معالجة الصور والتحكم في الطائرات بدون طيار - كتاب الذكاء الإصطناعي - الصف 12 - الفصل 1 - المملكة العربية السعودية

# reshapes image array to (channels, height, width) format

cameraImg = np.transpose(cameraImg, (2, 0, 1)) cameraImg = np.reshape(cameraImg, (3, 240, 400))

# creates RGB image from merged channels

img = Image.new('RGB', (400, 240)) img = cv2.merge((cameraImg[2], cameraImg[1], cameraImg[0]))

# converts image to grayscale

gray = cv2.cvtColor(np.uint8(img), cv2.COLOR_BGR2GRAY)

# loads and applies the Haar cascade classifier to detect humans in image

human_cascade = cv2.CascadeClassifier('haarcascade_fullbody.xml') humans = human_cascade.detectMultiScale(gray)

# loop, through detected human images, annotates them with a bounding box # and prints a timestamp and an info message on the console

for (x, y, w, h) in humans: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2) current_time = datetime.now() print(current_time) print("Found a person in coordinates [{:.2f}, {:.2f}]" .format(x_pos, y_pos))

# saves annotated image to file with timestamp

current_time = current_time.strftime("%Y-%m-%d_%H-%M-%S") filename = f"detected/IMAGE_{current_time}.png" cv2.imwrite(filename, img)

t2 = self.GetTime()

# calculates the desired input values for roll, pitch, yaw, # and altitude using various constants and disturbance values

roll_input = self.K_ROLL_P * clamp(roll, -1, 1) + roll_acceleration + roll_disturbance pitch_input = self.K_PITCH_P * clamp(pitch, -1, 1) + pitch_acceleration + pitch_disturbance yaw_input = yaw_disturbance clamped_difference_altitude = clamp(self.target_altitude - altitude + self.K_VERTICAL_OFFSET, -1, 1) vertical_input = self.K_VERTICAL_P * pow(clamped_difference_altitude, 3.0)

# calculates the motors' input values based on the desired roll, pitch, yaw, and altitude values

front_left_motor_input = self.K_VERTICAL_THRUST + vertical_input - yaw_input + pitch_input - roll_input front_right_motor_input = self.K_VERTICAL_THRUST + vertical_input + yaw_input + pitch_input + roll_input rear_left_motor_input = self.K_VERTICAL_THRUST + vertical_input + yaw_input - pitch_input - roll_input rear_right_motor_input = self.K_VERTICAL_THRUST + vertical_input - yaw_input - pitch_input + roll_input

# sets the velocity of each motor based on the motors' input values calculated above

self.front_left_motor.setVelocity(front_left_motor_input) self.front_right_motor.setVelocity(-front_right_motor_input) self.rear_left_motor.setVelocity(-rear_left_motor_input) self.rear_right_motor.setVelocity(rear_right_motor_input)

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وزارة التعليم Ministry of Education 2023 - 1447

# reshapes image array to (channels, height, width) format cameraImg = np.transpose(cameraImg, (2, 0, 1)) cameraImg = np.reshape(cameraImg, (3, 240, 400)) # creates RGB image from merged channels img = Image.new('RGB', (400, 240)) img = cv2.merge((cameraImg[2], cameraImg[1], cameraImg[0])) # converts image to grayscale gray = cv2.cvtColor(np.uint8(img), cv2.COLOR_BGR2GRAY) # loads and applies the Haar cascade classifier to detect humans in image human_cascade = cv2.CascadeClassifier('haarcascade_fullbody.xml') humans = human_cascade.detectMultiScale(gray) # loop, through detected human images, annotates them with a bounding box # and prints a timestamp and an info message on the console for (x, y, w, h) in humans: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2) current_time = datetime.now() print(current_time) print("Found a person in coordinates [{:.2f}, {:.2f}]" .format(x_pos, y_pos)) # saves annotated image to file with timestamp current_time = current_time.strftime("%Y-%m-%d_%H-%M-%S") filename = f"detected/IMAGE_{current_time}.png" cv2.imwrite(filename, img) t2 = self.GetTime() # calculates the desired input values for roll, pitch, yaw, # and altitude using various constants and disturbance values roll_input = self.K_ROLL_P * clamp(roll, -1, 1) + roll_acceleration + roll_disturbance pitch_input = self.K_PITCH_P * clamp(pitch, -1, 1) + pitch_acceleration + pitch_disturbance yaw_input = yaw_disturbance clamped_difference_altitude = clamp(self.target_altitude - altitude + self.K_VERTICAL_OFFSET, -1, 1) vertical_input = self.K_VERTICAL_P * pow(clamped_difference_altitude, 3.0) # calculates the motors' input values based on the desired roll, pitch, yaw, and altitude values front_left_motor_input = self.K_VERTICAL_THRUST + vertical_input - yaw_input + pitch_input - roll_input front_right_motor_input = self.K_VERTICAL_THRUST + vertical_input + yaw_input + pitch_input + roll_input rear_left_motor_input = self.K_VERTICAL_THRUST + vertical_input + yaw_input - pitch_input - roll_input rear_right_motor_input = self.K_VERTICAL_THRUST + vertical_input - yaw_input - pitch_input + roll_input # sets the velocity of each motor based on the motors' input values calculated above self.front_left_motor.setVelocity(front_left_motor_input) self.front_right_motor.setVelocity(-front_right_motor_input) self.rear_left_motor.setVelocity(-rear_left_motor_input) self.rear_right_motor.setVelocity(rear_right_motor_input) 334 وزارة التعليم Ministry of Education 2023 - 1447 --- VISUAL CONTEXT --- **IMAGE**: Ministry of Education Logo Description: A stylized logo consisting of green dots forming a pattern, with Arabic and English text below it. The text reads 'وزارة التعليم' and 'Ministry of Education 2023 - 1447'. Context: Identifies the publishing authority or source of the educational material.