def load_image(image_path):
try:
thresh = 0.24
hier_thresh = 0.5
areas = []
coords = []
img = pil_image.open(image_path)
img = img.convert('RGB')
width, height = img.size
area = width * height
outputs = pyyolo.test(image_path, thresh, hier_thresh, 0)
print outputs
for out in outputs:
if out['class'] == 'car':
if out['prob'] < 0.65:
continue
x1 = out['left']
x2 = out['right']
y1 = out['top']
y2 = out['bottom']
if abs((x2 - x1) * (y2 - y1)) < 0.3 * area:
continue
coords.append([x1, y1, x2, y2])
areas.append(abs((x2 - x1) * (y2 - y1)))
if not coords:
return False
car_coord = coords[areas.index(max(areas))]
scale_width = int((car_coord[2] - car_coord[0]) * 0.1)
scale_height = int((car_coord[3] - car_coord[1]) * 0.1)
car_coord[0] = car_coord[0] - scale_width
car_coord[1] = car_coord[1] - scale_height
car_coord[2] = car_coord[2] + scale_width
car_coord[3] = car_coord[3] + scale_height
car_coord = (max(int(car_coord[0]), 2), max(int(car_coord[1]), 2),
min(int(car_coord[2]),
width - 2), min(int(car_coord[3]), height - 2))
crop_img = img.crop(car_coord)
return crop_img
except:
print("Load Error: %s" % image_path)
return False
def detect_yolo_pyyolo(img_arr, url='', classes=constants.hls_yolo_categories):
# from file
print('----- test original C using a file')
hash = hashlib.sha1()
hash.update(str(time.time()))
img_filename = hash.hexdigest()[:10] + 'pyyolo.jpg'
# img_filename = 'incoming.jpg'
cv2.imwrite(img_filename, img_arr)
outputs = pyyolo.test(img_filename, thresh, hier_thresh)
relevant_bboxes = []
for output in outputs:
print(output)
label = output['class']
xmin = output['left']
ymin = output['top']
xmax = output['right']
ymax = output['bottom']
item = {
'object': label,
'bbox': [xmin, ymin, xmax, ymax],
'confidence': '>' + str(thresh)
}
# item = {'object':label,'bbox':[xmin,ymin,xmax,ymax],'confidence':round(float(confidence),3)}
relevant_bboxes.append(item)
#not sure wht the diff is between second method and first
# camera
# print('----- test python API using a file')
# i = 1
# while i < 2:
# # ret_val, img = cam.read()
# img = cv2.imread(filename)
# img = img.transpose(2,0,1)
# c, h, w = img.shape[0], img.shape[1], img.shape[2]
# # print w, h, c
# data = img.ravel()/255.0
# data = np.ascontiguousarray(data, dtype=np.float32)
# outputs = pyyolo.detect(w, h, c, data, thresh, hier_thresh)
# for output in outputs:
# print(output)
# i = i + 1
# free model
pyyolo.cleanup()
return relevant_bboxes
def predict_sample_image():
# From file
print('----- test original C using a file')
outputs = pyyolo.test(filename, thresh, hier_thresh, 0)
for output in outputs:
print(output)
# Camera
print('----- test python API using a file')
i = 1
while i < 2:
# ret_val, img = cam.read()
orig_img = cv2.imread(filename)
c, h, w, data = prepare_img(orig_img)
outputs = pyyolo.detect(w, h, c, data, thresh, hier_thresh)
draw_bounding_boxes(orig_img, outputs)
cv2.imwrite("predicted.jpg", orig_img)
i = i + 1
def crop_image(image_path):
thresh = 0.24
hier_thresh = 0.5
areas = []
coords = []
im = cv2.imread(image_path)
height, width, _ = im.shape
area = width * height
outputs = pyyolo.test(image_path, thresh, hier_thresh, 0)
for out in outputs:
if out['class'] == 'car':
if out['prob'] < 0.65:
continue
x1 = out['left']
x2 = out['right']
y1 = out['top']
y2 = out['bottom']
if abs((x2 - x1) * (y2 - y1)) < 0.3 * area:
continue
coords.append([x1, y1, x2, y2])
areas.append(abs((x2 - x1) * (y2 - y1)))
if not coords:
os.remove(image_path)
return False
car_coord = coords[areas.index(max(areas))]
scale_width = int((car_coord[2] - car_coord[0]) * 0.05)
scale_height = int((car_coord[3] - car_coord[1]) * 0.05)
car_coord[0] = car_coord[0] - scale_width
car_coord[1] = car_coord[1] - scale_height
car_coord[2] = car_coord[2] + scale_width
car_coord[3] = car_coord[3] + scale_height
car_coord = [
max(int(car_coord[0]), 2),
max(int(car_coord[1]), 2), car_coord[2], car_coord[3]
]
new_image = im[car_coord[1]:car_coord[3], car_coord[0]:car_coord[2]]
cv2.imwrite(image_path, new_image)
def serve():
data = json.loads(request.data.decode('utf-8'))
print "\n"
print "[TIME] " + time.strftime("%d/%m/%Y") + " " + time.strftime(
"%H:%M:%S")
path_to_client_data = iof.assert_client_data_path(request)
image_data, num_valid_images = iof.download_images(data,
path_to_client_data)
out = list()
# print "[Classifying]...", num_valid_images, "images"
err_count = 0
for idx in image_data:
img_data = image_data[idx]
out.append(dict())
o = out[-1]
o["url"] = img_data["url"]
o["img_resize"] = img_data["img_resize"]
if img_data["path"] == None:
o["pred"] = None
continue
try:
o["pred"] = pyyolo.test(img_data["path"], thresh, hier_thresh, 0)
if len(o["pred"]) == 0:
o["pred"] = None
except Exception as e:
o["pred"] = None
err_count += 1
print "[ERROR] while classifiying image\n\tsrc:", o[
"url"], "\n\tError:", e
print "[+] Classified", num_valid_images - err_count, "/", num_valid_images, "images"
shutil.rmtree(path_to_client_data)
print "[+] Deleted downloaded images."
