我们从Python开源项目中，提取了以下17个代码示例，用于说明如何使用cv2.FONT_HERSHEY_COMPLEX。
def test_detector(): print('test detector:') # load model detector = Detector('SeetaFaceEngine/model/seeta_fd_frontal_v1.0.bin') detector.set_min_face_size(30) image_color = cv2.imread('data/chloecalmon.png', cv2.IMREAD_COLOR) image_gray = cv2.cvtColor(image_color, cv2.COLOR_BGR2GRAY) faces = detector.detect(image_gray) for i, face in enumerate(faces): print('({0},{1},{2},{3}) score={4}'.format(face.left, face.top, face.right, face.bottom, face.score)) cv2.rectangle(image_color, (face.left, face.top), (face.right, face.bottom), (0,255,0), thickness=2) cv2.putText(image_color, str(i), (face.left, face.bottom),cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), thickness=1) cv2.imshow('test', image_color) cv2.waitKey(0) detector.release()
def draw_text(self, image, text, size, color, position): ''' Zeichnet einen Text auf das Bild. Parameter --------- text : String Anzuzeigender Text size : Integer Groesse des Textes color : Tupel Farbe des Textes >> (255,0,0) position : Tupel Position des Textes >> (x,y) ''' if imutils.is_cv2(): cv2.putText(image, text, position, cv2.FONT_HERSHEY_COMPLEX, size, color, 2, cv2.CV_AA) elif imutils.is_cv3(): cv2.putText(image, text, position, cv2.FONT_HERSHEY_COMPLEX, size, color, 2, cv2.LINE_AA)
def draw_all_detection(im_array, detections, class_names, scale, cfg, threshold=1e-1): """ visualize all detections in one image :param im_array: [b=1 c h w] in rgb :param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ] :param class_names: list of names in imdb :param scale: visualize the scaled image :return: """ import cv2 import random color_white = (255, 255, 255) im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS) # change to bgr im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) for j, name in enumerate(class_names): if name == '__background__': continue color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color dets = detections[j] for det in dets: bbox = det[:4] * scale score = det[-1] if score < threshold: continue bbox = map(int, bbox) cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=2) cv2.putText(im, '%s %.3f' % (class_names[j], score), (bbox[0], bbox[1] + 10), color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5) return im
def draw_all_detection(im_array, detections, class_names, scale): """ visualize all detections in one image :param im_array: [b=1 c h w] in rgb :param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ] :param class_names: list of names in imdb :param scale: visualize the scaled image :return: """ import cv2 import random color_white = (255, 255, 255) im = image.transform_inverse(im_array, config.PIXEL_MEANS) # change to bgr im = cv2.cvtColor(im, cv2.cv.CV_RGB2BGR) for j, name in enumerate(class_names): if name == '__background__': continue color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color dets = detections[j] for det in dets: bbox = det[:4] * scale score = det[-1] bbox = map(int, bbox) cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=2) cv2.putText(im, '%s %.3f' % (class_names[j], score), (bbox[0], bbox[1] + 10), color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5) return im
def draw_boxes(im, dets, classes, scale = 1.0): color_white = (255, 255, 255) for cls_idx, cls_name in enumerate(classes): cls_dets = dets[cls_idx] for det in cls_dets: bbox = det[:4] * scale bbox = map(int, bbox) color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=3) if cls_dets.shape[1] == 5: score = det[-1] cv2.putText(im, '%s %.3f' % (cls_name, score), (bbox[0], bbox[1]+10), color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=1, thickness=2) return im
def draw_all_detection(im, detections, class_names, scale = 1.0): """ visualize all detections in one image :param im_array: [b=1 c h w] in rgb :param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ] :param class_names: list of names in imdb :param scale: visualize the scaled image :return: """ import cv2 import random color_white = (255, 255, 255) # im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS) # change to bgr #im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) for j, name in enumerate(class_names): if name == '__background__': continue color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color dets = detections[j] for det in dets: bbox = det[:4] * scale score = det[-1] # if score < threshold: # continue bbox = map(int, bbox) cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=2) cv2.putText(im, '%s %.3f' % (class_names[j], score), (bbox[0], bbox[1] + 10), color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5) return im
def draw_result(img, result): for i in range(len(result)): left = int(result[i][1]) right = int(result[i][2]) top = int(result[i][3]) bot = int(result[i][4]) c = i%3 color = 200*(c==0), 200*(c==1), 200*(c==2) cv2.rectangle(img, (left, top), (right, bot), (color), 5) cv2.rectangle(img, (left, top + 20), (right, top+1), (color), -1) cv2.putText(img, result[i][0] + ' : %.2f' % result[i][5], (left+ 15, top -7 + 20), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1, cv2.CV_AA)
def draw_all_detection(im_array, detections, class_names, scale, cfg, threshold=0.1): """ visualize all detections in one image :param im_array: [b=1 c h w] in rgb :param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ] :param class_names: list of names in imdb :param scale: visualize the scaled image :return: """ import cv2 import random color_white = (255, 255, 255) im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS) # change to bgr im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) for j, name in enumerate(class_names): if name == '__background__': continue color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color dets = detections[j] for det in dets: bbox = det[:4] * scale score = det[-1] if score < threshold: continue bbox = map(int, bbox) cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=2) cv2.putText(im, '%s %.3f' % (class_names[j], score), (bbox[0], bbox[1] + 10), color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5) return im
