How to use img_debug method in Pyscreenshot

Best Python code snippet using pyscreenshot_python

deskew.py

Source:deskew.py

1import cv22import imutils3import numpy as np4import mahotas5from field_test.smart_test.license_plate_recognition.kmeans import kMeans6from field_test.smart_test.license_plate_recognition.find_color import car_plate_color7def distance(pt1, pt2):8    return np.sqrt((pt1[0] - pt2[0]) ** 2 + (pt1[1] - pt2[1]) ** 2)9def four_perspective(img, pt1, pt2, pt3, pt4):10    w = (distance(pt1, pt2) + distance(pt3, pt4)) / 211    h = (distance(pt2, pt3) + distance(pt1, pt4)) / 212    ori = [[pt1[0], pt1[1]], [pt2[0], pt2[1]],13           [pt3[0], pt3[1]], [pt4[0], pt4[1]]]14    ori = np.array(ori).astype(np.float32)15    dst = [[0, 0], [w, 0],16           [w, h], [0, h]]17    dst = np.array(dst).astype(np.float32)18    # compute the perspective transform matrix and then apply it19    M = cv2.getPerspectiveTransform(ori, dst)20    img = cv2.warpPerspective(img, M, (int(w), int(h)), flags=cv2.INTER_LANCZOS4,21                              borderMode=cv2.BORDER_CONSTANT, borderValue=0)22    return img23def deskew(ori_img, iteration=36, remove_bg=False):24    # preprocess25    img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2GRAY)26    img = cv2.medianBlur(img, 3)27    h, w = img.shape[:2]28    val, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)29    img = cv2.GaussianBlur(img, (7, 7), 0)30    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 1))31    img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel, iterations=iteration, borderType=cv2.BORDER_CONSTANT,32                           borderValue=0)33    _, img = cv2.threshold(img, 1, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)34    # rotation fix35    coords = np.column_stack(np.where(img > 0))  # get all non-zero pixel coords36    anchor, size, angle = cv2.minAreaRect(coords)  # bound them with a rotated rect37    # angle of minAreaRect is confusing, recommends to a good answer here38    # https://stackoverflow.com/questions/15956124/minarearect-angles-unsure-about-the-angle-returned39    if angle < -45:40        angle = -(90 + angle)41        center = (anchor[0] + size[1] / 2, anchor[1] + size[0] / 2)42    else:43        angle = -angle44        center = (anchor[0] + size[0] / 2, anchor[1] + size[1] / 2)45    M = cv2.getRotationMatrix2D(center, angle, 1)46    img_padded = cv2.copyMakeBorder(ori_img, int(0.2 * h), int(0.2 * h), int(0.2 * w), int(0.2 * w),47                                    cv2.BORDER_CONSTANT, value=0)48    h_padded, w_padded = img_padded.shape[:2]49    warp = cv2.warpAffine(img_padded, M, (w_padded, h_padded), flags=cv2.INTER_LANCZOS4,50                          borderMode=cv2.BORDER_CONSTANT, borderValue=0)51    if remove_bg:52        mask_padded = cv2.copyMakeBorder(img, int(0.2 * h), int(0.2 * h), int(0.2 * w), int(0.2 * w),53                                         cv2.BORDER_CONSTANT, value=0)54        mask_h_padded, mask_w_padded = mask_padded.shape[:2]55        mask_warp = cv2.warpAffine(mask_padded, M, (mask_w_padded, mask_h_padded), flags=cv2.INTER_LANCZOS4,56                                   borderMode=cv2.BORDER_CONSTANT, borderValue=0)57        coords = np.column_stack(np.where(mask_warp > 0))  # get all non-zero pixel coords58        anchor, size, angle = cv2.minAreaRect(coords)  # bound them with a rotated rect59        if angle < -45:60            warp = warp[int(anchor[0] - size[1] / 2):int(anchor[0] + size[1] / 2),61                   int(anchor[1] - size[0] / 2):int(anchor[1] + size[0] / 2)]62        else:63            warp = warp[int(anchor[0] - size[0] / 2):int(anchor[0] + size[0] / 2),64                   int(anchor[1] - size[1] / 2):int(anchor[1] + size[1] / 2)]65    return warp66def detrap(ori_img):67    # preprocess68    img_debug = ori_img69    # try to extract car license plate using color matching70    img_color = car_plate_color(img_debug, iteration=12)71    try:72        img_debug = deskew(img_color, remove_bg=True)73        img_debug = imutils.resize(img_debug, width=320, inter=cv2.INTER_LANCZOS4)74        cv2.imshow('img_debug_color', img_debug)75        cv2.waitKey(0)76    except:77        return ori_img78    img = cv2.cvtColor(img_debug, cv2.COLOR_BGR2GRAY)79    img = cv2.medianBlur(img, 3)80    h, w = img.shape[:2]81    # find out the best otsu despite of black pixels82    if len(img[img > 0]) > 0:83        T = mahotas.otsu(img[img > 0])84    else:85        T = 086    print('otsu value:{}'.format(T))87    # val, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)88    img[img >= T] = 25589    img[img < T] = 090    img = cv2.GaussianBlur(img, (13, 