feat: fix pre_approx location & same point when moving
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156
test.py
156
test.py
@ -1,4 +1,4 @@
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import numpy as np
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import cv2
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# 設定連接到 Android 手機的相機
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@ -6,48 +6,138 @@ cap = cv2.VideoCapture(0) # 0 表示第一個相機（通常是後置相機）
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920) # 設定寬度
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080) # 設定高度
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object_points = np.array([[0, 0, 0], [26, 0, 0], [0, 18.5, 0], [26, 18.5, 0]], dtype=np.float32)
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while True:
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ret, frame = cap.read() # 讀取影片幀
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def get_new_approx(approx, previous_approx):
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def distance(point, pre_point):
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point = point[0]
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pre_point = pre_point[0]
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d = ((point[0]-pre_point[0]) ** 2) + ((point[1]-pre_point[1]) ** 2)
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if not ret:
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break
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return np.sqrt(d)
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# 預處理影像（例如：轉為灰度）
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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new_approx = []
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for pre_point in previous_approx:
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min_d, min_index = 1e9, -100
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for index, point in enumerate(approx):
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d = distance(point, pre_point)
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if d < min_d and d<=500:
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min_d = d
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min_index = index
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if min_index != -100:
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np.delete(approx, min_index)
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new_approx.append(approx[min_index])
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# 偵測邊緣
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edges = cv2.Canny(gray, 100, 200)
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if len(new_approx) == 4:
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return new_approx
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else:
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return previous_approx
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# 偵測輪廓
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contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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def initial_approx(approx):
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distance = []
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for point in approx:
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x, y = point[0]
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distance.append(x**2+y**2)
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min_d, max_d = 1e9, -1
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min_index, max_index = -100, -100
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for index, d in enumerate(distance):
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if d < min_d:
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min_d = d
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min_index = index
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if d > max_d:
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max_d = d
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max_index = index
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new_approx = []
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# 找到最大的輪廓
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max_contour = max(contours, key=cv2.contourArea)
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remain_index = list(range(4))
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remain_index.remove(max_index)
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remain_index.remove(min_index)
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# 找到輪廓的近似多邊形
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epsilon = 0.05 * cv2.arcLength(max_contour, True)
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approx = cv2.approxPolyDP(max_contour, epsilon, True)
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if approx[remain_index[0]][0][0] > approx[remain_index[1]][0][0]: # remain 的第一個點的 x 大於第二個點的，代表他在右上角
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second_index = remain_index[0]
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third_index = remain_index[1]
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else:
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second_index = remain_index[1]
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third_index = remain_index[0]
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# 繪製多邊形
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if len(approx) == 4: # 確保是四個角點
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for point in approx:
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print(point)
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x, y = point[0]
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cv2.circle(frame, (x, y), 5, (0, 0, 255), -1) # 在角點位置畫紅色圓圈
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cv2.drawContours(frame, [approx], -1, (0, 255, 0), 2) # 繪製輪廓
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print()
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# 顯示結果
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cv2.imshow('Paper Detection', frame)
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# 按下 'q' 鍵退出迴圈
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if cv2.waitKey(33) & 0xFF == ord('q'): # 等待 33ms (1秒 = 1000ms, 1秒顯示30幀)
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break
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new_approx.append(approx[min_index])
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new_approx.append(approx[second_index])
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new_approx.append(approx[third_index])
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new_approx.append(approx[max_index])
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return new_approx
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if __name__ == '__main__':
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cap.release()
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cv2.destroyAllWindows()
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previous_approx = []
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while True:
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ret, frame = cap.read() # 讀取影片幀
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if not ret:
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break
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# 預處理影像（例如：轉為灰度）
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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gray = cv2.GaussianBlur(gray, (5, 5), 0)
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# 偵測邊緣
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edges = cv2.Canny(gray, 30, 90)
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dilate_kernel = np.ones((5, 5), np.uint8)
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dilate_edges = cv2.dilate(edges, dilate_kernel, iterations=1)
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# 偵測輪廓
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contours, _ = cv2.findContours(dilate_edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if contours != []:
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# 找到最大的輪廓
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max_contour = max(contours, key=cv2.contourArea)
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# 找到輪廓的近似多邊形
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epsilon = 0.05 * cv2.arcLength(max_contour, True)
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approx = cv2.approxPolyDP(max_contour, epsilon, True)
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# 繪製多邊形
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if len(approx) == 4: # 確保是四個角點
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if previous_approx == []:
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print("INITIAL")
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previous_approx = initial_approx(approx)
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new_approx = get_new_approx(approx, previous_approx)
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previous_approx = new_approx
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x, y = new_approx[0][0]
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cv2.circle(frame, (x, y), 15, (0, 0, 255), -1) # 在角點位置畫紅色圓圈
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x, y = new_approx[1][0]
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cv2.circle(frame, (x, y), 30, (0, 0, 255), -1) # 在角點位置畫紅色圓圈
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x, y = new_approx[2][0]
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cv2.circle(frame, (x, y), 15, (255, 0, 0), -1) # 在角點位置畫紅色圓圈
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x, y = new_approx[3][0]
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cv2.circle(frame, (x, y), 30, (255, 0, 0), -1) # 在角點位置畫紅色圓圈
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cv2.drawContours(frame, [approx], -1, (0, 255, 0), 2) # 繪製輪廓
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# 顯示結果
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cv2.namedWindow('Paper Detection(edge)', 0)
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cv2.imshow('Paper Detection(edge)', edges)
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cv2.namedWindow('Paper Detection(dilate edge)', 0)
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cv2.imshow('Paper Detection(dilate edge)', dilate_edges)
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cv2.namedWindow('Paper Detection', 0)
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cv2.imshow('Paper Detection', frame)
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# 按下 'q' 鍵退出迴圈
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if cv2.waitKey(33) & 0xFF == ord('q'): # 等待 33ms (1秒 = 1000ms, 1秒顯示幀)
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break
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if cv2.waitKey(33) & 0xFF == ord(' '):
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print("Clear previous approxes")
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previous_approx = []
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cap.release()
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cv2.destroyAllWindows()
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