Azadov Nadirbek 16 praktika

#Yolo.py

import cv2
import numpy as np

# Load YOLO model
yolo_config = 'yolov3.cfg'
yolo_weights = 'yolov3.weights'
labels_path = 'coco.names'

# Load class labels
with open(labels_path, 'r') as f:
    classes = [line.strip() for line in f.readlines()]

# Initialize YOLO model
net = cv2.dnn.readNet(yolo_weights, yolo_config)

# Set backend to GPU if available, otherwise use CPU
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

# Load input image
image_path = 'image.png'
image = cv2.imread(image_path)
if image is None:
    print("Error: Could not read image file. Check the file path.")
    exit()

(h, w) = image.shape[:2]

# Prepare input blob
blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)

# Get YOLO output layers
layer_names = net.getUnconnectedOutLayersNames()
layer_outputs = net.forward(layer_names)

# Initialize lists for detections
boxes, confidences, class_ids = [], [], []
for output in layer_outputs:
    for detection in output:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.7:  # Modify threshold here (e.g., d=0.7)
            box = detection[0:4] * np.array([w, h, w, h])
            (center_x, center_y, width, height) = box.astype('int')
            x = int(center_x - width / 2)
            y = int(center_y - height / 2)
            boxes.append([x, y, int(width), int(height)])
            confidences.append(float(confidence))
            class_ids.append(class_id)

# Perform non-max suppression
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# Draw bounding boxes and labels
for i in indices.flatten():
    x, y, w, h = boxes[i]
    label = f"{classes[class_ids[i]]}: {confidences[i]:.2f}"
    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
    cv2.putText(image, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# Display results
cv2.imshow("YOLO Object Detection", image)
cv2.waitKey(0)
cv2.destroyAllWindows()



#ssd.py

import cv2
import numpy as np

# Load SSD model
ssd_prototxt = 'MobileNetSSD_deploy.prototxt'
ssd_model = 'MobileNetSSD_deploy.caffemodel'
labels_path = 'coco.names'

# Load class labels
with open(labels_path, 'r') as f:
    classes = [line.strip() for line in f.readlines()]

# Initialize SSD model
net = cv2.dnn.readNetFromCaffe(ssd_prototxt, ssd_model)

# Load input image
image_path = 'image4.png'
image = cv2.imread(image_path)
if image is None:
    print("Error: Could not read image file. Check the file path.")
    exit()

(h, w) = image.shape[:2]

# Prepare input blob
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)
net.setInput(blob)

# Forward pass to get detections
detections = net.forward()

# Post-process detections
for i in range(detections.shape[2]):
    confidence = detections[0, 0, i, 2]
    if confidence > 0.7:  # Modify threshold here (e.g., d=0.7)
        idx = int(detections[0, 0, i, 1])
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype('int')
        label = f"{classes[idx]}: {confidence:.2f}"
        cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)
        cv2.putText(image, label, (startX, startY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# Display results
cv2.imshow("SSD Object Detection", image)
cv2.waitKey(0)
cv2.destroyAllWindows()