13), 0)91    ori_h, ori_w = img.shape[:2]92    img = cv2.copyMakeBorder(img, int(0.2 * ori_h), int(0.2 * ori_h), int(0.2 * ori_w), int(0.2 * ori_w),93                             cv2.BORDER_CONSTANT, value=0)94    img_debug = cv2.copyMakeBorder(img_debug, int(0.2 * ori_h), int(0.2 * ori_h), int(0.2 * ori_w), int(0.2 * ori_w),95                                   cv2.BORDER_CONSTANT, value=0)96    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 3))97    img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel, iterations=32, borderType=cv2.BORDER_CONSTANT, borderValue=0)98    val, img = cv2.threshold(img, 1, 255, cv2.THRESH_BINARY)99    h, w = img.shape[:2]100    # img = img[int(0.2 * ori_h):h - int(0.2 * ori_h), int(0.2 * ori_w): w - int(0.2 * ori_w)]101    cv2.imshow('img_debug1', img)102    cv2.waitKey(0)103    # remove small contours104    # img = cv2.Canny(img, 1, 1)105    cnts = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]106    if len(cnts) > 0:107        c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]108        img_cnts = np.zeros_like(img, dtype=np.uint8)109        cv2.drawContours(img_cnts, [c], 0, (255, 255, 255), -1)110        cv2.imshow('img_cnts', img_cnts)111        cv2.waitKey(0)112        img = img_cnts113    img_edge = cv2.Canny(img, 1, 1)114    img = cv2.bitwise_and(np.dstack([img, img, img]), img_debug)115    h, w = img.shape[:2]116    accum = 10117    lines = cv2.HoughLinesP(img_edge, 1, np.pi / 180, accum, minLineLength=h // 4, maxLineGap=h)118    # gather the adjacent lines together119    if lines is not None and len(lines) >= 4:120        print('found {} lines'.format(len(lines)))121        lines1 = lines[:, 0, :]  # extract to 2d122        k_set = []123        for i, (x1, y1, x2, y2) in enumerate(lines1[:]):124            k_set.append(slope(x1, x2, y1, y2, theta=True))125            # cv2.line(img, (x1, y1), (x2, y2), (128, 128, 128), 5)126        k_set = np.asarray(k_set)127        k_set = k_set.reshape((len(k_set), 1))128        while True:129            # make sure no nan/inf in k130            k_center, k_cluster = kMeans(k_set, 2)131            if k_center is None or k_cluster is None:132                k_center = [0, np.pi / 2]133            k_max = np.max(k_center)134            if not np.isnan(k_max):135                k_center = np.sort(k_center, axis=0)136                break137        h_idx = 0 if np.abs(k_center[0]) < np.abs(k_center[1]) else 1138        v_idx = int(not h_idx)139        theta_h = np.rad2deg(k_center[h_idx])140        theta_v = np.rad2deg(k_center[v_idx]) - theta_h141        if theta_v > 45:142            theta_v = 90 - theta_v143        # fix rotation144        img = imutils.rotate_bound(img, angle=-theta_h)145        cv2.imshow('img_debug_rotation', img)146        cv2.waitKey(0)147        # fix perspective148        ori = [[0, 0], [w - h * np.tan(np.deg2rad(theta_v)), 0],149               [w, h], [h * np.tan(np.deg2rad(theta_v)), h]]150        ori = np.array(ori).astype(np.float32)151        dst = [[0, 0], [w, 0],152               [w, h], [0, h]]153        dst = np.array(dst).astype(np.float32)154        # compute the perspective transform matrix and then apply it155        M = cv2.getPerspectiveTransform(ori, dst)156        old_h, old_w = img.shape[:2]157        img = cv2.copyMakeBorder(img, int(0.2 * old_h), int(0.2 * old_h), int(0.2 * old_w),158                                 int(0.2 * old_w),159                                 cv2.BORDER_CONSTANT, value=0)160        new_h, new_w = img.shape[:2]161        img = cv2.warpPerspective(img, M, (new_w, new_h), borderMode=cv2.BORDER_CONSTANT, borderValue=0)162        img = deskew(img, remove_bg=True)163    return img164def slope(x1, x2, y1, y2, theta=True):165    if x1 != x2:166        result = (y2 - y1) / (x2 - x1)167    else:168        result = np.inf169    if theta:170        return np.arctan(result)171    else:172        return result173def intercept(x1, x2, y1, y2):174    if x1 != x2:175        return y1 - ((y2 - y1) / (x2 - x1)) * x1176    else:177        return np.inf178def line_intersection(k1, b1, k2, b2):179    # l1 : y = k1 * x + b1180    # l2 : y = k2 * x + b2181    if k1 != k2:182        x = - (b2 - b1) / (k2 - k1)183        y = k1 * x + b1184        return x, y185    else:186        return np.inf, np.inf187if __name__ == '__main__':188    ori_img = cv2.imread('test/img_debug_color_screenshot_02.08.2018.png')189    # ori_img = cv2.imread('f:/temp/test.jpg')190    # warp = deskew(ori_img, remove_bg=True)191    trap = detrap(ori_img)192    cv2.imshow('img_p', trap)193    cv2.waitKey(0)194    # cv2.imshow('warp', warp)195    # cv2.waitKey(0)...

