def detection():
if request.method == 'POST':
image_file = request.form['image_file']
if image_file:
filename = image_file
try:
detect_people(filename)
except Exception:
pass
return render_template('./result.html', image_file=filename)
def _pred_class(image, net, ln, idx, color=(0, 0, 255)):
# 検出実行
results = detect_people(image, net, ln, personIdx=idx)
# 結果をループ
for (i, (prob, bbox, centroid)) in enumerate(results):
# バウンディングボックスとセントロイド座標を抽出し、アノテーションの色を初期化
(startX, startY, endX, endY) = bbox
# (cX, cY) = centroid
# 外接箱を描く
cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)
text = f'{coco_label_name}: {round(prob*100, 1)}%'
y = startY - 10 if startY - 10 > 10 else startY + 10
cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX,
0.45, color, 2)
return image
def _pred_frame(frame, idx):
# 検出
results = detect_people(frame, net, ln, personIdx=idx)
# 最小のsocial distanceに違反するインデックスのセットを初期化するe
violate = set()
# 少なくとも2人の検出があることを確認します（ペアワイズ距離マップを計算するために必要です）
if len(results) >= 2:
# 結果からすべてのセントロイドを抽出し、すべてのペアのセントロイド間のユークリッド距離を計算する
centroids = np.array([r[2] for r in results])
D = dist.cdist(centroids, centroids, metric="euclidean")
# 距離行列の上三角形をループ
for i in range(0, D.shape[0]):
for j in range(i + 1, D.shape[1]):
# 2つのセントロイドのペア間の距離が設定されたピクセル数よりも小さいかどうかを確認
if D[i, j] < config.MIN_DISTANCE:
# 違反セットをセントロイドペアのインデックスで更新
violate.add(i)
violate.add(j)
# 結果をループ
for (i, (prob, bbox, centroid)) in enumerate(results):
# バウンディングボックスとセントロイド座標を抽出し、アノテーションの色を初期化
(startX, startY, endX, endY) = bbox
(cX, cY) = centroid
color = (0, 255, 0)
# インデックスペアが違反集合内に存在する場合, その色を更新
if i in violate:
color = (0, 0, 255)
# (1)人物の周りに外接箱を描き、(2)人物のセントロイド座標を描く
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
cv2.circle(frame, (cX, cY), 5, color, 1)
# 出力フレーム上に社会的距離の違反の総数を描画
text = "Social Distancing Violations: {}".format(len(violate))
cv2.putText(frame, text, (10, frame.shape[0] - 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.85, (0, 0, 255), 3)
return frame
else:
vs = cv2.VideoCapture(0)
writer = None
image_placeholder = st.empty()
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
frame = imutils.resize(frame, width=700)
results = detect_people(frame,
net,
ln,
personIdx=LABELS.index("person"))
violate = set()
if len(results) >= 2:
centroids = np.array([r[2] for r in results])
D = dist.cdist(centroids, centroids, metric="euclidean")
for i in range(0, D.shape[0]):
for j in range(i + 1, D.shape[1]):
if D[i, j] < MIN_DISTANCE:
violate.add(i)
last_layer = model.getLayerNames()
last_layer = [last_layer[i[0] - 1] for i in model.getUnconnectedOutLayers()]
print("INPUT LOADED")
cap = cv2.VideoCapture(args["input"] if args["input"] != "" else 0)
writer = None
while True:
(access, frame) = cap.read()
if not access:
break
count = 0
frame = imutils.resize(frame, width=900)
results = detect_people(frame, model, last_layer, Labels)
a = set()
if len(results) >= 2:
centroids = np.array([r[3] for r in results])
for i in range(0, len(centroids)):
for j in range(i + 1, len(centroids)):
D = np.linalg.norm(centroids[i] - centroids[j])
if D < 125:
a.add(i)
a.add(j)
for (i, (prob, bounding_box, classes, centroid)) in enumerate(results):
if i in a:
X_start, Y_start, X_end, Y_end = bounding_box
cv2.rectangle(frame, (X_start, Y_start), (X_end, Y_end),
(0, 0, 255), 1)
def main(filename=""):
labelsPath = os.path.sep.join(
[os.getcwd().replace("\\", "/") + "/yolo-coco/coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")
weightsPath = os.path.sep.join(
[os.getcwd().replace("\\", "/") + "/yolo-coco/yolov3.weights"])
configPath = os.path.sep.join(
[os.getcwd().replace("\\", "/") + "/yolo-coco/yolov3.cfg"])
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
if config.USE_GPU:
print("[INFO] setting preferable backend and target to CUDA...")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
print("[INFO] accessing video stream...")
vs = cv2.VideoCapture(filename.replace("\\", "/") if filename != "" else 0)
writer = None
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
frame = imutils.resize(frame, width=700)
results = detect_people(frame,
net,
ln,
personIdx=LABELS.index("person"))
violate = set()
if len(results) >= 2:
centroids = np.array([r[2] for r in results])
D = dist.cdist(centroids, centroids, metric="euclidean")
for i in range(0, D.shape[0]):
for j in range(i + 1, D.shape[1]):
if D[i, j] < config.MIN_DISTANCE:
violate.add(i)
violate.add(j)
for (i, (prob, bbox, centroid)) in enumerate(results):
(startX, startY, endX, endY) = bbox
(cX, cY) = centroid
color = (0, 255, 0)
if i in violate:
color = (0, 0, 255)
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
cv2.circle(frame, (cX, cY), 5, color, 1)
text = "Social Distancing Violations: {}".format(len(violate))
cv2.putText(frame, text, (10, frame.shape[0] - 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.85, (0, 0, 255), 3)
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
