def detect(img_path, model_path):
file_id = utils.get_file_id(img_path)
img, img_raw, scale,scale_ = read_img(img_path)
print("dim1",img_raw.ndim)
head_detector = Head_Detector_VGG16(ratios=[1], anchor_scales=[2,4])
trainer = Head_Detector_Trainer(head_detector).cuda()
trainer.load(model_path)
img = at.totensor(img)
img = img[None, : ,: ,:]
img = img.cuda().float()
st = time.time()
pred_bboxes_, _ = head_detector.predict(img, scale, mode='evaluate', thresh=THRESH)
et = time.time()
tt = et - st
print ("[INFO] Head detection over. Time taken: {:.4f} s".format(tt))
for i in range(pred_bboxes_.shape[0]):
print(i)
ymin, xmin, ymax, xmax = pred_bboxes_[i,:]
print(ymin, xmin, ymax, xmax)
image_raw=Image.fromarray(np.uint8(img_raw))
utils.draw_bounding_box_on_image(image_raw,ymin*scale_, xmin*scale_, ymax*scale_, xmax*scale_)
img_raw=np.array(image_raw)
image_raw=Image.fromarray(np.uint8(img_raw))
if SAVE_FLAG == 1:
#image_raw.save('/home/hx/Project/FCHD-Fully-Convolutional-Head-Detector-master/'+file_id+'_1.png')
image_raw.save(write_path+'/'+os.path.basename(img_path))
frame_end = cv2.imread(write_path+'/'+os.path.basename(img_path))
cv2.imshow("frame_end",frame_end)
key_end = cv2.waitKey(1) & 0xFF
else:
image_raw.show()
def detect(img_read, model_path, head_detector):
img, img_raw, scale, scale_ = read_img(img_read)
#print("dim1",img_raw.ndim)
img = at.totensor(img)
img = img[None, :, :, :]
img = img.cuda().float()
st = time.time()
pred_bboxes_, _ = head_detector.predict(img,
scale,
mode='evaluate',
thresh=THRESH)
et = time.time()
tt = et - st
thispic_count = 0
#print ("[INFO] Head detection over. Time taken: {:.4f} s".format(tt))
for i in range(pred_bboxes_.shape[0]):
thispic_count = i
#print(i)
ymin, xmin, ymax, xmax = pred_bboxes_[i, :]
#print(ymin, xmin, ymax, xmax)
image_raw = Image.fromarray(np.uint8(img_raw))
utils.draw_bounding_box_on_image(image_raw, ymin * scale_,
xmin * scale_, ymax * scale_,
xmax * scale_)
img_raw = np.array(image_raw)
image_raw = Image.fromarray(np.uint8(img_raw))
if SAVE_FLAG == 1:
pass
#image_raw.save('/home/hx/Project/FCHD-Fully-Convolutional-Head-Detector-master/'+file_id+'_1.png')
#image_raw.save(write_path+'/'+os.path.basename(img_path))
else:
image_raw.show()
return thispic_count
def detect(img_read, write_path, head_detector, img_path):
global show
img, img_raw, scale, scale_ = read_img(img_read)
print("dim1", img_raw.ndim)
img_raw_2 = img_raw.copy()
img = at.totensor(img)
img = img[None, :, :, :]
img = img.cuda().float()
st = time.time()
pred_bboxes_, _ = head_detector.predict(img,
scale,
mode='evaluate',
thresh=THRESH)
et = time.time()
tt = et - st
print("[INFO] Head detection over. Time taken: {:.4f} s".format(tt))
for i in range(pred_bboxes_.shape[0]):
print(i)
ymin, xmin, ymax, xmax = pred_bboxes_[i, :]
print(ymin, xmin, ymax, xmax)
image_raw = Image.fromarray(np.uint8(img_raw))
image_raw_2 = Image.fromarray(np.uint8(img_raw_2))
utils.draw_bounding_box_on_image(image_raw, ymin * scale_,
xmin * scale_, ymax * scale_,
xmax * scale_)
#cv2.rectangle(image_raw_2, (int(xmin*scale_), int(ymin*scale_)), (int(xmax*scale_), int(ymax*scale_)), 255,cv2.FILLED)
