# Copyright 2015 The Android Open Source Project # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import os.path import cv2 import its.caps import its.device import its.image import its.objects import numpy as np FMT_ATOL = 0.01 # Absolute tolerance on format ratio AR_CHECKED = ["4:3", "16:9", "18:9"] # Aspect ratios checked FOV_PERCENT_RTOL = 0.15 # Relative tolerance on circle FoV % to expected LARGE_SIZE = 2000 # Define the size of a large image NAME = os.path.basename(__file__).split(".")[0] NUM_DISTORT_PARAMS = 5 THRESH_L_AR = 0.02 # aspect ratio test threshold of large images THRESH_XS_AR = 0.075 # aspect ratio test threshold of mini images THRESH_L_CP = 0.02 # Crop test threshold of large images THRESH_XS_CP = 0.075 # Crop test threshold of mini images THRESH_MIN_PIXEL = 4 # Crop test allowed offset PREVIEW_SIZE = (1920, 1080) # preview size def convert_ar_to_float(ar_string): """Convert aspect ratio string into float. Args: ar_string: "4:3" or "16:9" Returns: float(ar_string) """ ar_list = [float(x) for x in ar_string.split(":")] return ar_list[0] / ar_list[1] def determine_sensor_aspect_ratio(props): """Determine the aspect ratio of the sensor. Args: props: camera properties Returns: matched entry in AR_CHECKED """ match_ar = None sensor_size = props["android.sensor.info.preCorrectionActiveArraySize"] sensor_ar = (float(abs(sensor_size["right"] - sensor_size["left"])) / abs(sensor_size["bottom"] - sensor_size["top"])) for ar_string in AR_CHECKED: if np.isclose(sensor_ar, convert_ar_to_float(ar_string), atol=FMT_ATOL): match_ar = ar_string if not match_ar: print "Warning! RAW aspect ratio not in:", AR_CHECKED return match_ar def aspect_ratio_scale_factors(ref_ar_string, props): """Determine scale factors for each aspect ratio to correct cropping. Args: ref_ar_string: camera aspect ratio that is the reference props: camera properties Returns: dict of correction ratios with AR_CHECKED values as keys """ ref_ar = convert_ar_to_float(ref_ar_string) # find sensor area height_max = 0 width_max = 0 for ar_string in AR_CHECKED: match_ar = [float(x) for x in ar_string.split(":")] try: f = its.objects.get_largest_yuv_format(props, match_ar=match_ar) if f["height"] > height_max: height_max = f["height"] if f["width"] > width_max: width_max = f["width"] except IndexError: continue sensor_ar = float(width_max) / height_max # apply scaling ar_scaling = {} for ar_string in AR_CHECKED: target_ar = convert_ar_to_float(ar_string) # scale down to sensor with greater (or equal) dims if ref_ar >= sensor_ar: scaling = sensor_ar / ref_ar else: scaling = ref_ar / sensor_ar # scale up due to cropping to other format if target_ar >= sensor_ar: scaling = scaling * target_ar / sensor_ar else: scaling = scaling * sensor_ar / target_ar ar_scaling[ar_string] = scaling return ar_scaling def find_yuv_fov_reference(cam, req, props): """Determine the circle coverage of the image in YUV reference image. Args: cam: camera object req: camera request props: camera properties Returns: ref_fov: dict with [fmt, % coverage, w, h] """ ref_fov = {} fmt_dict = {} # find number of pixels in different formats for ar in AR_CHECKED: match_ar = [float(x) for x in ar.split(":")] try: f = its.objects.get_largest_yuv_format(props, match_ar=match_ar) fmt_dict[f["height"]*f["width"]] = {"fmt": f, "ar": ar} except IndexError: