def capture_samples(frame): global tick tick2 = cv2.getTickCount() t = (tick2 - tick) / freq * 1000 if t > capture_threshold: tick = tick2 capture(frame)
def main(): print('Creating video capture') cap = cv2.VideoCapture(1) while cap.isOpened(): timer = cv2.getTickCount() have_frame, showFrame = cap.read() if have_frame: pipeline.process(showFrame) extra_processing(showFrame) cv2.putText(showFrame, "FPS : " + str(int(cv2.getTickFrequency() / (cv2.getTickCount() - timer))), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2) cv2.imshow("match?", showFrame) cv2.waitKey(1) print('Capture closed')
def get(self): current_tick = cv2.getTickCount() different_time = (current_tick - self._start_tick) * self._freq self._start_tick = current_tick self._difftimes.append(different_time) fps = 1000.0 / (sum(self._difftimes) / len(self._difftimes)) fps_rounded = round(fps, 2) return fps_rounded
import numpy as np def access_pixels(image): print(image.shape) height = image.shape[0] width = image.shape[1] channels = image.shape[2] print("width:%s,height:%s,channels:%s" % (width, height, channels)) for row in range(height): for col in range(width): for c in range(channels): pv = image[row, col, c] image[row, col, c] = 255 - pv cv.imshow("pixels_demo", image) def creat_image(): img = np.zeros([400, 400, 3], np.uint8) img[:, :, 0] = np.ones([400, 400]) * 255 cv.imshow("new image", img) src = cv.imread("C:/Users/32936/Desktop/2/1.jpg") t1 = cv.getTickCount() creat_image() t2 = cv.getTickCount() print("time%s" % ((t2 - t1) / cv.getTickFrequency())) cv.waitKey(0) cv.destroyAllWindows()
def clock(): return cv.getTickCount() / cv.getTickFrequency()
def videoCap(): # pytesseract.pytesseract.tesseract_cmd = "C:/Program Files/Tesseract-OCR/tesseract" cap = cv2.VideoCapture( 'C:/Users/jeawa/Desktop/project/LPR_Project/project/video/test_car.mp4' ) count = 0 before_chars = "" while True: el = cv2.getTickCount() fps = cap.get(5) ret, img_ori = cap.read() height, width, channel = img_ori.shape # 높이, 너비, 채널 확보 gray = cv2.cvtColor(img_ori, cv2.COLOR_BGR2GRAY) # img_ori = cv2.imread('LPR_Project/project/image/3.jpg') # 이미지 불러오기 # gray = cv2.cvtColor(img_ori, cv2.COLOR_BGR2GRAY) img_blurred = cv2.GaussianBlur(gray, ksize=(3, 3), sigmaX=0) # 노이즈 제거 img_thresh = cv2.adaptiveThreshold( img_blurred, maxValue=255.0, adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C, thresholdType=cv2.THRESH_BINARY_INV, blockSize=19, # 12 통과 15,20 C=9) contours, _ = cv2.findContours( # 윤곽선을 찾기 img_thresh, mode=cv2.RETR_LIST, # method=cv2.CHAIN_APPROX_TC89_KCOS # ) temp_result = np.zeros((height, width, channel), dtype=np.uint8) cv2.drawContours( temp_result, # 원본이미지 contours=contours, # contours 정보 contourIdx=-1, # -1 : 전체 color=(255, 255, 255), thickness=1) contours_dict = [] for contour in contours: x, y, w, h = cv2.boundingRect(contour) # 컴투어를 감싸는 사각형 # cv2.rectangle( # temp_result, # pt1=(x, y), # pt2=(x+w, y+h), # color=(255, 255, 255), # thickness=1 # ) contours_dict.append({ 'contour': contour, 'x': x, 'y': y, 'w': w, 'h': h, 'cx': x + (w / 2), # 중심 좌표 'cy': y + (h / 2) }) MIN_AREA, MAX_AREA = 100, 1000 # boundingRect(사각형)의 최소넓이 MIN_WIDTH, MIN_HEIGHT = 2, 8 # boundingRect의 최소 넓이, 높이 2, 8 MIN_RATIO, MAX_RATIO = 0.3, 0.9 # boundingRect의 가로 세로 비율 possible_contours = [] # 위의 조건의 만족하는 사각형 cnt = 0 for d in contours_dict: area = d['w'] * d['h'] # 가로 * 세로 = 면적 ratio = d['w'] / d['h'] # 가로 / 세로 = 비율 if MIN_AREA < area < MAX_AREA and d['w'] > MIN_WIDTH and d[ 'h'] > MIN_HEIGHT and MIN_RATIO < ratio < MAX_RATIO: d['idx'] = cnt # 조건의 맞는 값을 idx에 저장한다. cnt += 1 possible_contours.append(d) # possible_contour를 업데이트한다. # temp_result = np.zeros((height, width, channel), dtype=np.uint8) for d in possible_contours: # cv2.drawContours(temp_result, d['contour'], -1, (255, 255, 255)) cv2.rectangle(temp_result, pt1=(d['x'], d['y']), pt2=(d['x'] + d['w'], d['y'] + d['h']), color=(0, 0, 255), thickness=1) MAX_DIAG_MULTIPLYER = 4 # 대각선의 5배 안에 있어야함 MAX_ANGLE_DIFF = 12.0 # 12.0 #세타의 최대값 12, 0.7, 0.5, 0.6, 3 MAX_AREA_DIFF = 0.3 # 0.5 #면적의 차이 MAX_WIDTH_DIFF = 0.5 # 너비차이 MAX_HEIGHT_DIFF = 0.6 # 높이차이 MIN_N_MATCHED = 4 # 3 #위의 조건이 3개 미만이면 뺀다 def find_chars(contour_list): # matched_result_idx = [] # idx 값 저장 for d1 in contour_list: matched_contours_idx = [] for d2 in contour_list: if d1['idx'] == d2['idx']: # d1 과 d2가 같으면 컨티뉴 continue dx = abs(d1['cx'] - d2['cx']) dy = abs(d1['cy'] - d2['cy']) diagonal_length1 = np.sqrt(d1['w']**2 + d1['h']**2) distance = np.linalg.norm( np.array([d1['cx'], d1['cy']]) - np.array([d2['cx'], d2['cy']])) # 대각선 길이 if dx == 0: angle_diff = 90 # 0일 때 각도 90도 (예외처리) else: angle_diff = np.degrees(np.arctan(dy / dx)) # 세타 구하기 area_diff = abs(d1['w'] * d1['h'] - d2['w'] * d2['h']) / ( d1['w'] * d1['h']) # 면적 비율 width_diff = abs(d1['w'] - d2['w']) / d1['w'] # 너비비율 height_diff = abs(d1['h'] - d2['h']) / d1['h'] # 높이비율 # 조건들 if distance < diagonal_length1 * MAX_DIAG_MULTIPLYER \ and angle_diff < MAX_ANGLE_DIFF and area_diff < MAX_AREA_DIFF \ and width_diff < MAX_WIDTH_DIFF and height_diff < MAX_HEIGHT_DIFF: # d2만 넣었기 때문에 마지막으로 d1을 넣은다 matched_contours_idx.append(d2['idx']) # append this contour matched_contours_idx.append(d1['idx']) if len(matched_contours_idx) < MIN_N_MATCHED: # 3개 이하이면 번호판 x continue matched_result_idx.append(matched_contours_idx) # 최종 후보군 unmatched_contour_idx = [] # 최종후보군이 아닌 애들 for d4 in contour_list: if d4['idx'] not in matched_contours_idx: unmatched_contour_idx.append(d4['idx']) unmatched_contour = np.take(possible_contours, unmatched_contour_idx) # recursive recursive_contour_list = find_chars(unmatched_contour) for idx in recursive_contour_list: matched_result_idx.append(idx) # 번호판 이외의 값을 재정의 break return matched_result_idx result_idx = find_chars(possible_contours) matched_result = [] for idx_list in result_idx: matched_result.append(np.take(possible_contours, idx_list)) # visualize possible contours temp_result = np.zeros((height, width, channel), dtype=np.uint8) for r in matched_result: for d in r: cv2.rectangle(temp_result, pt1=(d['x'], d['y']), pt2=(d['x'] + d['w'], d['y'] + d['h']), color=(0, 0, 255), thickness=1) PLATE_WIDTH_PADDING = 1.2 # 1.3 PLATE_HEIGHT_PADDING = 1.5 # 1.5 MIN_PLATE_RATIO = 3 MAX_PLATE_RATIO = 7 #10 plate_imgs = [] plate_infos = [] for i, matched_chars in enumerate(matched_result): sorted_chars = sorted(matched_chars, key=lambda x: x['cx']) # x방향으로 순차적으로 정렬 plate_cx = (sorted_chars[0]['cx'] + sorted_chars[-1]['cx']) / 2 # 센터 좌표 구하기 plate_cy = (sorted_chars[0]['cy'] + sorted_chars[-1]['cy']) / 2 plate_width = (sorted_chars[-1]['x'] + sorted_chars[-1]['w'] - sorted_chars[0]['x']) * PLATE_WIDTH_PADDING # 너비 # sum_height = 0 # for d in sorted_chars: # sum_height += d['h'] # plate_height = int(sum_height / len(sorted_chars) # * PLATE_HEIGHT_PADDING) # 높이 plate_height = (sorted_chars[-1]['y'] - sorted_chars[0]['y'] + sorted_chars[-1]['h']) * PLATE_HEIGHT_PADDING # triangle_height = sorted_chars[-1]['cy'] - sorted_chars[0]['cy'] triangle_hypotenus = np.linalg.norm( np.array([sorted_chars[0]['cx'], sorted_chars[0]['cy']]) - np.array([sorted_chars[-1]['cx'], sorted_chars[-1]['cy']])) angle = np.degrees(np.arcsin(triangle_height / triangle_hypotenus)) rotation_matrix = cv2.getRotationMatrix2D(center=(plate_cx, plate_cy), angle=angle, scale=1.0) # 회전 img_rotated = cv2.warpAffine(img_thresh, M=rotation_matrix, dsize=(width, height)) img_cropped = cv2.getRectSubPix(img_rotated, patchSize=(int(plate_width), int(plate_height)), center=(int(plate_cx), int(plate_cy))) if img_cropped.shape[1] / img_cropped.shape[ 0] < MIN_PLATE_RATIO or img_cropped.shape[ 1] / img_cropped.shape[ 0] < MIN_PLATE_RATIO > MAX_PLATE_RATIO: continue plate_imgs.append(img_cropped) plate_infos.append({ 'x': int(plate_cx - plate_width / 2), 'y': int(plate_cy - plate_height / 2), 'w': int(plate_width), 'h': int(plate_height) }) for i, plate_img in enumerate(plate_imgs): plate_img = cv2.resize(plate_img, dsize=(0, 0), fx=1.6, fy=1.6) _, plate_img = cv2.threshold(plate_img, thresh=0.0, maxval=255.0, type=cv2.THRESH_BINARY | cv2.THRESH_OTSU) # plt.imshow(plate_img, cmap='gray') # plt.show() # find contours again (same as above) contours, _ = cv2.findContours(plate_img, mode=cv2.RETR_LIST, method=cv2.CHAIN_APPROX_SIMPLE) plate_min_x, plate_min_y = plate_img.shape[1], plate_img.shape[ 0] plate_max_x, plate_max_y = 0, 0 for contour in contours: x, y, w, h = cv2.boundingRect(contour) area = w * h ratio = w / h if area > MIN_AREA \ and w > MIN_WIDTH and h > MIN_HEIGHT \ and MIN_RATIO < ratio < MAX_RATIO: if x < plate_min_x: plate_min_x = x if y < plate_min_y: plate_min_y = y if x + w > plate_max_x: plate_max_x = x + w if y + h > plate_max_y: plate_max_y = y + h img_result = plate_img[plate_min_y:plate_max_y, plate_min_x:plate_max_x] img_result = cv2.GaussianBlur(img_result, ksize=(3, 3), sigmaX=0) _, img_result = cv2.threshold(img_result, thresh=0.0, maxval=255.0, type=cv2.THRESH_BINARY | cv2.THRESH_OTSU) img_result = cv2.copyMakeBorder(img_result, top=10, bottom=10, left=20, right=10, borderType=cv2.BORDER_CONSTANT, value=(0, 0, 0)) chars = pytesseract.image_to_string(img_result, lang='kor', config='--psm 7 --oem 0') result_chars = '' has_digit = False # <= ord('힣') for c in chars: if \ + ord('가') == ord(c) or \ + ord('나') == ord(c) or \ + ord('다') == ord(c) or \ + ord('라') == ord(c) or \ + ord('마') == ord(c) or \ + ord('거') == ord(c) or \ + ord('너') == ord(c) or \ + ord('더') == ord(c) or \ + ord('러') == ord(c) or \ + ord('머') == ord(c) or \ + ord('버') == ord(c) or \ + ord('서') == ord(c) or \ + ord('어') == ord(c) or \ + ord('저') == ord(c) or \ + ord('고') == ord(c) or \ + ord('노') == ord(c) or \ + ord('도') == ord(c) or \ + ord('로') == ord(c) or \ + ord('모') == ord(c) or \ + ord('보') == ord(c) or \ + ord('소') == ord(c) or \ + ord('오') == ord(c) or \ + ord('조') == ord(c) or \ + ord('구') == ord(c) or \ + ord('누') == ord(c) or \ + ord('두') == ord(c) or \ + ord('루') == ord(c) or \ + ord('무') == ord(c) or \ + ord('부') == ord(c) or \ + ord('수') == ord(c) or \ + ord('우') == ord(c) or \ + ord('주') == ord(c) or \ + ord('아') == ord(c) or \ + ord('바') == ord(c) or \ + ord('사') == ord(c) or \ + ord('자') == ord(c) or \ + ord('배') == ord(c) or \ + ord('하') == ord(c) or \ + ord('허') == ord(c) or \ + ord('호') == ord(c) or c.isdigit(): if c.isdigit(): has_digit = True result_chars += c # print(result_chars) # print(len(result_chars)) if result_chars == "": pass else: if before_chars == result_chars and 6 < len(result_chars) < 9: count += 1 else: before_chars = result_chars count = 0 if count == 1: print(result_chars) count = 0 before_chars = "" # print(fps + " : 프레임 영상입니다.") print(fps) a = np.hstack((img_ori, temp_result)) cv2.imshow("Go", a) if cv2.waitKey(42) == ord('q'): break e2 = cv2.getTickCount() time = (e2 - el) / cv2.getTickFrequency() print(time) # print("1프레임 : 1초당 처리속도는 " + time + "입니다.") cap.release() cv2.destroyAllWindows()
