Rework

best_fit.py

@@ -1,40 +1,63 @@
 import cv2
-import matplotlib.pyplot as plt
+# import matplotlib.pyplot as plt
 import numpy as np
+from sklearn.cluster import KMeans
+
 
 def fit(img, templates, start_percent, stop_percent, threshold):
     img_width, img_height = img.shape[::-1]
     best_location_count = -1
     best_locations = []
     best_scale = 1
 
-    plt.axis([0, 2, 0, 1])
-    plt.show(block=False)
-
-    x = []
-    y = []
+    # plt.axis([0, 2, 0, 1])
+    # plt.show(block=False)
+    #
+    # x = []
+    # y = []
     for scale in [i/100.0 for i in range(start_percent, stop_percent + 1, 3)]:
         locations = []
         location_count = 0
         for template in templates:
             template = cv2.resize(template, None,
                 fx = scale, fy = scale, interpolation = cv2.INTER_CUBIC)
             result = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
-            result = np.where(result >= threshold)
+
+            scores = result[result >= threshold].reshape(-1, 1)  # all relevant scores
+
+            if len(scores) < 2:
+                continue
+            kmeans = KMeans(n_clusters=2, random_state=0).fit(scores)
+
+            # Get cluster with higher mean (strong matches)
+            cluster_labels = kmeans.labels_
+            means = kmeans.cluster_centers_
+
+            strong_cluster = np.argmax(means)
+            adaptive_threshold = np.mean(scores[cluster_labels == strong_cluster])
+            result = np.where(result >= adaptive_threshold)
+            # import matplotlib.pyplot as plt
+
+            # plt.hist(scores.flatten(), bins=50, color='blue', alpha=0.7)
+            # plt.axvline(np.mean(scores[cluster_labels == strong_cluster]), color='red', label='Adaptive Threshold')
+            # plt.legend()
+            # plt.title("MatchTemplate Score Distribution")
+            # plt.xlabel("Score")
+            # plt.ylabel("Frequency")
+            # plt.show()
             location_count += len(result[0])
             locations += [result]
-        print("scale: {0}, hits: {1}".format(scale, location_count))
-        x.append(location_count)
-        y.append(scale)
-        plt.plot(y, x)
-        plt.pause(0.00001)
+        # print("scale: {0}, hits: {1}".format(scale, location_count))
+        # x.append(location_count)
+        # y.append(scale)
+        # plt.plot(y, x)
+        # plt.pause(0.00001)
         if (location_count > best_location_count):
             best_location_count = location_count
             best_locations = locations
             best_scale = scale
-            plt.axis([0, 2, 0, best_location_count])
+            # plt.axis([0, 2, 0, best_location_count])
         elif (location_count < best_location_count):
             pass
-    plt.close()
-
+    # plt.close()
     return best_locations, best_scale