def verification(self): ap = argparse.ArgumentParser() ap.add_argument("-i","--image", required=True, help="path to input image") args=vars(ap.parse_args()) print("[INFO] starting video stream ...") vs = VideoSteam(usePiCamera=True).start() time.sleep(60.0) found =set() while True: frame=vs.read() frame=imutils.resize(frame,width=400) barcodes=pyzbar.decode(frame) for barcode in barcodes: (x,y,w,h)= barcode.rect cv2.retangle(image,(x,y),(x+w,y+h),(0,0,255),2) barcodeData = barcode.data.decode("utf-8") barcodeType = barcode.type text= "{} ({})".format(barcodeData, barcodeType) cv2.putText(image,text,(x,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255),2) if barcodeData not in found: return None else: found.intersection(barcodeData)
def face_detect(path, file_name): img = cv2.imread(path) cascade = cv2.CascadeClassifier('/root/Semi/kyberi/body10/haarcascade_upperbody.xml') rects = cascade.detectMultiScale(img, 1.3, 4, cv2.cv.CV_HAAR_SCALE_IMAGE, (20,20)) if len(rects) == 0: return False rects[:, 2:] += rects[:, :2] for x1, y1, x2, y2 in rects: cv2.retangle(img, (x1, y1), (x2, y2), (127, 255,0), 2) cv2.imwrite('%s/%s-%s' % (FACES_DIR, PCAP, file_name), img) return True
def detect(grey, orig_img): faces = face_cascade.detectMultiScale(grey, 1.1, 5) for (x, y, w, h) in faces: cv2.retangle(orig_img, (x, y), (x + w, y + h), (255, 0, 0), 2) roi_grey = grey[y:y + h, x:x + w] roi_color = orig_img[y:y + h, x:x + w] smiles = smile_cascade.detectMultiScale(roi_grey, 1.7, 22) for (sx, sy, sw, sh) in smiles: cv2.rectangle(roi_color, (sx, sy), (sx + sw, sy + sh), (0, 255, 0)) eye = eye_cascade.detectMultiscale(roi_grey, 1.1, 22) for (ex, ey, ew, eh) in eye: cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2) return orig_img
def face_detect(path, file_name): img = cv2.imread(path) # apply a classifier that is trained in advanced for detecting faces # in a front-facing orientation cascade = cv2.CascadeClassifier('/home/bytegirl/Desktop/haarcascade_upperbody.xml') # returns retangle coordinates that correspnd to where the face # was detected in the image. rects = cascade.detectMultiScale(img, 1.3, 4, cv2.cv.CV_HAAR_SCALE_IMAGE, (20,20)) if len(rects) == 0: return False rects[:, 2:] += rects[:, :2] # highlight the faces in the image # draw a green retangle over the area for x1, y1, x2, y2 in rects: cv2.retangle(img, (x1, y1), (x2, y2), (127, 255,0), 2) # write out the resulting image cv2.imwrite('%s/%s-%s' % (FACES_DIR, PCAP, file_name), img) return True
def face_detect(path, file_name): img = cv2.imread(path) cascade = cv2.CasecadeClassifier("haarcascade_frontalface_alt.xml") rects = cascade.detectMultiScale(img, 1.3, 4, cv2.cv.CV_HAAR_SCALE_IMAGE, (20, 20)) if len(rects) == 0: return False rects[:, 2:] += rects[:, :2] # highlight the faces in the image for x1, y1, x2, y2 in rects: cv2.retangle(img, (x1, y1), (x2, y2), (127, 255, 0), 2) cv2.imwrite("%s/%s-%s" % (faces_directory, pcap_file, file_name), img) return True
def face_detect(path, file_name): img = cv2.imread(path) # apply a classifier that is trained in advanced for detecting faces # in a front-facing orientation cascade = cv2.CascadeClassifier( '/home/bytegirl/Desktop/haarcascade_upperbody.xml') # returns retangle coordinates that correspnd to where the face # was detected in the image. rects = cascade.detectMultiScale(img, 1.3, 4, cv2.cv.CV_HAAR_SCALE_IMAGE, (20, 20)) if len(rects) == 0: return False rects[:, 2:] += rects[:, :2] # highlight the faces in the image # draw a green retangle over the area for x1, y1, x2, y2 in rects: cv2.retangle(img, (x1, y1), (x2, y2), (127, 255, 0), 2) # write out the resulting image cv2.imwrite('%s/%s-%s' % (FACES_DIR, PCAP, file_name), img) return True
def face_mask_image(): global RGB_image print('[Info] Loading Face Detector Model...') proto_Path = os.path.sep.join(['face_detector', 'deploy.prototxt']) weights_Path = os.path.sep.join( ['face_detection', 'res10_300x300_ssd_iter_140000.caffemodel']) net = cv2.dnn.readNet(proto_Path, weights_Path) print('[Info] loading face mask detector model........') # load the face mask detector model model = load_model('face_mask_detector.model') # load image input and grab the image spatial / dimensions image = cv2.imread('./images/out.jpg') (h, w) = image.shape[:2] # construct a blob from the image blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0)) # pass the blob through the network and get the face user face_detection print('[Info] computing Face Detection......please wait') net.setInput(blob) user_detections = net.forward() # looping over user_detections for i in range(0, user_detections.shape[2]): # probability associated with user_detections confirm = user_detections[0, 0, i, 2] # filter weak user_detections by ensuring confirm is greater then min confirm if confirm > 0.5: # compute the x, y coordinates coor_box = user_detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = coor_box.astype('int') # ensure the coor_box are with in the dimentions of the frame. (startX, startY) = (max(0, startX), max(0, startY)) (endX, endY) = (min(w - 1, endX), min(h - 1, endY)) # extract the face ROI, convert , convert iy from BGR to Rgb channel # and resize it to 224 x224, and preprocess it. face_change = image[startY:endY, startX:endX] face_change = cv2.cvtColor(face_change, cv2.COLOR_BGB2RGB) face_change = cv2.resize(face_change, (224, 224)) face_change = img_to_array(face_change) face_change = preprocess_input(face_change) face_change = np.expand_dims(face_change, axis=0) # pass the face_change through the model to determine if user has mask or not (with_mask, with_out_mask) = model.predict(face_change)[0] #determin the class label and color used to draw coor_box retangle output frame label = "Mask" if with_mask > with_out_mask else "No Mask" color = (0, 255, 0) if label == "Mask" else (0, 0, 255) # Probability in label label = "{}: {:.2f}%".format(label, max(with_mask, with_out_mask) * 100) # label retangle output frame cv2.putText(image, label, (startX, startY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2) cv2.retangle(image, (startX, startY), (endX, endY), color, 2) RGB_image = cv2.cvrColor(image, cv2.COLOR_B2B2RGB)
#!/usr/bin/env python # coding: utf8 # author: youdi import numpy as np import cv2 img = np.zeros(shape=(512, 512, 3), dtype=np.uint8) cv2.retangle(img, (384, 0), (510, 128), (0, 255, 0), 3)