run_picar.py

Source:run_picar.py

1# This is the main code to run the pi car.2import time3import numpy as np4from cv2 import cv2 as cv5import driver6import image_processing7import camera_capturer8import utils9DEBUG = False10PERIOD = 0  # the period of image caption, processing and sending signal11OFFSET = 37112def cruise_control(bias, k_p=1, k_i=0, k_d=1):13    """ Controls the picar on the mode of cruise14    """15    return 016def cruise():17    """ Tracks the black line.18    Acquires images from front camera and uses it to do pure pursuit.19    Uses functions in driver.py to drive the pi car.20    There is a three-step process to reach the goal.21    Step 1.22        Employs CameraCapturer class to acquire images from front camera and23        rectify lens distortion.24    Step 2.25        Chooses the ROI and binarizes the it. Then uses morphology method to26        get the target point.27    Step 3.28        According to target point, applies pure pursuit algorithm and uses29        functions in driver.py to drive the car.30    Args:31        None32    Returns:33        None34    """35    # Initialize CameraCapturer and drive36    cap = camera_capturer.CameraCapturer("front")37    d = driver.driver()38    d.setStatus(motor=0.4, servo=0, mode="speed")39    last_time = time.time()40    target = OFFSET  # -int(cap.width / 5)41    # Parameters of PID controller42    kp = 1.243    ki = 044    kd = 0.145    # Initialize error to 0 for PID controller46    error_p = 047    error_i = 048    error_d = 049    error = 050    servo = 051    last_servo = 052    last_angle = 053    n = 0.254    try:55        while True:56            this_time = time.time()57            if this_time - last_time > PERIOD:  # Execute the code below every58                                                # PERIOD time59                last_time = this_time60                # d.setStatus(motor=0, servo=n, mode="speed")61                # n = -n62                # continue63                # ----------------------------------------------------------- #64                #                       Start your code here                  #65                # Image processing. Outputs a target_point.66                frame = cap.get_frame()67                start = time.time()68                skel, img_bin_rev = image_processing.image_process(frame)69                white_rate = \70                    np.size(img_bin_rev[img_bin_rev == 255]) / img_bin_rev.size71                if white_rate > 0.3:72                    print("stay", white_rate)73                    d.setStatus(servo=last_servo)74                    continue75                target_point, width, _, img_DEBUG, angle = \76                    choose_target_point(skel, target)77                end = time.time()78                if angle == 0:79                    angle = last_angle80                    pass81                else:82                    # Update the PID error83                    error_p = angle  # **(9/7)84                    error_i += error_p85                    error_d = error_p - error86                    error = error_p87                    # PID controller88                    servo = utils.constrain(- kp*error_p89                                            - ki*error_i90                                            - kd*error_d,91                                            1, -1)92                d.setStatus(servo=servo)93                last_servo = servo94                # print("servo: ", servo, "error_p: ", error_p)95                # img_DEBUG[:, target] = 25596                # if DEBUG:97                #     # cv.imshow("frame", frame)98                #     cv.imshow("img_bin_rev", img_bin_rev)99                #     cv.imshow("img_DEBUG", img_DEBUG)100                #     cv.waitKey(300)101                # ----------------------------------------------------------- #102            else:103                # time.sleep(0.01)104                pass105    except KeyboardInterrupt:106        d.setStatus(servo=0, motor=0)107def choose_target_point(skel, target):108    """ Selects a target poitn from skeleton for pure pursuit.109    Draws a ellipse and applies an and operation to the ellipse with the skel.110    Then returns a point that has least distance with the center of the111    ellipse.112    Args:113        skel: skeleton of trajectory.114    Returns:115        target_point: target point for pure pursuit.116    """117    width = skel.shape[1]118    height = skel.shape[0]119    img = np.zeros((height, width), dtype=np.uint8)120    ellipse_a = width // 2121    ellipse_b = height // 3122    ellipse = cv.ellipse(img,123                         center=(target, height),124                         axes=(ellipse_a, ellipse_b),125                         angle=0,126                         startAngle=180,127                         endAngle=360,128                         color=255,129                         thickness=1)130    img_points = np.bitwise_and(skel, ellipse)131    _, contours, _ = cv.findContours(img_points,132                                     mode=cv.RETR_EXTERNAL,133                                     method=cv.CHAIN_APPROX_NONE)134    discrete_points = []135    img_DEBUG = np.zeros((height, width, 3), dtype=np.uint8)136    img_DEBUG[:, :, 0] = skel137    img_DEBUG[:, :, 1] = img_points138    img_DEBUG[:, :, 2] = ellipse139    cv.imwrite("img_DEBUG_2.jpg", img_DEBUG)140    # cv.waitKey(200)141    for contour in contours:142        if contour.size == 2:143            discrete_points.append(np.squeeze(contour))144        else:145            pass146    # discrete_points = sorted(discrete_points,147    #                          key=lambda x: (x[0] - width // 2)**2 +148    #                                        (x[1] - height) ** 2)149    discrete_points = sorted(discrete_points,150                             key=lambda x: np.abs(x[0] - target))151    if len(discrete_points) != 0:152        px = discrete_points[0][0] - target153        angle = np.arctan2(px, ellipse_a)154        # angle = angle[0]155        print("angle:", angle)156        return discrete_points[0], width, height, img_DEBUG, angle157    else:158        return [0, 0], width, height, img_DEBUG, 0159        # return [target, 0], width, height, img_DEBUG160if __name__ == "__main__":...

