Rename test.py to app.py

test.py → app.py

@@ -1,82 +1,86 @@
-import os
-from ast import literal_eval
-
-from segment_key import *
-from matplotlib import pyplot as plt
-
-
-def show_kps(contour):
-    list1 = range(0, 310)
-    list2 = list(zip(get_features_right(contour), list1))
-
-    x_coords = [point[0] for point in list2]
-    y_coords = [point[1] for point in list2]
-    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')
-
-    list2 = list(zip(get_features_left(contour), list1))
-    x_coords = [point[0] for point in list2]
-    y_coords = [point[1] for point in list2]
-    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')
-
-    list2 = list(zip(list1, get_features_up(contour)))
-    x_coords = [point[0] for point in list2]
-    y_coords = [point[1] for point in list2]
-    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')
-
-    list2 = list(zip(list1, get_features_down(contour)))
-    x_coords = [point[0] for point in list2]
-    y_coords = [point[1] for point in list2]
-    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')
-
-    plt.show()
-
-
-def get_all_features():
-    contours = []
-    with open('prediction/database.txt', 'r') as file:
-        lines = file.readlines()
-        for line in lines:
-            results = (line.split(';')[1])
-            results = results.replace(",,", ",'',")
-            results = literal_eval(results)
-            results = np.array(results)
-            contours.append((line.split(';')[0], results))
-
-    return contours
-
-
-def cos_similarity(feature1, feature2):
-    return np.dot(feature1, feature2) / (np.linalg.norm(feature1) * np.linalg.norm(feature2))
-
-
-def predict_match(image_path):
-    main_name = os.path.basename(image_path)[:-11] + '.jpg'
-    main_feature = final_features(image_path)
-    contours = get_all_features()
-
-    l = []
-
-    for image in contours:
-        feature = image[1]
-        feature_similarity = 1 - cos_similarity(feature, main_feature)
-
-        l.append([image[0], feature_similarity])
-
-    l.sort(key=lambda x: x[1])
-
-    print(l)
-    print(l[0])
-    index_in_list = -1
-    for i in range(len(l)):
-        if l[i][0] == main_name:
-            index_in_list = i
-    return l[0][0], l[0][1], index_in_list
-
-
-# for image_file in os.listdir('../augmentation/testing/3'):
-#     image_path = os.path.join('../augmentation/testing/3', image_file)
-#     best_image_name, best_match_value, index = predict_match(image_path)
-#     with open('performance/performance_results.txt', 'a+') as file:
-#         file.write(image_file + ';' + best_image_name + ';' + str(best_match_value) + ';' + str(index) + '\n')
-
-predict_match('./examples/img.jpg')
+import os
+from ast import literal_eval
+
+from segment_key import *
+from matplotlib import pyplot as plt
+
+
+def show_kps(contour):
+    list1 = range(0, 310)
+    list2 = list(zip(get_features_right(contour), list1))
+
+    x_coords = [point[0] for point in list2]
+    y_coords = [point[1] for point in list2]
+    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')
+
+    list2 = list(zip(get_features_left(contour), list1))
+    x_coords = [point[0] for point in list2]
+    y_coords = [point[1] for point in list2]
+    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')
+
+    list2 = list(zip(list1, get_features_up(contour)))
+    x_coords = [point[0] for point in list2]
+    y_coords = [point[1] for point in list2]
+    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')
+
+    list2 = list(zip(list1, get_features_down(contour)))
+    x_coords = [point[0] for point in list2]
+    y_coords = [point[1] for point in list2]
+    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')
+
+    plt.show()
+
+
+def get_all_features():
+    contours = []
+    with open('prediction/database.txt', 'r') as file:
+        lines = file.readlines()
+        for line in lines:
+            results = (line.split(';')[1])
+            results = results.replace(",,", ",'',")
+            results = literal_eval(results)
+            results = np.array(results)
+            contours.append((line.split(';')[0], results))
+
+    return contours
+
+
+def cos_similarity(feature1, feature2):
+    return np.dot(feature1, feature2) / (np.linalg.norm(feature1) * np.linalg.norm(feature2))
+
+
+def predict_match(image_path):
+    main_name = os.path.basename(image_path)[:-11] + '.jpg'
+    main_feature = final_features(image_path)
+    contours = get_all_features()
+
+    l = []
+
+    for image in contours:
+        feature = image[1]
+        feature_similarity = 1 - cos_similarity(feature, main_feature)
+
+        l.append([image[0], feature_similarity])
+
+    l.sort(key=lambda x: x[1])
+
+    print(l)
+    print(l[0])
+    index_in_list = -1
+    for i in range(len(l)):
+        if l[i][0] == main_name:
+            index_in_list = i
+    return l[0][0]
+
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_match,
+    inputs=gr.Image(type='numpy'),
+    outputs=["text"],
+    title="YOLOv8 Object Detection",
+    description="Upload an image to detect objects using the YOLOv8 model.",
+)
+
+# Launch the interface
+iface.launch()