continue # use image with largest coverage as reference ar_max_pixels = max(fmt_dict, key=int) # capture and determine circle area in image cap = cam.do_capture(req, fmt_dict[ar_max_pixels]["fmt"]) w = cap["width"] h = cap["height"] img = its.image.convert_capture_to_rgb_image(cap, props=props) print "Captured %s %dx%d" % ("yuv", w, h) img_name = "%s_%s_w%d_h%d.png" % (NAME, "yuv", w, h) _, _, circle_size = measure_aspect_ratio(img, False, img_name, True) fov_percent = calc_circle_image_ratio(circle_size[1], circle_size[0], w, h) ref_fov["fmt"] = fmt_dict[ar_max_pixels]["ar"] ref_fov["percent"] = fov_percent ref_fov["w"] = w ref_fov["h"] = h print "Using YUV reference:", ref_fov return ref_fov def calc_circle_image_ratio(circle_w, circle_h, image_w, image_h): """Calculate the circle coverage of the image. Args: circle_w (int): width of circle circle_h (int): height of circle image_w (int): width of image image_h (int): height of image Returns: fov_percent (float): % of image covered by circle """ circle_area = math.pi * math.pow(np.mean([circle_w, circle_h])/2.0, 2) image_area = image_w * image_h fov_percent = 100*circle_area/image_area return fov_percent def main(): """Test aspect ratio & check if images are cropped correctly for each fmt. Aspect ratio test runs on level3, full and limited devices. Crop test only runs on full and level3 devices. The test image is a black circle inside a black square. When raw capture is available, set the height vs. width ratio of the circle in the full-frame raw as ground truth. Then compare with images of request combinations of different formats ("jpeg" and "yuv") and sizes. If raw capture is unavailable, take a picture of the test image right in front to eliminate shooting angle effect. the height vs. width ratio for the circle should be close to 1. Considering shooting position error, aspect ratio greater than 1+THRESH_*_AR or less than 1-THRESH_*_AR will FAIL. """ aspect_ratio_gt = 1 # ground truth failed_ar = [] # streams failed the aspect ration test failed_crop = [] # streams failed the crop test failed_fov = [] # streams that fail FoV test format_list = [] # format list for multiple capture objects. # Do multi-capture of "iter" and "cmpr". Iterate through all the # available sizes of "iter", and only use the size specified for "cmpr" # Do single-capture to cover untouched sizes in multi-capture when needed. format_list.append({"iter": "yuv", "iter_max": None, "cmpr": "yuv", "cmpr_size": PREVIEW_SIZE}) format_list.append({"iter": "yuv", "iter_max": PREVIEW_SIZE, "cmpr": "jpeg", "cmpr_size": None}) format_list.append({"iter": "yuv", "iter_max": PREVIEW_SIZE, "cmpr": "raw", "cmpr_size": None}) format_list.append({"iter": "jpeg", "iter_max": None, "cmpr": "raw", "cmpr_size": None}) format_list.append({"iter": "jpeg", "iter_max": None, "cmpr": "yuv", "cmpr_size": PREVIEW_SIZE}) ref_fov = {} with its.device.ItsSession() as cam: props = cam.get_camera_properties() props = cam.override_with_hidden_physical_camera_props(props) its.caps.skip_unless(its.caps.read_3a(props)) full_device = its.caps.full_or_better(props) limited_device = its.caps.limited(props) its.caps.skip_unless(full_device or limited_device) level3_device = its.caps.level3(props) raw_avlb = its.caps.raw16(props) mono_camera = its.caps.mono_camera(props) run_crop_test = (level3_device or full_device) and raw_avlb if not run_crop_test: print "Crop test skipped" debug = its.caps.debug_mode() # Converge 3A and get the estimates. sens, exp, gains, xform, focus = cam.do_3a(get_results=True, lock_ae=True, lock_awb=True, mono_camera=mono_camera) print "AE sensitivity %d, exposure %dms" % (sens, exp / 1000000.0) print "AWB gains", gains print "AWB transform", xform print "AF distance", focus req = its.objects.manual_capture_request( sens, exp, focus, True, props) xform_rat = its.objects.float_to_rational(xform) req["android.colorCorrection.gains"] = gains req["android.colorCorrection.transform"] = xform_rat # If raw capture is available, use it as ground truth. if raw_avlb: # Capture full-frame raw. Use its aspect ratio and circle center # location as ground truth for the other jepg or yuv images. print "Creating references for fov_coverage from RAW" out_surface = {"format": "raw"} cap_raw = cam.do_capture(req, out_surface) print "Captured %s %dx%d" % ("raw", cap_raw["width"], cap_raw["height"]) img_raw = its.image.convert_capture_to_rgb_image(cap_raw, props=props) if its.caps.distortion_correction(props): # The intrinsics and distortion coefficients are meant for full # size RAW. Resize back to full size here. img_raw = cv2.resize(img_raw, (0, 0), fx=2.0, fy=2.0) # Intrinsic cal is of format: [f_x, f_y, c_x, c_y, s] # [f_x, f_y] is the horizontal and vertical focal lengths, # [c_x, c_y] is the position of the optical axis, # and s is skew of sensor plane vs lens plane. print "Applying intrinsic calibration and distortion params" ical = np.array(props["android.lens.intrinsicCalibration"]) msg = "Cannot include lens distortion without intrinsic cal!" assert len(ical) == 5, msg sensor_h = props["android.sensor.info.physicalSize"]["height"] sensor_w = props["android.sensor.info.physicalSize"]["width"] pixel_h = props["android.sensor.info.pixelArraySize"]["height"] pixel_w = props["android.sensor.info.pixelArraySize"]["width"] fd = float(cap_raw["metadata"]["android.lens.focalLength"]) fd_w_pix = pixel_w * fd / sensor_w fd_h_pix = pixel_h * fd / sensor_h # transformation matrix # k = [[f_x, s, c_x], # [0, f_y, c_y], # [0, 0, 1]] k = np.array([[ical[0], ical[4], ical[2]], [0, ical[1], ical[3]], [0, 0, 1]]) print "k:", k e_msg = "fd_w(pixels): %.2f\tcal[0](pixels): %.2f\tTOL=20%%" % ( fd_w_pix, ical[0]) assert np.isclose(fd_w_pix, ical[0], rtol=0.20), e_msg e_msg = "fd_h(pixels): %.2f\tcal[1](pixels): %.2f\tTOL=20%%" % ( fd_h_pix, ical[0]) assert np.isclose(fd_h_pix, ical[1], rtol=0.20), e_msg # distortion rad_dist = props["android.lens.distortion"] print "android.lens.distortion:", rad_dist e_msg = "%s param(s) found. %d expected." % (len(rad_dist), NUM_DISTORT_PARAMS) assert len(rad_dist) == NUM_DISTORT_PARAMS, e_msg opencv_dist = np.array([rad_dist[0], rad_dist[1], rad_dist[3], rad_dist[4], rad_dist[2]]) print "dist:", opencv_dist img_raw = cv2.undistort(img_raw, k, opencv_dist) size_raw = img_raw.shape w_raw = size_raw[1] h_raw = size_raw[0] img_name = "%s_%s_w%d_h%d.png" % (NAME, "raw", w_raw, h_raw) aspect_ratio_gt, cc_ct_gt, circle_size_raw = measure_aspect_ratio( img_raw, raw_avlb, img_name, debug) raw_fov_percent = calc_circle_image_ratio( circle_size_raw[1], circle_size_raw[0], w_raw, h_raw) # Normalize the circle size to 1/4 of the image size, so that # circle size won't affect the crop test result factor_cp_thres = (min(size_raw[0:1])/4.0) / max(circle_size_raw) thres_l_cp_test = THRESH_L_CP * factor_cp_thres thres_xs_cp_test = THRESH_XS_CP * factor_cp_thres # If RAW in AR_CHECKED, use it as reference ref_fov["fmt"] = determine_sensor_aspect_ratio(props) if ref_fov["fmt"]: ref_fov["percent"] = raw_fov_percent ref_fov["w"] = w_raw ref_fov["h"] = h_raw print "Using RAW reference:", ref_fov else: ref_fov = find_yuv_fov_reference(cam, req, props) else: ref_fov = find_yuv_fov_reference(cam, req, props) # Determine scaling factors for AR calculations ar_scaling = aspect_ratio_scale_factors(ref_fov["fmt"], props) # Take pictures of each settings with all the image sizes available. for fmt in format_list: fmt_iter = fmt["iter"] fmt_cmpr = fmt["cmpr"] dual_target = fmt_cmpr is not "none" # Get the size of "cmpr" if dual_target: sizes = its.objects.get_available_output_sizes( fmt_cmpr, props, fmt["cmpr_size"]) if not sizes: # device might not support RAW continue size_cmpr = sizes[0] for size_iter in its.objects.get_available_output_sizes( fmt_iter, props, fmt["iter_max"]): w_iter = size_iter[0] h_iter = size_iter[1] # Skip testing same format/size combination # ITS does not handle that properly now if (dual_target and w_iter*h_iter == size_cmpr[0]*size_cmpr[1] and fmt_iter == fmt_cmpr): continue out_surface = [{"width": w_iter, "height": h_iter, "format": fmt_iter}] if dual_target: out_surface.append({"width": size_cmpr[0], "height": size_cmpr[1], "format": fmt_cmpr}) cap = cam.do_capture(req, out_surface) if dual_target: frm_iter = cap[0] else: frm_iter = cap assert frm_iter["format"] == fmt_iter assert frm_iter["width"] == w_iter assert frm_iter["height"] == h_iter print "Captured %s with %s %dx%d. Compared size: %dx%d" % ( fmt_iter, fmt_cmpr, w_iter, h_iter, size_cmpr[0], size_cmpr[1]) img = its.image.convert_capture_to_rgb_image(frm_iter) if its.caps.distortion_correction(props) and raw_avlb: w_scale = float(w_iter)/w_raw h_scale = float(h_iter)/h_raw k_scale = np.array([[ical[0]*w_scale, ical[4], ical[2]*w_scale], [0, ical[1]*h_scale, ical[3]*h_scale], [0, 0, 1]]) print "k_scale:", k_scale img = cv2.undistort(img, k_scale, opencv_dist) img_name = "%s_%s_with_%s_w%d_h%d.png" % (NAME, fmt_iter, fmt_cmpr, w_iter, h_iter) aspect_ratio, cc_ct, (cc_w, cc_h) = measure_aspect_ratio( img, raw_avlb, img_name, debug) # check fov coverage for all fmts in AR_CHECKED fov_percent = calc_circle_image_ratio( cc_w, cc_h, w_iter, h_iter) for ar_check in AR_CHECKED: match_ar_list = [float(x) for x in ar_check.split(":")] match_ar = match_ar_list[0] / match_ar_list[1] if np.isclose(float(w_iter)/h_iter, match_ar, atol=FMT_ATOL): # scale check value based on aspect ratio chk_percent = ref_fov["percent"] * ar_scaling[ar_check] if not np.isclose(fov_percent, chk_percent, rtol=FOV_PERCENT_RTOL): msg = "FoV %%: %.2f, Ref FoV %%: %.2f, " % ( fov_percent, chk_percent) msg += "TOL=%.f%%, img: %dx%d, ref: %dx%d" % ( FOV_PERCENT_RTOL*100, w_iter, h_iter, ref_fov["w"], ref_fov["h"]) failed_fov.append(msg) its.image.write_image(img/255, img_name, True) # check pass/fail for aspect ratio # image size >= LARGE_SIZE: use THRESH_L_AR # image size == 0 (extreme case): THRESH_XS_AR # 0 < image size < LARGE_SIZE: scale between THRESH_XS_AR # and THRESH_L_AR thres_ar_test = max( THRESH_L_AR, THRESH_XS_AR + max(w_iter, h_iter) * (THRESH_L_AR-THRESH_XS_AR)/LARGE_SIZE) thres_range_ar = (aspect_ratio_gt-thres_ar_test, aspect_ratio_gt+thres_ar_test) if (aspect_ratio < thres_range_ar[0] or aspect_ratio > thres_range_ar[1]): failed_ar.append({"fmt_iter": fmt_iter, "fmt_cmpr": fmt_cmpr, "w": w_iter, "h": h_iter, "ar": aspect_ratio, "valid_range": thres_range_ar}) its.image.write_image(img/255, img_name, True) # check pass/fail for crop if run_crop_test: # image size >= LARGE_SIZE: use thres_l_cp_test # image size == 0 (extreme case): thres_xs_cp_test # 0 < image size < LARGE_SIZE: scale between # thres_xs_cp_test and thres_l_cp_test # Also, allow at least THRESH_MIN_PIXEL off to # prevent threshold being too tight for very # small circle thres_hori_cp_test = max( thres_l_cp_test, thres_xs_cp_test + w_iter * (thres_l_cp_test-thres_xs_cp_test)/LARGE_SIZE) min_threshold_h = THRESH_MIN_PIXEL / cc_w thres_hori_cp_test = max(thres_hori_cp_test, min_threshold_h) thres_range_h_cp = (cc_ct_gt["hori"]-thres_hori_cp_test, cc_ct_gt["hori"]+thres_hori_cp_test) thres_vert_cp_test = max( thres_l_cp_test, thres_xs_cp_test + h_iter * (thres_l_cp_test-thres_xs_cp_test)/LARGE_SIZE) min_threshold_v = THRESH_MIN_PIXEL / cc_h thres_vert_cp_test = max(thres_vert_cp_test, min_threshold_v) thres_range_v_cp = (cc_ct_gt["vert"]-thres_vert_cp_test, cc_ct_gt["vert"]+thres_vert_cp_test) if (cc_ct["hori"] < thres_range_h_cp[0] or cc_ct["hori"] > thres_range_h_cp[1] or cc_ct["vert"] < thres_range_v_cp[0] or cc_ct["vert"] > thres_range_v_cp[1]): failed_crop.append({"fmt_iter": fmt_iter, "fmt_cmpr": fmt_cmpr, "w": w_iter, "h": h_iter, "ct_hori": cc_ct["hori"], "ct_vert": cc_ct["vert"], "valid_range_h": thres_range_h_cp, "valid_range_v": thres_range_v_cp}) its.image.write_image(img/255, img_name, True) # Print aspect ratio test results failed_image_number_for_aspect_ratio_test = len(failed_ar) if failed_image_number_for_aspect_ratio_test > 0: print "\nAspect ratio test summary" print "Images failed in the aspect ratio test:" print "Aspect ratio value: width / height" for fa in failed_ar: print "%s with %s %dx%d: %.3f;" % ( fa["fmt_iter"], fa["fmt_cmpr"], fa["w"], fa["h"], fa["ar"]), print "valid range: %.3f ~ %.3f" % ( fa["valid_range"][0], fa["valid_range"][1]) # Print FoV test results failed_image_number_for_fov_test = len(failed_fov) if failed_image_number_for_fov_test > 0: print "\nFoV test summary" print "Images failed in the FoV test:" for fov in failed_fov: print fov # Print crop test results failed_image_number_for_crop_test = len(failed_crop) if failed_image_number_for_crop_test > 0: print "\nCrop test summary" print "Images failed in the crop test:" print "Circle center position, (horizontal x vertical), listed", print "below is relative to the image center." for fc in failed_crop: print "%s with %s %dx%d: %.3f x %.3f;" % ( fc["fmt_iter"], fc["fmt_cmpr"], fc["w"], fc["h"], fc["ct_hori"], fc["ct_vert"]), print "valid horizontal range: %.3f ~ %.3f;" % ( fc["valid_range_h"][0], fc["valid_range_h"][1]), print "valid vertical range: %.3f ~ %.3f" % ( fc["valid_range_v"][0], fc["valid_range_v"][1]) assert failed_image_number_for_aspect_ratio_test == 0 assert failed_image_number_for_fov_test == 0 if level3_device: assert failed_image_number_for_crop_test == 0 def