opencv筆記(車輛計數實現)

注意：更準確的車輛計數實現應考慮深度學習。

基本實現思路

1. 加載視頻
2. 通過形態學識別車輛
3. 對車輛進行統計
4. 顯示車輛統計信息

涉及知識

• 窗口展示
• 圖像/視頻加載
• 基本圖形的繪制
• 車輛識別
  • 基本圖像運算與處理
  • 形態學
  • 輪廓查找

分步驟代碼

加載視頻

import cv2


# 創建窗口對象
cv2.namedWindow('video', cv2.WINDOW_NORMAL)
cap = cv2.VideoCapture('2.mp4')

while 1:
    ok, frame = cap.read()
    if ok:
        # 窗口顯示
        cv2.imshow('video', frame)
        key = cv2.waitKey(1)
        if key == 27 or cv2.getWindowProperty('color', cv2.WND_PROP_VISIBLE) < 1.0:
            break
    else:
        break

# 釋放資源
cap.release()
cv2.destroyAllWindows()

通過形態學識別車輛

import cv2

# 車輛過濾條件應根據參照物適配，此處僅為演示原理
min_w = 100
min_h = 100

line_high = 200
line_width = 6

cv2.namedWindow('video', cv2.WINDOW_AUTOSIZE)
cap = cv2.VideoCapture('6.ts')
bs = cv2.createBackgroundSubtractorMOG2()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
cars = []
while 1:
    ok, frame = cap.read()
    if ok:
        # 轉換繪圖圖
        cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # 高斯去噪
        blur = cv2.GaussianBlur(frame, (3, 3), 5)
        # 去除背景
        mask = bs.apply(blur)
        # 腐蝕
        erode = cv2.erode(mask, kernel, iterations=2)
        # 膨脹
        dilate = cv2.dilate(erode, kernel, iterations=2)
        # 閉運算
        close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)
        close = cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)

        # 查找輪廓
        contours, _ = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        # 過濾、繪制輪廓，車輛計數
        for contour in contours:
            x, y, w, h = cv2.boundingRect(contour)
            # 過濾
            if w < min_w or h < min_h:
                continue
            # 繪制輪廓
            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

        cv2.imshow('video', frame)
        key = cv2.waitKey(42)
        if key == 27:
            break
    else:
        break

# 釋放資源
cap.release()
cv2.destroyAllWindows()

對車輛進行統計

import cv2

# 車輛過濾條件應根據參照物適配，此處僅為演示原理
min_w = 100
min_h = 100

# 檢測線高度、誤差
line_high = 190
line_offset = 5

cv2.namedWindow('video', cv2.WINDOW_AUTOSIZE)
cap = cv2.VideoCapture('6.ts')
bs = cv2.createBackgroundSubtractorMOG2()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
car_count = 0
while 1:
    ok, frame = cap.read()
    if ok:
        # 轉換繪圖圖
        cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # 高斯去噪
        blur = cv2.GaussianBlur(frame, (3, 3), 3)
        # 去除背景
        mask = bs.apply(blur)
        # 腐蝕
        erode = cv2.erode(mask, kernel, iterations=3)
        # 膨脹
        dilate = cv2.dilate(erode, kernel, iterations=3)
        # 閉運算
        close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)
        close = cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)
        # 查找輪廓
        contours, _ = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        # 繪制檢測線
        cv2.line(frame, (0, line_high), (frame.shape[1], line_high), (255, 0, 0), 3)
        # 過濾、繪制輪廓，車輛計數
        for contour in contours:
            x, y, w, h = cv2.boundingRect(contour)
            # 過濾
            if w < min_w or h < min_h:
                continue

            # 搜集有效車輛（此處有邏輯限制：車輛不能停止于檢測線區域內）
            center = y + h / 2
            if line_high - line_offset < center < line_high + line_offset:
                car_count += 1
                # 繪制輪廓
                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

        cv2.imshow('video', frame)
        key = cv2.waitKey(42)
        if key == 27:
            break
    else:
        break
# 釋放資源
cap.release()
cv2.destroyAllWindows()

顯示車輛統計信息

import cv2

# 車輛過濾條件應根據參照物適配，此處僅為演示原理
min_w = 100
min_h = 100

# 檢測線高度、誤差
line_high = 150
line_offset = 3

cv2.namedWindow('video', cv2.WINDOW_AUTOSIZE)
cap = cv2.VideoCapture('6.ts')
bs = cv2.createBackgroundSubtractorMOG2()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
car_count = 0
while 1:
    ok, frame = cap.read()
    if ok:
        # 轉換繪圖圖
        cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # 高斯去噪
        blur = cv2.GaussianBlur(frame, (3, 3), 3)
        # 去除背景
        mask = bs.apply(blur)
        # 腐蝕
        erode = cv2.erode(mask, kernel, iterations=3)
        # 膨脹
        dilate = cv2.dilate(erode, kernel, iterations=3)
        # 閉運算
        close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, kernel)
        close = cv2.morphologyEx(close, cv2.MORPH_CLOSE, kernel)
        # 查找輪廓
        contours, _ = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        # 繪制檢測線
        cv2.line(frame, (0, line_high), (frame.shape[1], line_high), (255, 0, 0), 3)
        # 過濾、繪制輪廓，車輛計數
        for contour in contours:
            x, y, w, h = cv2.boundingRect(contour)
            # 過濾
            if w < min_w or h < min_h:
                continue
            # 搜集有效車輛（此處有邏輯限制：車輛不能停止于檢測線區域內，準確計數應該為每輛車提供唯一標識）
            center = y + h / 2
            if line_high - line_offset < center < line_high + line_offset:
                car_count += 1
                # 繪制輪廓
                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(frame, f'Cars: {car_count}', (300, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 3)
        cv2.imshow('video', frame)
        key = cv2.waitKey(42)
        if key == 27:
            break
    else:
        break
# 釋放資源
cap.release()
cv2.destroyAllWindows()