File size: 3,598 Bytes
be9fb11 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
import cv2
import numpy as np
# need to change these offsets later
X1_OFFSET, X2_OFFSET = 0,0
Y1_OFFSET, Y2_OFFSET = 0,0
class YOLO:
def __init__(self):
self.net = cv2.dnn.readNet("models/yolov3-tiny.weights", "configs/yolov3-tiny.cfg")
self.layer_names = self.net.getLayerNames()
self.output_layers = [self.layer_names[i - 1] for i in self.net.getUnconnectedOutLayers()]
self.classes = []
with open("coco.names", "r") as f:
self.classes = [line.strip() for line in f.readlines()]
def get_patches(self, img):
patches = []
for (x1, y1), (x2, y2), color, confidence, label in self.forward(img):
if (x1 == x2 or y1 == y2):
continue
print((x1, y1), (x2, y2))
patches.append(img[y1:y2, x1:x2])
return patches
def forward(self, img):
height, width, channels = img.shape
# Prepare the image for YOLO
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
self.net.setInput(blob)
# Run the forward pass
outs = self.net.forward(self.output_layers)
# Processing the output
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:] # center x, center y, width, height, object confidence score, class confidence scores...
class_id = np.argmax(scores)
class_confidence = scores[class_id]
object_confidence = detection[4]
if object_confidence > 0.5:
# Get the coordinates for the bounding box
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
# Rectangle coordinates
x = int(center_x - w / 2)
y = int(center_y - h / 2)
if x < 0 or y < 0:
continue
boxes.append([x, y, w, h])
confidences.append(float(class_confidence))
class_ids.append(class_id)
# Apply non-max suppression to remove overlapping boxes
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(self.classes[class_ids[i]])
confidence = confidences[i]
color = (0, 255, 0) # Green box
if label == "person":
yield (x + X1_OFFSET, y + Y1_OFFSET), (x + w + X2_OFFSET, y + h + Y2_OFFSET), color, confidence, label
__call__ = get_patches
def display(yolo_model:YOLO):
cam = cv2.VideoCapture(0)
while True:
ret, img = cam.read()
if not ret:
print("unable to record")
continue
for (x1, y1), (x2, y2), color, confidence, label in yolo_model.forward(img):
print(x1, y1, x2, y2)
cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
cv2.putText(img, f"{label} {confidence:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
cv2.imshow("Camera Feed", img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Show the image
cam.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
yolo_model = YOLO()
display(yolo_model=yolo_model) |