CARIA.2.0

Precedent repo CARIA :
Ajout de nouveau modele.
Travail sur multiple ia.
...

server-ia/Identification_Yolov3/yolo_opencv_video.py

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+import cv2
+import argparse
+import numpy as np
+
+ap = argparse.ArgumentParser()
+ap.add_argument('-v', '--video', required=True,
+                help='path to input video file or "cam" for webcam')
+ap.add_argument('-c', '--config', required=True,
+                help='path to yolo config file')
+ap.add_argument('-w', '--weights', required=True,
+                help='path to yolo pre-trained weights')
+ap.add_argument('-cl', '--classes', required=True,
+                help='path to text file containing class names')
+args = ap.parse_args()
+
+def get_output_layers(net):
+    layer_names = net.getLayerNames()
+    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
+    return output_layers
+
+def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
+    label = str(classes[class_id])
+    color = COLORS[class_id]
+    cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
+    cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+classes = None
+with open(args.classes, 'r') as f:
+    classes = [line.strip() for line in f.readlines()]
+
+COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
+
+net = cv2.dnn.readNet(args.weights, args.config)
+
+# If webcam is chosen, use camera capture
+if args.video == 'cam':
+    cap = cv2.VideoCapture(0)
+else:
+    cap = cv2.VideoCapture(args.video)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    Width = frame.shape[1]
+    Height = frame.shape[0]
+    scale = 0.00392
+
+    blob = cv2.dnn.blobFromImage(frame, scale, (416, 416), (0, 0, 0), True, crop=False)
+    net.setInput(blob)
+    outs = net.forward(get_output_layers(net))
+
+    class_ids = []
+    confidences = []
+    boxes = []
+    conf_threshold = 0.5
+    nms_threshold = 0.4
+
+    for out in outs:
+        for detection in out:
+            scores = detection[5:]
+            class_id = np.argmax(scores)
+            confidence = scores[class_id]
+            if confidence > 0.5:
+                center_x = int(detection[0] * Width)
+                center_y = int(detection[1] * Height)
+                w = int(detection[2] * Width)
+                h = int(detection[3] * Height)
+                x = center_x - w / 2
+                y = center_y - h / 2
+                class_ids.append(class_id)
+                confidences.append(float(confidence))
+                boxes.append([x, y, w, h])
+
+    indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
+
+    for i in indices:
+        i = i[0]
+        box = boxes[i]
+        x = box[0]
+        y = box[1]
+        w = box[2]
+        h = box[3]
+        draw_prediction(frame, class_ids[i], confidences[i], round(x), round(y), round(x + w), round(y + h))
+
+    cv2.imshow("object detection", frame)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()