Merge remote-tracking branch 'origin/main' into main

clear.py

@@ -0,0 +1,177 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+plt.style.use('dark_background')
+
+
+
+# # Read Input Image
+
+# In[3]:
+
+
+path, dirs, files = next(os.walk("test1"))
+print(f"{path}안에 {len(files)}개의 파일이 있음")
+
+for file in files:
+    img_ori = cv2.imread(f'test1/{file}')
+
+    height, width, channel = img_ori.shape
+
+    # plt.figure(figsize=(12, 10))
+    # cv2.imshow("img_ori",img_ori)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    # # Convert Image to Grayscale
+
+    # In[4]:
+
+    # hsv = cv2.cvtColor(img_ori, cv2.COLOR_BGR2HSV)
+    # gray = hsv[:,:,2]
+    gray = cv2.cvtColor(img_ori, cv2.COLOR_BGR2GRAY)
+
+    # cv2.imshow("gray",gray)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    # # Maximize Contrast (Optional)
+
+    # In[5]:
+
+    structuringElement = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
+
+    imgTopHat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, structuringElement)
+    imgBlackHat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, structuringElement)
+
+    imgGrayscalePlusTopHat = cv2.add(gray, imgTopHat)
+    gray = cv2.subtract(imgGrayscalePlusTopHat, imgBlackHat)
+
+    # cv2.imshow("gray",gray)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    # # Adaptive Thresholding
+
+    # In[6]:
+
+    img_blurred = cv2.GaussianBlur(gray, ksize=(5, 5), sigmaX=0)
+
+    img_thresh = cv2.adaptiveThreshold(
+        img_blurred,
+        maxValue=255.0,
+        adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+        thresholdType=cv2.THRESH_BINARY_INV,
+        blockSize=19,
+        C=9
+    )
+
+    # cv2.imshow("gray",gray)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    # # Find Contours
+
+    # In[ ]:
+
+    contours, _ = cv2.findContours(
+        img_thresh,
+        cv2.RETR_LIST,
+        method=cv2.CHAIN_APPROX_SIMPLE
+    )
+
+    temp_result = np.zeros((height, width, channel), dtype=np.uint8)
+
+    cv2.drawContours(temp_result, contours=contours, contourIdx=-1, color=(255, 255, 255))
+
+    # cv2.imshow("temp_result",temp_result)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    # # Prepare Data
+
+    # In[10]:
+
+    temp_result = np.zeros((height, width, channel), dtype=np.uint8)
+
+    contours_dict = []
+
+    for contour in contours:
+        x, y, w, h = cv2.boundingRect(contour)
+        cv2.rectangle(temp_result, pt1=(x, y), pt2=(x + w, y + h), color=(255, 255, 255), thickness=2)
+
+        # insert to dict
+        contours_dict.append({
+            'contour': contour,
+            'x': x,
+            'y': y,
+            'w': w,
+            'h': h,
+            'cx': x + (w / 2),
+            'cy': y + (h / 2),
+            'area': w * h
+        })
+
+    # cv2.imshow("temp_result",temp_result)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    MIN_AREA = 10
+    MIN_WIDTH, MIN_HEIGHT = 4, 4
+    MIN_RATIO, MAX_RATIO = 0.3, 5.0
+    # 수치 조정 해야함 <------------------------------------------------------------------------------------------------------------
+
+    possible_contours = []
+    max_contours = 0
+
+    cnt = 0
+    for d in contours_dict:
+        area = d['w'] * d['h']
+        ratio = d['w'] / d['h']
+
+        if area > MIN_AREA and d['w'] > MIN_WIDTH and d['h'] > MIN_HEIGHT and MIN_RATIO < ratio < MAX_RATIO:
+            d['idx'] = cnt
+            cnt += 1
+            possible_contours.append(d)
+            if d['area'] >= max_contours:
+                max_contours = d['area']
+    print(len(possible_contours))
+    print(max_contours)
+    # visualize possible contours
+    dst = []
+
+    for d in possible_contours:
+        #cv2.drawContours(temp_result, d['contour'], -1, (255, 255, 255))
+        if d['h'] >= 40 and  d['w'] >= 40:
+            dst.append(img_ori[d['y']:d['y'] + d['h'], d['x']:d['x'] + d['w']])
+
+    number = 1
+    for img in dst:
+        cv2.imwrite(f"crop_image/{number}_{file}", img)
+        print(f"crop_image/{number}_{file}")
+        number += 1
+
+    for d in possible_contours:
+        cv2.drawContours(temp_result, d['contour'], -1, (255, 255, 255))
+        #cv2.rectangle(img_ori, pt1=(d['x'], d['y']), pt2=(d['x'] + d['w'], d['y'] + d['h']), color=(211, 200, 86),
+        #              thickness=2)
+    for d in possible_contours:
+        if d['area'] != max_contours:
+            cv2.rectangle(img_ori, pt1=(d['x'], d['y']), pt2=(d['x'] + d['w'], d['y'] + d['h']), color=(255, 255, 255),
+                          thickness=-1)
+
+
+    cv2.imshow("img_ori", img_ori)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+