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()
python类FONT_HERSHEY_COMPLEX的实例源码
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_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, 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, 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, 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_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_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, 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_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_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_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_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
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_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
