基於影象處理和tensorflow實現GTA5的車輛自動駕駛——第七節繪製優化後的道路線條
阿新 • • 發佈:2020-12-16
效果
第六節實現的效果
本節效果
注:
本節作者一開篇就說了這句話:
實現的效果確實可以,但是程式碼很複雜,我看了下也不想加註釋了 :(,等整個專案實現的時候再回來填這個坑吧
本節程式碼修改的較多,我直接附上整個原始碼好了
import numpy as np from PIL import ImageGrab import cv2 import time def compare_lines(lines, color=[0, 255, 255], thickness=3): ''' try: for line in lines: coords = line[0] cv2.line(img=img, pt1=(coords[0], coords[1]), pt2=(coords[2], coords[3]), color=[255, 255, 255], thickness=3 ) except: pass ''' # if this fails, go with some default line try: # finds the maximum y value for a lane marker # (since we cannot assume the horizon will always be at the same point.) ys = [] for i in lines: for ii in i: ys += [ii[1], ii[3]] min_y = min(ys) max_y = 600 new_lines = [] line_dict = {} for idx, i in enumerate(lines): for xyxy in i: # These four lines: # modified from http://stackoverflow.com/questions/21565994/method-to-return-the-equation-of-a-straight-line-given-two-points # Used to calculate the definition of a line, given two sets of coords. x_coords = (xyxy[0], xyxy[2]) y_coords = (xyxy[1], xyxy[3]) A = np.vstack([x_coords, np.ones(len(x_coords))]).T m, b = np.linalg.lstsq(A, y_coords)[0] # Calculating our new, and improved, xs x1 = (min_y - b) / m x2 = (max_y - b) / m line_dict[idx] = [m, b, [int(x1), min_y, int(x2), max_y]] new_lines.append([int(x1), min_y, int(x2), max_y]) final_lanes = {} for idx in line_dict: final_lanes_copy = final_lanes.copy() m = line_dict[idx][0] b = line_dict[idx][1] line = line_dict[idx][2] if len(final_lanes) == 0: final_lanes[m] = [[m, b, line]] else: found_copy = False for other_ms in final_lanes_copy: if not found_copy: if abs(other_ms * 1.2) > abs(m) > abs(other_ms * 0.8): if abs(final_lanes_copy[other_ms][0][1] * 1.2) > abs(b) > abs( final_lanes_copy[other_ms][0][1] * 0.8): final_lanes[other_ms].append([m, b, line]) found_copy = True break else: final_lanes[m] = [[m, b, line]] line_counter = {} for lanes in final_lanes: line_counter[lanes] = len(final_lanes[lanes]) top_lanes = sorted(line_counter.items(), key=lambda item: item[1])[::-1][:2] lane1_id = top_lanes[0][0] lane2_id = top_lanes[1][0] def average_lane(lane_data): x1s = [] y1s = [] x2s = [] y2s = [] for data in lane_data: x1s.append(data[2][0]) y1s.append(data[2][1]) x2s.append(data[2][2]) y2s.append(data[2][3]) return int(np.mean(x1s)), int(np.mean(y1s)), int(np.mean(x2s)), int(np.mean(y2s)) l1_x1, l1_y1, l1_x2, l1_y2 = average_lane(final_lanes[lane1_id]) l2_x1, l2_y1, l2_x2, l2_y2 = average_lane(final_lanes[lane2_id]) return [l1_x1, l1_y1, l1_x2, l1_y2], [l2_x1, l2_y1, l2_x2, l2_y2] except Exception as e: print(str(e)) def draw_lines(image, gray_img, lines): try: l1, l2 = compare_lines( lines) cv2.line(image, (l1[0], l1[1]), (l1[2], l1[3]), [0, 255, 0], 30) cv2.line(image, (l2[0], l2[1]), (l2[2], l2[3]), [0, 255, 0], 30) except Exception as e: print(str(e)) pass try: for coords in lines: coords = coords[0] try: cv2.line(gray_img, (coords[0], coords[1]), (coords[2], coords[3]), [255, 0, 0], 3) except Exception as e: print(str(e)) except Exception as e: pass def roi(img, vertices): mask = np.zeros_like(img) cv2.fillPoly(mask, vertices, 255) masked = cv2.bitwise_and(img, mask) return masked def convert_To_gray(image): # to gray gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # canny gray_img = cv2.Canny(gray_img, threshold1=100, threshold2=200) # 高斯模糊 gray_img = cv2.GaussianBlur(gray_img, ksize=(5,5), sigmaX=0) # mask img 只取紅色區域的資料 vertices = np.array([[10, 500], [10, 300], [300, 200], [500, 200], [800, 300], [800, 500], ], np.int32) gray_img = roi(gray_img, [vertices]) # 劃線 lines = cv2.HoughLinesP(gray_img, rho=1, theta=np.pi / 180, threshold=180, lines=np.array([]),minLineLength=150, maxLineGap=5) draw_lines(image=image, gray_img=gray_img, lines=lines) def screen_record(): last_time = time.time() while True: # 800x600 windowed mode for GTA 5, at the top left position of your main screen. # 40 px accounts for title bar. printscreen = np.array(ImageGrab.grab(bbox=(0, 40, 800, 640))) print('loop took {} seconds'.format(time.time() - last_time)) last_time = time.time() gray_img = convert_To_gray(printscreen) # cv2.imshow('window', gray_img) cv2.imshow('window', cv2.cvtColor(printscreen, cv2.COLOR_BGR2RGB)) if cv2.waitKey(25) & 0xFF == ord('q'): cv2.destroyAllWindows() break screen_record()