screenpoint.py

Source:screenpoint.py

1import numpy as np2import cv23import logging4MIN_MATCH_COUNT = 205FLANN_INDEX_KDTREE = 06sift = cv2.xfeatures2d.SIFT_create()7def project(view, screen, point=None, debug=False):8    kp_screen, des_screen = sift.detectAndCompute(screen, None)9    kp_view, des_view = sift.detectAndCompute(view, None)10    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)11    search_params = dict(checks=50)12    flann = cv2.FlannBasedMatcher(index_params, search_params)13    matches = flann.knnMatch(des_screen, des_view, k=2)14    # Store all good matches as per Lowe's ration test15    good = []16    for m, n in matches:17        if m.distance < 0.7 * n.distance:18            good.append(m)19    if len(good) < MIN_MATCH_COUNT:20        logging.debug("ScreenPoint: Not enough matches.")21        return -1, -122    screen_pts = np.float32([kp_screen[m.queryIdx].pt23                             for m in good]).reshape(-1, 1, 2)24    view_pts = np.float32([kp_view[m.trainIdx].pt25                           for m in good]).reshape(-1, 1, 2)26    if point==None:27        h, w = view.shape[0],view.shape[1]28        h=(h - 1) * 0.529        w=(w - 1) * 0.530    else:31        w, h = point32    # print(h,w)33    M, mask = cv2.findHomography(view_pts, screen_pts, cv2.RANSAC, 5.0)34    pts = np.float32([[w, h]]).reshape(-1, 1, 2)35    dst = cv2.perspectiveTransform(pts, M)36    x, y = np.int32(dst[0][0])37    if debug:38        img_debug = draw_debug_(x, y, view, screen, M, mask, kp_screen,39                                kp_view, good)40        return x, y, img_debug41    else:42        return x, y43def draw_debug_(x, y, view, screen, M, mask, kp_screen, kp_view, good):44    matchesMask = mask.ravel().tolist()45    draw_params = dict(46        matchColor=(0, 255, 0),  # draw matches in green color47        singlePointColor=None,48        matchesMask=matchesMask,  # draw only inliers49        flags=2)50    img_debug = cv2.drawMatches(screen, kp_screen, view, kp_view, good, None,51                                **draw_params)52    # Get view centroid coordinates in img_debug space.53    cx = int(view.shape[1] * 0.5 + screen.shape[1])54    cy = int(view.shape[0] * 0.5)55    # Draw view outline.56    cv2.rectangle(img_debug, (screen.shape[1], 0),57                  (img_debug.shape[1] - 2, img_debug.shape[0] - 2),58                  (0, 0, 255), 2)59    # draw connecting line.60    cv2.polylines(img_debug, [np.int32([(x, y), (cx, cy)])], True,61                  (100, 100, 255), 1, cv2.LINE_AA)62    # Draw query/match markers.63    cv2.drawMarker(img_debug, (cx, cy), (0, 0, 255), cv2.MARKER_CROSS, 30, 2)64    cv2.circle(img_debug, (x, y), 10, (0, 0, 255), -1)...

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