#draw_rect = ImageDraw.Draw(image_raw_2)
#draw_rect.rectangle((xmin*scale_, ymin*scale_, xmax*scale_, ymax*scale_), 'red')
img_raw = np.array(image_raw)
img_raw_2 = np.array(image_raw_2)
image_raw = Image.fromarray(np.uint8(img_raw))
image_raw_2 = Image.fromarray(np.uint8(img_raw_2))
image_raw = cv2.cvtColor(np.asarray(image_raw), cv2.COLOR_RGB2BGR)
image_raw_2 = cv2.cvtColor(np.asarray(image_raw_2), cv2.COLOR_RGB2BGR)
if SAVE_FLAG == 1:
#image_raw.save('/home/hx/Project/FCHD-Fully-Convolutional-Head-Detector-master/'+file_id+'_1.png')
#image_raw.save(write_path+'/'+os.path.basename(img_path))
#frame_end = cv2.imread(write_path+'/'+os.path.basename(img_path))
#cv2.imshow("frame_end",frame_end)
cv2.imwrite(write_path + '/' + os.path.basename(img_path), image_raw)
cv2.imshow("image_raw", image_raw)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
show = 0
else:
image_raw.show()
def detect(img_read, write_path, head_detector, img_path, face_detect):
global show
global people_num_detect
global best_frame
people_num_cache = 0
img, img_raw, scale, scale_ = read_img(img_read)
#print("dim1",img_raw.ndim)
img_raw_2 = img_raw.copy()
img = at.totensor(img)
img = img[None, :, :, :]
img = img.cuda().float()
st = time.time()
pred_bboxes_, _ = head_detector.predict(img,
scale,
mode='evaluate',
thresh=THRESH)
et = time.time()
tt = et - st
print("[INFO] Head detection over. Time taken: {:.4f} s".format(tt))
for i in range(pred_bboxes_.shape[0]):
#print(i)
people_num_cache = i + 1
ymin, xmin, ymax, xmax = pred_bboxes_[i, :]
#print(ymin, xmin, ymax, xmax)
image_raw = Image.fromarray(np.uint8(img_raw))
image_raw_2 = Image.fromarray(np.uint8(img_raw_2))
utils.draw_bounding_box_on_image(image_raw, ymin * scale_,
xmin * scale_, ymax * scale_,
xmax * scale_)
#cv2.rectangle(image_raw_2, (int(xmin*scale_), int(ymin*scale_)), (int(xmax*scale_), int(ymax*scale_)), 255,cv2.FILLED)
draw_rect = ImageDraw.Draw(image_raw_2)
draw_rect.rectangle(
(xmin * scale_, ymin * scale_, xmax * scale_, ymax * scale_),
'red')
img_raw = np.array(image_raw)
img_raw_2 = np.array(image_raw_2)
image_raw = Image.fromarray(np.uint8(img_raw))
image_raw_2 = Image.fromarray(np.uint8(img_raw_2))
image_raw = cv2.cvtColor(np.asarray(image_raw), cv2.COLOR_RGB2BGR)
image_raw_2 = cv2.cvtColor(np.asarray(image_raw_2), cv2.COLOR_RGB2BGR)
rects, landmarks = face_detect.detect_face(image_raw_2, 20)
people_num_cache += len(rects)
for (i, rect) in enumerate(rects):
cv2.rectangle(image_raw, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 2)
if SAVE_FLAG == 1:
#image_raw.save('/home/hx/Project/FCHD-Fully-Convolutional-Head-Detector-master/'+file_id+'_1.png')
#image_raw.save(write_path+'/'+os.path.basename(img_path))
#frame_end = cv2.imread(write_path+'/'+os.path.basename(img_path))
#cv2.imshow("frame_end",frame_end)
cv2.imwrite(write_path + '/' + os.path.basename(img_path), image_raw)
cv2.imshow("image_raw", image_raw)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
show = 0
print("best frame is **********" + best_frame +
" num is *************" + str(people_num_detect))
else:
image_raw.show()
return people_num_cache