count), (cX - 16, cY + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
     return pip_count, frame
 
+def center_crop_with_padding(image):
+    im = Image.fromarray(image)
+    width, height = im.size
+    new_size = min(width, height)
+    im = im.crop(((width - new_size) // 2, (height - new_size) // 2, (width + new_size) // 2, (height + new_size) // 2))
+    im = im.crop((25, 25, new_size - 25, new_size - 25))
+    im_with_border = ImageOps.expand(im, border=3, fill=(255,255,255))
+    return np.array(im_with_border)
+
+sample_images = ["23.jpg", "28.png", "35.jpg", "36w.jpg" "46.jpg", "64.jpg"]
+
+
 sample_images = {
     "23.jpg": "23.jpg",
     "28.png": "28.png",
@@ -43,12 +59,8 @@ def detect_pips(uploaded_image, sample_image_selection, hue_threshold):
     if uploaded_image is not None:
         image = uploaded_image[:, :, :3]
     else:
-        image = cv2.imread(sample_images[sample_image_selection])
-
-    # If only sample_image_selection is provided, return the sample image without processing
-    if uploaded_image is None and hue_threshold == 250:
-        return image, image
-
+        image = cv2.imread(sample_image_selection)
+    
     image = cv2.resize(image, (512, 512))
     hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
     hue = 60
@@ -56,22 +68,18 @@ def detect_pips(uploaded_image, sample_image_selection, hue_threshold):
     lower_hsv = np.array([lower_hue, 50, 20])
     upper_hsv = np.array([upper_hue, 255, 255])
     mask = cv2.inRange(hsv, lower_hsv, upper_hsv)
-    count, result_frame = search_contours(mask, image)
+    count, result_frame = search_contours(mask, image, source="image")
     cv2.putText(result_frame, f'Total pip count is: {count}', (10, result_frame.shape[0] - 30), font, 1, (0, 255, 255), 2, cv2.LINE_AA)
-    
-    return result_frame, image
+    return result_frame
 
 iface = gr.Interface(
     detect_pips,
     inputs=[
-        gr.inputs.Image(type="numpy", label="Upload Image", source="upload", optional=True),  # Image upload
-        gr.inputs.Dropdown(choices=list(sample_images.keys()), label="Or Select a Sample Image", default=None),  # Dropdown for sample images
+        gr.inputs.Image(type="numpy", label="Upload Image", source="upload", optional=True), # Image upload
+        gr.inputs.Dropdown(choices=list(sample_images.keys()), label="Or Select a Sample Image", default=None), # Dropdown for sample images
         gr.inputs.Slider(label="Hue Threshold", minimum=0, maximum=500, step=1, default=250)
     ],
-    outputs=[
-        gr.outputs.Image(type="numpy", label="Processed Result"),  # Processed image output
-        gr.outputs.Image(type="numpy", label="Original/Selected Image")  # Original or selected image output
-    ],
+    outputs=gr.outputs.Image(type="numpy", label="Result"),
     title='<b><font size="4">🁫Image Processing demonstration using OpenCV and Python:🁫</b>',
     description='<div style="text-align:center;"><b style="font-size:20px;">App to help you keep score when playing Dominos</b><br>Use your iPad to take a photo and upload to calculate a score<br>Note: lighting and camera angles are key factors :)</div>',
     allow_flagging=False,
@@ -79,5 +87,6 @@ iface = gr.Interface(
     theme="dark",
 )
 
-iface.launch()
-
+# Launch the Gradio interface if the script is run as the main program
+if __name__ == '__main__':
+    iface.launch()