measure_aspect_ratio(img, raw_avlb, img_name, debug): """Measure the aspect ratio of the black circle in the test image. Args: img: Numpy float image array in RGB, with pixel values in [0,1]. raw_avlb: True: raw capture is available; False: raw capture is not available. img_name: string with image info of format and size. debug: boolean for whether in debug mode. Returns: aspect_ratio: aspect ratio number in float. cc_ct: circle center position relative to the center of image. (circle_w, circle_h): tuple of the circle size """ size = img.shape img *= 255 # Gray image img_gray = 0.299*img[:, :, 2] + 0.587*img[:, :, 1] + 0.114*img[:, :, 0] # otsu threshold to binarize the image _, img_bw = cv2.threshold(np.uint8(img_gray), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # connected component cv2_version = cv2.__version__ if cv2_version.startswith("2.4."): contours, hierarchy = cv2.findContours(255-img_bw, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) elif cv2_version.startswith("3.2."): _, contours, hierarchy = cv2.findContours(255-img_bw, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Check each component and find the black circle min_cmpt = size[0] * size[1] * 0.005 max_cmpt = size[0] * size[1] * 0.35 num_circle = 0 aspect_ratio = 0 for ct, hrch in zip(contours, hierarchy[0]): # The radius of the circle is 1/3 of the length of the square, meaning # around 1/3 of the area of the square # Parental component should exist and the area is acceptable. # The coutour of a circle should have at least 5 points child_area = cv2.contourArea(ct) if (hrch[3] == -1 or child_area < min_cmpt or child_area > max_cmpt or len(ct) < 15): continue # Check the shapes of current component and its parent child_shape = component_shape(ct) parent = hrch[3] prt_shape = component_shape(contours[parent]) prt_area = cv2.contourArea(contours[parent]) dist_x = abs(child_shape["ctx"]-prt_shape["ctx"]) dist_y = abs(child_shape["cty"]-prt_shape["cty"]) # 1. 0.56*Parent"s width < Child"s width < 0.76*Parent"s width. # 2. 0.56*Parent"s height < Child"s height < 0.76*Parent"s height. # 3. Child"s width > 0.1*Image width # 4. Child"s height > 0.1*Image height # 5. 0.25*Parent"s area < Child"s area < 0.45*Parent"s area # 6. Child is a black, and Parent is white # 7. Center of Child and center of parent should overlap if (prt_shape["width"] * 0.56 < child_shape["width"] < prt_shape["width"] * 0.76 and prt_shape["height"] * 0.56 < child_shape["height"] < prt_shape["height"] * 0.76 and child_shape["width"] > 0.1 * size[1] and child_shape["height"] > 0.1 * size[0] and 0.30 * prt_area < child_area < 0.50 * prt_area and img_bw[child_shape["cty"]][child_shape["ctx"]] == 0 and img_bw[child_shape["top"]][child_shape["left"]] == 255 and dist_x < 0.1 * child_shape["width"] and dist_y < 0.1 * child_shape["height"]): # If raw capture is not available, check the camera is placed right # in front of the test page: # 1. Distances between parent and child horizontally on both side,0 # dist_left and dist_right, should be close. # 2. Distances between parent and child vertically on both side, # dist_top and dist_bottom, should be close. if not raw_avlb: dist_left = child_shape["left"] - prt_shape["left"] dist_right = prt_shape["right"] - child_shape["right"] dist_top = child_shape["top"] - prt_shape["top"] dist_bottom = prt_shape["bottom"] - child_shape["bottom"] if (abs(dist_left-dist_right) > 0.05 * child_shape["width"] or abs(dist_top-dist_bottom) > 0.05 * child_shape["height"]): continue # Calculate aspect ratio aspect_ratio = float(child_shape["width"]) / child_shape["height"] circle_ctx = child_shape["ctx"] circle_cty = child_shape["cty"] circle_w = float(child_shape["width"]) circle_h = float(child_shape["height"]) cc_ct = {"hori": float(child_shape["ctx"]-size[1]/2) / circle_w, "vert": float(child_shape["cty"]-size[0]/2) / circle_h} num_circle += 1 # If more than one circle found, break if num_circle == 2: break if num_circle == 0: its.image.write_image(img/255, img_name, True) print "No black circle was detected. Please take pictures according", print "to instruction carefully!\n" assert num_circle == 1 if num_circle > 1: its.image.write_image(img/255, img_name, True) print "More than one black circle was detected. Background of scene", print "may be too complex.\n" assert num_circle == 1 # draw circle center and image center, and save the image line_width = max(1, max(size)/500) move_text_dist = line_width * 3 cv2.line(img, (circle_ctx, circle_cty), (size[1]/2, size[0]/2), (255, 0, 0), line_width) if circle_cty > size[0]/2: move_text_down_circle = 4 move_text_down_image = -1 else: move_text_down_circle = -1 move_text_down_image = 4 if circle_ctx > size[1]/2: move_text_right_circle = 2 move_text_right_image = -1 else: move_text_right_circle = -1 move_text_right_image = 2 # circle center text_circle_x = move_text_dist * move_text_right_circle + circle_ctx text_circle_y = move_text_dist * move_text_down_circle + circle_cty cv2.circle(img, (circle_ctx, circle_cty), line_width*2, (255, 0, 0), -1) cv2.putText(img, "circle center", (text_circle_x, text_circle_y), cv2.FONT_HERSHEY_SIMPLEX, line_width/2.0, (255, 0, 0), line_width) # image center text_imgct_x = move_text_dist * move_text_right_image + size[1]/2 text_imgct_y = move_text_dist * move_text_down_image + size[0]/2 cv2.circle(img, (size[1]/2, size[0]/2), line_width*2, (255, 0, 0), -1) cv2.putText(img, "image center", (text_imgct_x, text_imgct_y), cv2.FONT_HERSHEY_SIMPLEX, line_width/2.0, (255, 0, 0), line_width) if debug: its.image.write_image(img/255, img_name, True) print "Aspect ratio: %.3f" % aspect_ratio print "Circle center position wrt to image center:", print "%.3fx%.3f" % (cc_ct["vert"], cc_ct["hori"]) return aspect_ratio, cc_ct, (circle_w, circle_h) def component_shape(contour): """Measure the shape for a connected component in the aspect ratio test. Args: contour: return from cv2.findContours. A list of pixel coordinates of the contour. Returns: The most left, right, top, bottom pixel location, height, width, and the center pixel location of the contour. """ shape = {"left": np.inf, "right": 0, "top": np.inf, "bottom": 0, "width": 0, "height": 0, "ctx": 0, "cty": 0} for pt in contour: if pt[0][0] < shape["left"]: shape["left"] = pt[0][0] if pt[0][0] > shape["right"]: shape["right"] = pt[0][0] if pt[0][1] < shape["top"]: shape["top"] = pt[0][1] if pt[0][1] > shape["bottom"]: shape["bottom"] = pt[0][1] shape["width"] = shape["right"] - shape["left"] + 1 shape["height"] = shape["bottom"] - shape["top"] + 1 shape["ctx"] = (shape["left"]+shape["right"])/2 shape["cty"] = (shape["top"]+shape["bottom"])/2 return shape if __name__ == "__main__": main()