print "[+] Returning predictions..."
return jsonify(out)
def call_api(data):
print data
print "-" * 10
timestamp = str(int(time.time()))
outdir = api_dir_path + "/" + timestamp
os.makedirs(outdir)
image_data, num_valid_images = download_images(data, outdir)
print image_data
print "num_valid_images", num_valid_images
print "-" * 10
out = list()
err_count = 0
for idx in image_data:
img_data = image_data[idx]
out.append(dict())
o = out[-1]
o["url"] = img_data["url"]
o["img_resize"] = img_data["img_resize"]
if img_data["path"] == None:
o["pred"] = None
continue
try:
o["pred"] = pyyolo.test(img_data["path"], thresh, hier_thresh, 0)
if len(o["pred"]) == 0:
o["pred"] = None
except Exception as e:
o["pred"] = None
err_count += 1
print "[ERROR] while classifiying image\n\tsrc:", o[
"url"], "\n\tError:", e
print "[+] Classified", num_valid_images - err_count, "/", num_valid_images, "images"
shutil.rmtree(outdir)
print "[+] Deleted downloaded images."
print "[+] Returning predictions..."
return out
filename = '../annotation-tool/images/101323.jpg'
thresh = 0.24
hier_thresh = 0.5
#cam = cv2.VideoCapture(0)
#ret_val, img = cam.read()
#print(ret_val)
#if ret_val:
# ret_val = cv2.imwrite(filename,img)
# print(ret_val)
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
# from file
print('----- test original C using a file')
outputs = pyyolo.test(filename, thresh, hier_thresh, 0)
for output in outputs:
print(output)
# camera
print('----- test python API using a file')
i = 1
while i < 2:
# ret_val, img = cam.read()
img = cv2.imread(filename)
img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)
img = img.transpose(2, 0, 1)
c, h, w = img.shape[0], img.shape[1], img.shape[2]
# print w, h, c
data = img.ravel() / 255.0
data = np.ascontiguousarray(data, dtype=np.float32)
def get_local_pyyolo_results(img_arr,
url='',
classes=constants.hls_yolo_categories,
method='file'):
# from file
relevant_bboxes = []
if method == 'file':
print('----- test original C using a file')
hash = hashlib.sha1()
hash.update(str(time.time()))
img_filename = hash.hexdigest()[:10] + 'pyyolo.jpg'
# img_filename = 'incoming.jpg'
cv2.imwrite(img_filename, img_arr)
outputs = pyyolo.test(img_filename, thresh, hier_thresh)
#not sure what the diff is between this second method (pyyolo.detect) and first (pyyolo.test)
#except it uses array instead of file
# print('----- test python API using a file')
else:
i = 1 #wtf is this count for
while i < 2:
# ret_val, img = cam.read()
# img = cv2.imread(filename)
img = img_arr.transpose(2, 0, 1)
c, h, w = img.shape[0], img.shape[1], img.shape[2]
# print w, h, c
data = img.ravel() / 255.0
data = np.ascontiguousarray(data, dtype=np.float32)
print('calling pyyolo.detect')
try:
outputs = pyyolo.detect(w, h, c, data, thresh, hier_thresh)
except:
print('some trouble calling pyyolo detect,' +
str(sys.exc_info()[0]))
print('returned from pyyolo.detect')
for output in outputs:
print(output)
i = i + 1
# free model
for output in outputs:
print(output)
label = output['class']
if 'person' in label:
label = 'person' #convert 'person_wearing_red/blue_shirt' into just person
xmin = output['left']
ymin = output['top']
xmax = output['right']
ymax = output['bottom']
conf = output['prob']
item = {
'object': label,
'bbox': [xmin, ymin, xmax, ymax],
'confidence': round(conf, 4)
}
h, w = img_arr.shape[0:2]
frac = 5
cropped_arr = img_arr[h / frac:h - (h / frac), w / frac:w - (w / frac)]
dominant_color = imutils.dominant_colors(cropped_arr)
print('dominant color:' + str(dominant_color))
if dominant_color is not None:
item['details'] = {'color': dominant_color}
# item = {'object':label,'bbox':[xmin,ymin,xmax,ymax],'confidence':round(float(confidence),3)}
relevant_bboxes.append(item)
# pyyolo.cleanup()
return relevant_bboxes
weightfile = '../tiny-yolo.weights'
filename = darknet_path + '/data/person.jpg'
thresh = 0.24
hier_thresh = 0.5
cam = cv2.VideoCapture(-1)
ret_val, img = cam.read()
print(ret_val)
if ret_val:
ret_val = cv2.imwrite(filename,img)
print(ret_val)
pyyolo.init(darknet_path, datacfg, cfgfile, weightfile)
# from file
print('----- test original C using a file')
outputs = pyyolo.test(filename, thresh, hier_thresh)
for output in outputs:
print(output)
# camera
print('----- test python API using a file')
i = 1
while i < 2:
# ret_val, img = cam.read()
img = cv2.imread(filename)
img = img.transpose(2,0,1)
c, h, w = img.shape[0], img.shape[1], img.shape[2]
# print w, h, c
data = img.ravel()/255.0
data = np.ascontiguousarray(data, dtype=np.float32)
outputs = pyyolo.detect(w, h, c, data, thresh, hier_thresh)