def main(): global drone prediction_class = ObjectDetectionPredict(model_name=MODEL_NAME) try: drone.subscribe(drone.EVENT_FLIGHT_DATA, handler) drone.connect() drone.wait_for_connection(60.0) container = av.open(drone.get_video_stream()) # skip first 300 frames frame_skip = 1000 bbox = (287, 23, 86, 320) c = 0 # drone.takeoff() # sleep(5) th = threading.Thread(target=droneControl) th.start() landflag = False while True: print("******************\nNEW WHILE\n******************** ") for frame in container.decode(video=0): if 0 < frame_skip: frame_skip = frame_skip - 1 continue if c < 2: c += 1 continue else: c = 0 pil_im = Image.fromarray(np.array(frame.to_image())) print("pil_im :", pil_im) timer = cv2.getTickCount() scores, classes, img, boxes = prediction_class.predict_single_image( pil_im) opencvImage = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) # Calculate Frames per second (FPS) fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer) print('FPS : ' + str(float(fps))) cv2.putText(opencvImage, "FPS : " + str(int(fps)), (50, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2) cv2.imshow('opencvImage', opencvImage) if cv2.waitKey(1) & 0xFF == ord('q'): landflag = True cv2.destroyAllWindows() break if landflag: print('down') drone.down(50) sleep(3) drone.land() sleep(1) break # prediction_class.sess.close() print('down again') drone.land() sleep(1) except Exception as ex: exc_type, exc_value, exc_traceback = sys.exc_info() traceback.print_exception(exc_type, exc_value, exc_traceback) print(ex) finally: drone.quit() cv2.destroyAllWindows()
# Initialize video stream videostream = VideoStream(resolution=(imW, imH)).start() time.sleep(1) def get_color(name): if name == 'car': return 10, 255, 0 elif name == 'person': return 0, 10, 255 elif name == 'cycle': return 255, 10, 0 return 0, 127, 255 tick = cv2.getTickCount() out_file = open("out/%s.csv" % datetime.now().strftime('%Y%m%d-%H%M%S'), 'a') def capture_samples(frame): global tick tick2 = cv2.getTickCount() t = (tick2 - tick) / freq * 1000 if t > capture_threshold: tick = tick2 capture(frame) def capture(frame): filename = "samples/frame-%s.jpg" % datetime.now().strftime('%Y%m%d-%H%M%S')
from cv2 import cv2 import numpy as np ''' #模板 e1 = cv2.getTickCount() # 你的代码 e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() print(time) ''' img1 = cv2.imread('img/bird.jpg') e1 = cv2.getTickCount() for i in range(5, 49, 2): img1 = cv2.medianBlur(img1, i) e2 = cv2.getTickCount() t = (e2 - e1) / cv2.getTickFrequency() print(t)
def __init__(self, buffer_len=1): self._start_tick = cv2.getTickCount() self._freq = 1000.0 / cv2.getTickFrequency() # deque = collections.deque self._difftimes = deque(maxlen=buffer_len)
bbox = (287, 23, 86, 320) # Uncomment the line below to select a different bounding box bbox = cv2.selectROI(frame, False) # Initialize tracker with first frame and bounding box ok = tracker.init(frame, bbox) while True: # Read a new frame ok, frame = video.read() if not ok: break # Start timer timer = cv2.getTickCount() # Update tracker ok, bbox = tracker.update(frame) # Calculate Frames per second (FPS) fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer) # Draw bounding box if ok: # Tracking success p1 = (int(bbox[0]), int(bbox[1])) p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3])) cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1) else: # Tracking failure
from cv2 import cv2 as cv import numpy as np import time img = cv.imread("star.jpg") e1 = cv.getTickCount() # 也可用python的time模块计数 # 估量代码执行效率 for i in range(5, 49, 2): img = cv.medianBlur(img, i) e2 = cv.getTickCount() time = (e2 - e1) / cv.getTickFrequency() print(time) # 查看是否开启优化 ret = cv.useOptimized() print(ret) # 魔法命令%time(使用ipython)