Merge branch 'master' of https://github.com/Py-Contributors/OpenCv-tutorial

Author: codeperfectplus

.github/workflows/label.yml

@@ -14,6 +14,6 @@ jobs:
  runs-on: ubuntu-latest
 
  steps:
- - uses: actions/labeler@v2
+ - uses: actions/labeler@v3
  with:
  repo-token: "${{ secrets.GITHUB_TOKEN }}"

10.Invisible_Cloak/invisiblecloak.py

@@ -0,0 +1,80 @@
+ 
+import cv2
+import time
+import numpy as np
+
+## Preparation for writing the ouput video
+fourcc = cv2.VideoWriter_fourcc(*'XVID')
+out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
+
+##reading from the webcam
+cap = cv2.VideoCapture(0)
+
+## Allow the system to sleep for 3 seconds before the webcam starts
+time.sleep(3)
+count = 0
+background = 0
+
+## Capture the background in range of 60
+for i in range(60):
+ ret, background = cap.read()
+background = np.flip(background, axis=1)
+
+## Read every frame from the webcam, until the camera is open
+while (cap.isOpened()):
+ ret, img = cap.read()
+ if not ret:
+ break
+ count += 1
+ img = np.flip(img, axis=1)
+
+ ## Convert the color space from BGR to HSV
+ hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+ ## Generat masks to detect red color
+ lower_red = np.array([0, 120, 50])
+ upper_red = np.array([10, 255,255])
+ mask1 = cv2.inRange(hsv, lower_red, upper_red)
+
+ lower_red = np.array([170, 120, 70])
+ upper_red = np.array([180, 255, 255])
+ mask2 = cv2.inRange(hsv, lower_red, upper_red)
+
+ mask1 = mask1 + mask2
+
+ ## Open and Dilate the mask image
+ mask1 = cv2.morphologyEx(mask1, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8))
+ mask1 = cv2.morphologyEx(mask1, cv2.MORPH_DILATE, np.ones((3, 3), np.uint8))
+
+ ## Create an inverted mask to segment out the red color from the frame
+ mask2 = cv2.bitwise_not(mask1)
+
+ ## Segment the red color part out of the frame using bitwise and with the inverted mask
+ res1 = cv2.bitwise_and(img, img, mask=mask2)
+
+ ## Create image showing static background frame pixels only for the masked region
+ res2 = cv2.bitwise_and(background, background, mask=mask1)
+
+ ## Generating the final output and writing
+ finalOutput = cv2.addWeighted(res1, 1, res2, 1, 0)
+ out.write(finalOutput)
+ cv2.imshow("magic", finalOutput)
+ cv2.waitKey(1)
+
+
+cap.release()
+out.release()
+cv2.destroyAllWindows()
+
+#------------------------
+#colors code
+
+#skin color
+#lower_red = np.array([0, 0, 70])
+#upper_red = np.array([100, 255,255])
+# mask1 = cv2.inRange(hsv, lower_red, upper_red)
+
+# lower_red = np.array([170, 120, 70])
+# upper_red = np.array([180, 255, 255])
+
+#-----------------------

7. Face Detection/face detection.py

@@ -0,0 +1,51 @@
+import cv2
+
+# Path
+face_path = '7.FaceDetection/haarcascade_frontalface_default.xml'
+eye_path = '7.FaceDetection/haarcascade_eye.xml'
+face_cascade = cv2.CascadeClassifier(face_path)
+eye_cascade = cv2.CascadeClassifier(eye_path)
+
+
+def detectedFace(img):
+ img = cv2.imread(img)
+
+ gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+ faces = face_cascade.detectMultiScale(gray, 1.4, 5)
+
+ for face in faces:
+ x, y, width, height = face
+ # draw a rectangle for detection
+ cv2.rectangle(
+ img,
+ (x, y),
+ (x + width, y + height),
+ (0, 0, 255),
+ 1,
+ )
+ roi_gray = gray[y:y+height, x:x+width]
+ roi_color = img[y:y+height, x:x+width]
+
+ eyes = eye_cascade.detectMultiScale(roi_gray)
+ for (ex, ey, ew, eh) in eyes:
+ cv2.rectangle(
+ roi_color,
+ (ex, ey),
+ (ex+ew, ey+eh),
+ (0, 255, 0),
+ 2,
+ )
+
+ cv2.imshow('Face Detection', img)
+ k = cv2.waitKey(0) & 0xFF
+
+ if k == 27:
+ cv2.destroyAllWindows()
+ elif k == ord('s'):
+ cv2.imwrite('Media/face-detected.jpeg', img)
+ cv2.destroyAllWindows()
+
+
+path_img = './Media/face-001.jpg'
+face = detectedFace(path_img)